Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
Curtis Hill
Topic 07 Assignment: Long-Term Care Chart
HA3010 - Introduction to US Healthcare Delivery
Jenifer Henke
May 24, 2020
HA3010 Topic 7 Assignment
Long Term Care Chart
Complete the chart comparing and contrasting long-term care services.
Type of LTC Service
Cost Effectiveness
Efficacy
Patient Satisfaction
Home care
Home care services are cost effective since the costs are flexible depending on one’s ability to pay.
Efficient in helping individuals with daily activities. It also helps patients with healthcare needs.
Relatively high
Community services
This is also considered cost effective since it can be provided by health care programs, social or other related providers.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Supportive housing programs
Their cost ranges from low to medium, hence making them cost effective. This is especially the case when such is offered by the government.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Continuing care retirement communities
The cost of CCRC is high as compared to the types discussed above. This is because it offers a full range of services.
Efficient for both healthcare and daily activities requirements.
High
Nursing homes
The cost of this type of long term care service is high. This is because the cost includes skilled services such as nursing and rehabilitation, meals and other support activities.
Efficient for both healthcare and daily activities requirements.
High
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product .
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxwlynn1
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxjeanettehully
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a ...
Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
These are topics we have worked in residency week in group projectchestnutkaitlyn
These are topics we have worked in residency week in group project and individual assignment.
Residency Group Project/Assignment.
We have worked on in group on research paper and prepared power point.
Our group select the project is:
Research Topic 1: Data Visualization.
Research: Data Visualization
Background: As noted by Sharda et al (2020), Data Visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included chats and graphs as well as other types of visual elements used to create scorecards and dashboards.
Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
ISBN-13: 978-0-13-519201-6
Research Question: What is data visualization? Why is it needed? Finally, write briefly on the historical roots of data visualization.
Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
This is the Individual assignment :
Research Topic 6: Executive Program Practical Connection Assignment
At UC, it is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where students will demonstrate how this course research has connected and put into practice within their own careers.
Assignment:
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.
Requirements:
Provide a 500 word (or 2 pages double spaced) minimum reflection.
Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.
Share a personal connection that identifies specific knowledge and theories from this course.
Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.
You should NOT provide an overview of the assignments assigned in the course. The assignment asks that you reflect on how the knowledge and skills obtained through meeting course objectives were applied or ...
These are topics we have worked in residency week in group project.docxrandymartin91030
These are topics we have worked in residency week in group project and individual assignment.
Residency Group Project/Assignment.
We have worked on in group on research paper and prepared power point.
Our group select the project is:
Research Topic 1: Data Visualization.
Research: Data Visualization
Background: As noted by Sharda et al (2020), Data Visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included chats and graphs as well as other types of visual elements used to create scorecards and dashboards.
Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
ISBN-13: 978-0-13-519201-6
Research Question: What is data visualization? Why is it needed? Finally, write briefly on the historical roots of data visualization.
Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
This is the Individual assignment :
Research Topic 6: Executive Program Practical Connection Assignment
At UC, it is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where students will demonstrate how this course research has connected and put into practice within their own careers.
Assignment:
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.
Requirements:
Provide a 500 word (or 2 pages double spaced) minimum reflection.
Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.
Share a personal connection that identifies specific knowledge and theories from this course.
Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.
You should NOT provide an overview of the assignments assigned in the course. The assignment asks that you reflect on how the knowledge and skills obtained through meeting course objectives were applied or.
Chapter 10 Modeling and Analysis Heuristic Search Methods EstelaJeffery653
Chapter 10: Modeling and Analysis: Heuristic Search
Methods and Simulation
Learning Objectives
• Explain the basic concepts of simulation and when to
use it
• Understand the concepts and applications of different
types of simulation
• Explain what is meant by Monte Carlo and discrete
event simulation
Simulation
• Simulation is the “appearance” of reality
• It is often used to conduct what-if analysis on the
model of the actual system
• It is a popular DSS technique for conducting
experiments with a computer on a comprehensive
model of the system to assess its dynamic behavior
• Often used when the system is too complex for other
DSS techniques
Application Case 10.3
Simulating Effects of Hepatitis B
Interventions
Questions for Discussion
1. Explain the advantage of operations research methods such
as simulation over clinical trial methods in determining the
best control measure for Hepatitis B.
2. In what ways do the decision and Markov models provide
cost-effective ways of combating the disease?
3. Discuss how multidisciplinary background is an asset in
finding a solution for the problem described in the case.
4. Besides healthcare, in what other domain could such a
modeling approach help reduce cost?
Major Characteristics of Simulation
• Imitates reality and captures its richness both in
shape and behavior
• “Represent” versus “Imitate”
• Technique for conducting experiments
• Descriptive, not normative tool
• Often to “solve” [i.e., analyze] very complex
systems/problems
• Simulation should be used only when a numerical
optimization is not possible
Advantages of Simulation
• The theory is fairly straightforward
• Great deal of time compression
• Experiment with different alternatives
• The model reflects manager’s perspective
• Can handle wide variety of problem types
• Can include the real complexities of problems
• Produces important performance measures
• Often it is the only DSS modeling tool for non-structured problems
Disadvantages of Simulation
• Cannot guarantee an optimal solution
• Slow and costly construction process
• Cannot transfer solutions and inferences to solve other problems
(problem specific)
• So easy to explain/sell to managers, may lead to overlooking
analytical solutions
• Software may require special skills
Simulation Methodology
Steps:
1. Define problem 5. Conduct experiments
2. Construct the model 6. Evaluate results
3. Test and validate model 7. Implement solution
4. Design experiments
Simulation Types
• Probabilistic/Stochastic vs. Deterministic Simulation
• Uses probability distributions
• Time-dependent vs. Time-independent Simulation
• Monte Carlo technique (X = A + B)[A, B, and X are all
distributions]
• Discrete Event vs. Continuous Simulation
• Simulation Implementation
• Visual Simulation and/or Object-Oriented Simulation
Visual Interactive Simulation (VIS)
• Visual interactive modeling (VIM), also called Visual
Inte ...
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Deadline 6 PM Friday September 27, 201310 Project Management Que.docxedwardmarivel
Deadline 6 PM Friday September 27, 2013
10 Project Management Questions with sub-questions under each question. A word document is provided with all questions and directions.
Problem 1
The following data were obtained from a project to create a new portable electronic.
Activity
Duration
Predecessors
A
5 Days
---
B
6 Days
---
C
8 Days
---
D
4 Days
A, B
E
3 Days
C
F
5 Days
D
G
5 Days
E, F
H
9 Days
D
I
12 Days
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
What is the Scheduled Completion of the Project?
b)
What is the Critical Path of the Project?
c)
What is the ES for Activity D?
d)
What is the LS for Activity G?
e)
What is the EF for Activity B?
f)
What is the LF for Activity H?
g)
What is the float for Activity I?
Problem 2
The following data were obtained from a project to build a pressure vessel:
Activity
Duration
Predecessors
A
6 weeks
---
B
6 weeks
---
C
5 weeks
B
D
4 weeks
A, C
E
5 weeks
B
F
7 weeks
D, E, G
G
4 weeks
B
H
8 weeks
F
I
5 weeks
G
J
3 week
I
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 3
The following data were obtained from a project to design a new software package:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
6 Days
A
D
4 Days
C, B
E
5 Days
A
F
4 Days
D, E, G
G
4 Days
B, C
H
3 Day
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path(s)
c)
What is the slack time (float) for activity B?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 4
The following data were obtained from an in-house MIS project:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
5 Days
A
D
4 Days
B
E
5 Days
B
F
3 Day
C, D
G
7 Days
C, D
H
6 Days
E, F, G
I
9 Days
E, F
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e)
What is the slack time (float) for activity E?
f)
What is the slack time (float) for activity F?
PROBLEM 5
Use the network diagram below and the additional information provided to answer the corresponding questions.
a) Give the crash cost per day per activity.
b) Which activities should be crash.
More Related Content
Similar to DDBA 8307 Week 4 Assignment TemplateJohn DoeDDBA 8.docx
Curtis HillTopic 07 Assignment Long-Term Care ChartHA30.docxdorishigh
Curtis Hill
Topic 07 Assignment: Long-Term Care Chart
HA3010 - Introduction to US Healthcare Delivery
Jenifer Henke
May 24, 2020
HA3010 Topic 7 Assignment
Long Term Care Chart
Complete the chart comparing and contrasting long-term care services.
Type of LTC Service
Cost Effectiveness
Efficacy
Patient Satisfaction
Home care
Home care services are cost effective since the costs are flexible depending on one’s ability to pay.
Efficient in helping individuals with daily activities. It also helps patients with healthcare needs.
Relatively high
Community services
This is also considered cost effective since it can be provided by health care programs, social or other related providers.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Supportive housing programs
Their cost ranges from low to medium, hence making them cost effective. This is especially the case when such is offered by the government.
Efficient to patients requiring help in daily activities.
Not efficient for provision of healthcare needs.
Relatively low
Continuing care retirement communities
The cost of CCRC is high as compared to the types discussed above. This is because it offers a full range of services.
Efficient for both healthcare and daily activities requirements.
High
Nursing homes
The cost of this type of long term care service is high. This is because the cost includes skilled services such as nursing and rehabilitation, meals and other support activities.
Efficient for both healthcare and daily activities requirements.
High
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product .
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxwlynn1
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a.
Running head INFORMATION LITERACY 1INFORMATION LITERACY 2.docxjeanettehully
Running head: INFORMATION LITERACY 1
INFORMATION LITERACY 2
INFORMATION LITERACY
GEN 499: General Education Capstone
October 14, 2019.
Ashford University Library has good resources for any academic material one wants to read. I am a business student and when I joined Ashford University I was a little worried about what might happen if I could not find the necessary academic materials to support my education. Another issue I found overwhelming at first was how to navigate the library database because there were so many options. If you click on a particular option at times they are not relevant to the topic under research. A friend directed me on how to navigate in the Databases A-Z. Nowadays it is easier because I followed all the instructions to the later.
I like the ProQuest Database because it has so many options someone can choose from and the resources are very helpful, (Brannon, 2017). I do not have any concerns but don't like the fact that Ashford Library pulls up student's research papers as references that have to be changed. In these databases, one has to use the subject topic to find readings or scholarly articles, (Nelson & Huffman, 2015). Some databases may not have the articles one is looking for because they are all specified for certain course work, if you are new it can be very overwhelming. I also realized that if I download a full PDF then all the details about the authors and references will be readily available.
Ashford University Library has improved skills in my business course because before the exams approach I am always equipped with adequate information. This keeps me away from using search engines like Google and some of the resources may not be credible. The best part with the resources that come from Ashford Library is that they help one reduce the reference format mistakes because they are already located on the articles, (Omar, et.al, 2018). The newspapers and other articles that are on the internet can be very difficult to cite at times. In general, the Ashford University Library is effective and reliable because it has good resources and citations which are accurate.
References
Brannon, P. C. (2017). ProQuest Regulatory Insight. Law Library Journal, 109(3), 484.
Nelson, N., & Huffman, J. (2015). Predatory journals in library databases: How much should we worry?. The serials librarian, 69(2), 169-192.
Omar, D., Preater, A., Clark, I., & Liebert, R. J. (2018). Inclusive reading lists: how libraries can support student and academic leadership.
INFORMATION
GOVERNANCE
Founded in 1807, John Wiley & Sons is the oldest independent publishing company in
the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley
is globally committed to developing and marketing print and electronic products and
services for our customers’ professional and personal knowledge and understanding.
The Wiley CIO series provides information, tools, and insights to IT executives
a ...
Could you increase your knowledge—and raise your grade—i.docxfaithxdunce63732
Could you increase your knowledge—
and raise your grade—if you…
…used an online tutorial that assisted you with Access
and Excel skills mapped to this book?
…learned to use Microsoft’s SharePoint, the number one
organizational tool for file sharing and collaboration?
…had flashcards and student PowerPoints
to prepare for lectures?
Visit , a valuable tool
for your student success and your
business career.
www.myMISlab.com
www.myMISlab.com
INTEGRATING BUSINESS WITH TECHNOLOGY
By completing the projects in this text, students will be able to demonstrate business knowledge, application
software proficiency, and Internet skills.These projects can be used by instructors as learning assessment tools
and by students as demonstrations of business, software, and problem-solving skills to future employers. Here
are some of the skills and competencies students using this text will be able to demonstrate:
Business Application skills: Use of both business and software skills in real-world business applications.
Demonstrates both business knowledge and proficiency in spreadsheet, database, and Web page/blog creation
tools.
Internet skills: Ability to use Internet tools to access information, conduct research, or perform online
calculations and analysis.
Analytical, writing and presentation skills: Ability to research a specific topic, analyze a problem, think
creatively, suggest a solution, and prepare a clear written or oral presentation of the solution, working either
individually or with others in a group.
Business Application Skills
BUSINESS SKILLS
Finance and Accounting
Financial statement analysis
Pricing hardware anrj software
Technology rent vs. buy decision
Total Cost of Ownership (TCO) analysis
Analyzing telecommunications services anrj costs
Risk assessment
Retirement planning
Capital budgeting
Human Resources
Employee training and skills tracking
Job posting database and Web page
Manufacturing and Production
Analyzing supplier performance and pricing
Inventory management
Bill of materials cost sensitivity analysis
Sales and Marketing
Sales trend analysis
SOFTWARE SKILLS
Spreadsheet charts
Spreadsheet formulas
Spreadsheet downloading and formatting
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet formulas
Spreadsheet charts and formulas
Spreadsheet formulas and logical functions
Spreadsheet formulas
Database design
Database querying and reporting
Database design
Web page design and creation
Spreadsheet date functions
Database functions
Data filtering
Importing data into a database
Database querying and reporting
Spreadsheet data tables
Spreadsheet formulas
Database querying and reporting
CHAPTER
Chapter 2*
Chapter 10
Chapter 5
Chapter 5*
Chapter 7
Chapter 8
Chapter 11
Chapter 14
Chapter 14*
Chapter 13*
Chapter 15
Chapter 2
Chapter 6
Chapter 12*
Chapter 1
Customer reservation system
Improving marketing decisions
Customer profiling
Customer service analysis
Sales lead and.
These are topics we have worked in residency week in group projectchestnutkaitlyn
These are topics we have worked in residency week in group project and individual assignment.
Residency Group Project/Assignment.
We have worked on in group on research paper and prepared power point.
Our group select the project is:
Research Topic 1: Data Visualization.
Research: Data Visualization
Background: As noted by Sharda et al (2020), Data Visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included chats and graphs as well as other types of visual elements used to create scorecards and dashboards.
Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
ISBN-13: 978-0-13-519201-6
Research Question: What is data visualization? Why is it needed? Finally, write briefly on the historical roots of data visualization.
Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
This is the Individual assignment :
Research Topic 6: Executive Program Practical Connection Assignment
At UC, it is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where students will demonstrate how this course research has connected and put into practice within their own careers.
Assignment:
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.
Requirements:
Provide a 500 word (or 2 pages double spaced) minimum reflection.
Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.
Share a personal connection that identifies specific knowledge and theories from this course.
Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.
You should NOT provide an overview of the assignments assigned in the course. The assignment asks that you reflect on how the knowledge and skills obtained through meeting course objectives were applied or ...
These are topics we have worked in residency week in group project.docxrandymartin91030
These are topics we have worked in residency week in group project and individual assignment.
Residency Group Project/Assignment.
We have worked on in group on research paper and prepared power point.
Our group select the project is:
Research Topic 1: Data Visualization.
Research: Data Visualization
Background: As noted by Sharda et al (2020), Data Visualization is closely related to the fields of information graphics, information visualization, scientific visualization, and statistical graphics. Until recently, the major forms of data visualization available in both BI applications have included chats and graphs as well as other types of visual elements used to create scorecards and dashboards.
Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
ISBN-13: 978-0-13-519201-6
Research Question: What is data visualization? Why is it needed? Finally, write briefly on the historical roots of data visualization.
Your research paper should be at least 3 pages (800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Your cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date.
This is the Individual assignment :
Research Topic 6: Executive Program Practical Connection Assignment
At UC, it is a priority that students are provided with strong educational programs and courses that allow them to be servant-leaders in their disciplines and communities, linking research with practice and knowledge with ethical decision-making. This assignment is a written assignment where students will demonstrate how this course research has connected and put into practice within their own careers.
Assignment:
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.
Requirements:
Provide a 500 word (or 2 pages double spaced) minimum reflection.
Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.
Share a personal connection that identifies specific knowledge and theories from this course.
Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.
You should NOT provide an overview of the assignments assigned in the course. The assignment asks that you reflect on how the knowledge and skills obtained through meeting course objectives were applied or.
Chapter 10 Modeling and Analysis Heuristic Search Methods EstelaJeffery653
Chapter 10: Modeling and Analysis: Heuristic Search
Methods and Simulation
Learning Objectives
• Explain the basic concepts of simulation and when to
use it
• Understand the concepts and applications of different
types of simulation
• Explain what is meant by Monte Carlo and discrete
event simulation
Simulation
• Simulation is the “appearance” of reality
• It is often used to conduct what-if analysis on the
model of the actual system
• It is a popular DSS technique for conducting
experiments with a computer on a comprehensive
model of the system to assess its dynamic behavior
• Often used when the system is too complex for other
DSS techniques
Application Case 10.3
Simulating Effects of Hepatitis B
Interventions
Questions for Discussion
1. Explain the advantage of operations research methods such
as simulation over clinical trial methods in determining the
best control measure for Hepatitis B.
2. In what ways do the decision and Markov models provide
cost-effective ways of combating the disease?
3. Discuss how multidisciplinary background is an asset in
finding a solution for the problem described in the case.
4. Besides healthcare, in what other domain could such a
modeling approach help reduce cost?
Major Characteristics of Simulation
• Imitates reality and captures its richness both in
shape and behavior
• “Represent” versus “Imitate”
• Technique for conducting experiments
• Descriptive, not normative tool
• Often to “solve” [i.e., analyze] very complex
systems/problems
• Simulation should be used only when a numerical
optimization is not possible
Advantages of Simulation
• The theory is fairly straightforward
• Great deal of time compression
• Experiment with different alternatives
• The model reflects manager’s perspective
• Can handle wide variety of problem types
• Can include the real complexities of problems
• Produces important performance measures
• Often it is the only DSS modeling tool for non-structured problems
Disadvantages of Simulation
• Cannot guarantee an optimal solution
• Slow and costly construction process
• Cannot transfer solutions and inferences to solve other problems
(problem specific)
• So easy to explain/sell to managers, may lead to overlooking
analytical solutions
• Software may require special skills
Simulation Methodology
Steps:
1. Define problem 5. Conduct experiments
2. Construct the model 6. Evaluate results
3. Test and validate model 7. Implement solution
4. Design experiments
Simulation Types
• Probabilistic/Stochastic vs. Deterministic Simulation
• Uses probability distributions
• Time-dependent vs. Time-independent Simulation
• Monte Carlo technique (X = A + B)[A, B, and X are all
distributions]
• Discrete Event vs. Continuous Simulation
• Simulation Implementation
• Visual Simulation and/or Object-Oriented Simulation
Visual Interactive Simulation (VIS)
• Visual interactive modeling (VIM), also called Visual
Inte ...
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
Deadline 6 PM Friday September 27, 201310 Project Management Que.docxedwardmarivel
Deadline 6 PM Friday September 27, 2013
10 Project Management Questions with sub-questions under each question. A word document is provided with all questions and directions.
Problem 1
The following data were obtained from a project to create a new portable electronic.
Activity
Duration
Predecessors
A
5 Days
---
B
6 Days
---
C
8 Days
---
D
4 Days
A, B
E
3 Days
C
F
5 Days
D
G
5 Days
E, F
H
9 Days
D
I
12 Days
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
What is the Scheduled Completion of the Project?
b)
What is the Critical Path of the Project?
c)
What is the ES for Activity D?
d)
What is the LS for Activity G?
e)
What is the EF for Activity B?
f)
What is the LF for Activity H?
g)
What is the float for Activity I?
Problem 2
The following data were obtained from a project to build a pressure vessel:
Activity
Duration
Predecessors
A
6 weeks
---
B
6 weeks
---
C
5 weeks
B
D
4 weeks
A, C
E
5 weeks
B
F
7 weeks
D, E, G
G
4 weeks
B
H
8 weeks
F
I
5 weeks
G
J
3 week
I
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 3
The following data were obtained from a project to design a new software package:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
6 Days
A
D
4 Days
C, B
E
5 Days
A
F
4 Days
D, E, G
G
4 Days
B, C
H
3 Day
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path(s)
c)
What is the slack time (float) for activity B?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 4
The following data were obtained from an in-house MIS project:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
5 Days
A
D
4 Days
B
E
5 Days
B
F
3 Day
C, D
G
7 Days
C, D
H
6 Days
E, F, G
I
9 Days
E, F
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e)
What is the slack time (float) for activity E?
f)
What is the slack time (float) for activity F?
PROBLEM 5
Use the network diagram below and the additional information provided to answer the corresponding questions.
a) Give the crash cost per day per activity.
b) Which activities should be crash.
DEADLINE 15 HOURS
6 PAGES
UNDERGRADUATE
COURSEWORK
HARVARD FORMATING
DOUBLE SPACING
INSTRUCTIONS
This assignment seeks to assess your ability to:
• Critically evaluate and discuss the major developments during 2017 in corporate taxation from the perspective of multinational companies and their auditors, governments and other stakeholders.
• Apply appropriate knowledge, analytical techniques and concepts to problems and issues arising from both familiar and unfamiliar situations;
• Think critically, examine problems and issues from a number of perspectives, challenge viewpoints, ideas and concepts and make well-reasoned judgements;
• Present, discuss and defend ideas, concepts and views effectively through formal language.
Background:
In the final weeks of 2017 a leading tax expert suggested that “a whirlwind of international tax changes has swept the globe”. He also went on to say that for companies operating in Europe there is no end in sight to the pace of change. The final recommendations on base erosion and profit shifting (BEPS) from the OECD have been endorsed by the EU. In fact a number of European governments have already implemented large parts of these proposals ahead of schedule.
The third quarter of the year saw the European Commission in the spotlight with its landmark decision that the technology giant Apple must repay no less than €13 billion of taxes to the Irish government. This ruling was based on the view that the favourable tax treatment was effectively state aid and hence the Irish government had broken EU law. At the same time countries across the world continue to compete by reducing the rate of corporate taxes. Many commentators suggest that the UK government will cut the corporate tax rate to 10% if the country fails to negotiate a trade deal with the European Union as part of the Brexit process. In a separate development earlier in the year the government of Hungary announced it would become the tax haven of Central Europe with a plan to reduce corporation tax to a mere 9%.
Required:
You are to write a report for the Board of Directors of a listed global company that has manufacturing and R&D activities across Europe, Asia, Australasia and America. The report should assume that the directors have detailed knowledge of the group activities but are not taxation specialists. However they would be aware of issues relating to corporate governance, transparency and reputational risks.
The report should cover the following aspects:
Evaluate the major developments that occurred in corporate taxation in 2017 and the issues that may arise in the current year.
Discuss the implications for the group in regard to the relationship with its auditors.
Consider how other stakeholders and non-governmental organisations (NGOs) may be affected by changes in the level of corporate taxes and their possible reaction.
The resources below are on Blackboard and provide an introduction to the topic.
“Corpor.
De nada.El gusto es mío.Encantada.Me llamo Pepe.Muy bien, grac.docxedwardmarivel
De nada. El gusto es mío. Encantada. Me llamo Pepe.
Muy bien, gracias. Nada. Nos vemos. Soy de Argentina.
1. ¿Cómo te llamas?
2. ¿Qué hay de nuevo?
3. ¿De dónde eres?
4. Adiós.
5. ¿Cómo está usted?
6. Mucho gusto.
7. Te presento a la señora Díaz.
8. Muchas gracias.
Modelo ¡Hola! Buenos días.
Adiós cómo Chau de eres
es está gusto Hasta Le
mío Muy Soy usted vemos
1. ANA Buenos días, señor González. ¿Cómo (1) (2) ?
SR. GONZÁLEZ (3) bien, gracias. Y tú, ¿(4) estás?
ANA Regular. (5) presento a Antonio.
SR. GONZÁLEZ Mucho (6) , Antonio.
ANTONIO El gusto (7) (8) .
SR. GONZÁLEZ ¿De dónde (9) , Antonio?
ANTONIO (10) (11) México.
ANA (12) luego, señor González.
SR. GONZÁLEZ Nos (13) , Ana.
ANTONIO (14) , señor González.
• • Hasta mañana.
• Nos vemos.
• Buenos días.
• Hasta pronto.
• • ¿Qué tal?
• Regular.
• ¿Qué pasa?
• ¿Cómo estás?
• • Puerto Rico
• Washington
• México
• Estados Unidos
• • Muchas gracias.
• Muy bien, gracias.
• No muy bien.
• Regular.
• • ¿De dónde eres?
• ¿Cómo está usted?
• ¿De dónde es usted?
• ¿Cómo se llama usted?
• • Chau.
• Buenos días.
• Hola.
• ¿Qué tal?
Modelo un papel
unos papeles
1. : unas fotografías
2. : un día
3. : un cuaderno
4. : unos pasajeros
5. : una computadora
6. : unas escuelas
7. : unos videos
8. : un programa
9. : unos autobuses
10. : una palabra
Modelo el señor Díaz
Addresing him: usted
Talking about him: él
1. Don Francisco
Addressing him:
Talking about him:
2. Jimena y Marissa
Addressing them:
Talking about them:
3. Maru y Miguel
Addressing them:
Talking about them:
4. la profesora
Addressing her:
Talking about her:
5. un estudiante
Addressing him:
Talking about him:
6. el director de una escuela
Addressing him:
Talking about him:
7. tres chicas
Addressing them:
Talking about them:
8. un pasajero de autobús
Addressing him:
Talking about him:
9. Juan Carlos y Felipe
Addressing them:
Talking about them:
10. una turista
Addressing her:
Talking about her:
Modelo Ustedes son profesores.
Nosotros somos profesores.
1. Nosotros somos estudiantes.
Ustedes .
2. Usted es de Puerto Rico.
Ella .
3. Nosotros somos conductores.
Ellos .
4. Yo soy turista.
Tú .
5. Ustedes son de México.
Nosotras .
6. Ella es profesora.
Yo .
7. Tú eres de España.
Él .
8. Ellos son pasajeros.
Ellas
Modelo Yo soy Jorge.
1. Hola, me llamo Jorge y de Cuba. Pilar y Nati de España. Pedro, Juan y Paco de México. Todos estudiantes. La señorita Blasco de San Antonio. Ella la profesora. Luis el conductor. Él de Puerto Rico. Ellos de los Estados Unidos. El autobús de la agencia Marazul. Todos pasajeros de la agencia de viajes Marazul. Perdón, ¿de dónde tú, quién ella y de quién las maletas?
Modelo nombre / el pasajero
Es el nombre del pasajero.
.
DDL 24 hours reading the article and writing a 1-page doubl.docxedwardmarivel
DDL:
24 hours
reading the article and writing a
1-page double space
annotated bibliography
including:
1.reference
2.specify the concept you will use
3.explain its significance to the course
4.specify how you'll use it in your project
see the article and project inf below
.
*
DCF valuation methodSuper-normal growth modelApplications: single CF, annuity, perpetuity, uneven CFs, bond, stock, etc.
LECTURE 2 Valuation Basics
(Chapters 4, 6, 7)
*
Amount of cash flows expectedRisk of the cash flows Timing of the cash flow stream
Factors that Determine Value
*
DCF Method: General Formula
Finding PVs is discounting. The discount factor i is determined by the cost of capital invested.
*
10%
Single Cash Flow
100
0
1
2
3
PV = ?
What’s the PV of $100 due in 3 years if i = 10%?
*
Financial Calculator Setup
BGN END
P/Y 1
FORMAT: DEC 4 or larger
*
Financial Calculator
Solution
s
N I/YR PV PMTFV
?
N = 3, I/YR = 10, PMT = 0, FV = 100
CPT, PV
-75.13
/
INPUTS
OUTPUT
*
Spreadsheet
.
DDBA 8307 Week 2 Assignment Exemplar
John Doe[footnoteRef:1] [1: Type your name here]
DDBA 8307-6[footnoteRef:2] [2: Type in DDBA section number (e.g. DDBA 8307 – 6) ]
Dr. Jane Doe[footnoteRef:3] [3: Enter faculty name here.]
1
Scales of Measurement
Type text here. Discuss the implications of “scales of measurement” in quantitative research. Be sure to use a minimum of two citations to support your position(s). Be sure to review the “Scales of Measurement” media from Week 1. This section should be no more than two paragraphs.
Research Question
What are the means, standard deviations, frequencies, and percentages of the Lesson 21 Exercise File variables?
Presentation of Findings
I analyzed data from Lesson 21 Exercise File [footnoteRef:4]. In this section, I present descriptive statistics for the study quantitative and qualitative variables. Appropriate APA tables and figures accompany the analysis[footnoteRef:5]. [4: Insert the appropriate file name. ] [5: The tables and figures from your SPSS output will need to be copied and pasted in the appropriate location.]
Descriptive Statistics[footnoteRef:6] [6: Detailed information can be found in Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables,” and Lesson 21, “Univariate Descriptive Statistics for Quantitative Variables,” in the Green and Salkind text.
]
Descriptive statistics were run for the quantitative and qualitative variables in the Week 1 Assignment data set. Table 1 depicts the means and standard deviations for the quantitative data. Figure 1 depicts a histogram for the GPA variable. Table 2 depicts the frequencies and percentages for the qualitative (categorical) data. Figure 2 depicts a pie chart for the ethnic variable. Appendix 1 depicts the SPSS output.
Table 1[footnoteRef:7] [7: This is an example of an APA-formatted descriptive statistics table. Refer to Sections 5.01-5.19, in the APA Manual for detailed information on APA tables. The descriptive statistics table here includes the appropriate information derived from the SPSS output that is to be pasted as an appendix. Do not split tables across pages. Note: The numbers in the SPSS output presented here are fictitious numbers and do not represent correct numbers in the data set you will use for this application.
]
Means (M) and Standard Deviations (SD) for Study
Quantitative Variables (N = 105)
Variable[footnoteRef:8] [8: You would simply add rows to the table to accommodate the variables you have used in the analysis (i.e., variable 3, variable 4, etc.). Hint: Use the Microsoft Word Table feature.
]
M
SD
GPA
2.78
.76
Final
61.48
7.94
Percent
80.34
12.12
Figure 1. Histogram of GPA distribution.
Table 2[footnoteRef:9] [9: Recall from Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables” (Green & Salkind, 2017), frequencies and percentages are reported for qualitative (nominal) variables. Note: Frequency and percentages are the only c.
DBM380 v14Create a DatabaseDBM380 v14Page 2 of 2Create a D.docxedwardmarivel
DBM/380 v14
Create a Database
DBM/380 v14
Page 2 of 2Create a Database
The following assignment is based on the business scenario for which you created both an entity-relationship diagram and a normalized database design in Week 2.
For this assignment, you will create multiple related tables that match your normalized database design. In other words, you will implement a physical design (an actual, usable database) based on a logical design.
Refer to the linked W3Schools.com articles “SQL CREATE TABLE Statement,” “SQL PRIMARY KEY Constraint,” “SQL FOREIGN KEY Constraint,” and “SQL INSERT INTO Statement” for help in completing this assignment.
Note: In the industry, even the most carefully thought out database designs can contain mistakes. Feel free to correct in your tables any mistakes you notice in your normalized database design. Also, note that in Microsoft® Access®, you follow the steps below to launch the SQL editor:
Figure 1. To create a SQL query in Microsoft® Access®, begin by clicking the CREATE tab.
To Complete This Assignment:
1. Use the CREATE TABLE statement to create each table in your design. Note that a table in a RDMS corresponds to an entity in an entity-relationship diagram. Recommended tables for this assignment are CUSTOMER, ORDER, ORDER_DETAIL, PRODUCT, EMPLOYEE, and STORE.
2. As part of each CREATE TABLE statement, define all of the columns, or fields, that you want each particular table to contain. Give them short, meaningful names and include constraints; that is, describe what type of data each column (field) is allowed to hold and any other constraints, such as size, range, or uniqueness.
3. Note that any field you marked as a unique identifier in your normalized database design is a key field. Key fields must be described as both UNIQUE and NOT NULL, which means a value must exist for each record and that value must be unique across all records.
4. After you have created all six tables, including relationships between the tables as appropriate (matching the primary key in one table to a foreign key in another table), use the INSERT INTO statement to insert 10 records into each of your tables. You will need to make up the data you insert into your tables. For example, to insert one record into the CUSTOMER table, you will need to invent a customer number, a customer name, and so on—one value for each of the fields you defined for the CUSTOMER table—to insert into the table.
5. To ensure that your INSERT INTO statements succeeded in populating your tables, use the SELECT statement described in Ch. 7, “Introduction to Structured Query Language,” in Database Systems: Design, Implementation, and Management.to retrieve the records you inserted. For example, to see all 10 records you inserted into the CUSTOMER table, you might apply the following SQL statement: SELECT * FROM CUSTOMER;
After you have created all six tables and populated ten records in each table, submit to the Assignment Files tab the database containin.
DB3.1 Mexico corruptionDiscuss the connection between pol.docxedwardmarivel
DB3.1: Mexico corruption
Discuss the connection between politics, corruption, and criminal organizations in Mexico. How would you go about separating these? Give examples and be specific. Support your ideas on why you would do these specific measures.
DB3.2: Collapse of Soviet Union
How has the collapse of the Soviet Union fostered pirate capitalism and organized crime? Be specific with your answer and support your answer. Do you think that if the Soviet Union did not collapse pirate capitalism and organized crime would still flourish? Support your opinion.
300 words per post
.
DB2Pepsi Co and Coke American beverage giants, must adhere to th.docxedwardmarivel
DB2
Pepsi Co and Coke American beverage giants, must adhere to the U.S Foreign Corruption Act wherever their businesses may take them. Both companies expanded their U.S businesses to India with differing initial results. Coke came home (initially) and Pepsi Co prospered.
Do your research and explain the socio-cultural barriers faced by these two companies? What in your view were the reasons which negatively impacted Coke and positively touched Pepsi Co?
WEEK 3:
Interactive
: Select one company other than the 2 mentioned above, and share this company’s experience in the United Arab Emirates. Comment on another learner’s company experience in a different location of the world.
WEEK 4:
Interactive
: Comment on a different learner’s company experience in a totally different location from those completed earlier. Do you feel that cultural training is an essential pre-requisite for expatriates in any host country? Why/Why not?
Remember to use APA referencing in the body of your posting.
.
DB1 What Ive observedHave you ever experienced a self-managed .docxedwardmarivel
DB1: What I've observed
Have you ever experienced a self-managed team? If so, describe it. If not, why do you think your organization has not embraced self managed teams?
DB2: Case Analysis
Review the case study at the end of Chapter 8, Frederick W. Smith - FedEx. Answer the five questions below:
1. How do the standards set by Fred Smith for FedEx teams improve organizational performance?
2. What motivates the members of FedEx to remain highly engaged in their teams?
3. Describe the role FedEx managers play in facilitating team effectiveness.
4. What types of teams does FedEx use? Provide evidence from the case to support your answer.
5. Leaders play a critical role in building effective teams. Cite evidence from the case that FedEx managers performed some of these roles in developing effective teams.
Image Source Team:
http://www.freedigitalphotos.net/images/gallery-thumbnails.php?id=50143103253525199427035558
.
DB Response 1I agree with the decision to search the house. Ther.docxedwardmarivel
DB Response 1
I agree with the decision to search the house. There was reasonable suspicion to believe the fugitive could have been in the home. The homeowner not only consented to the search of the house but requested it for her safety. Complacency kills. In this situation, the officer is very regretful in his decision to conduct a complacent search of the home, and luckily nobody was killed.
My department does not have body cameras, but I still conduct business as if somebody is recording me. We live in a generation of surveillance. You never know when there are hidden cameras, a camera on a business you did not notice, or a cell phone recording from the top floor of a building. We hire police officers with high amounts of integrity because the definition of integrity is doing the right thing even when nobody is looking. I would be lying if I said my grandmother would approve of everything I do on the job. I am most guilty of foul language and it is something that I am working on not doing that. However, I can emphatically say I work with integrity and honesty without a doubt.
I think setting limits on tolerable behavior in regards to sexual and general harassment is appropriate; however, there are too many situations to make a policy for every behavior one could find inappropriate. When it comes to using force again every situation is different but there should be a pretty well laid out policy at departments for when and how an officer should use a certain amount of force. Officers should be trained on de-escalation tactics and alternatives to using force. Tactical training should include strategies to create time, space, and distance, to reduce the likelihood that force will be necessary and should occur in realistic conditions appropriate to the department’s location (U.S. Commission On Civil Rights, 2018).
Philippians 2 verses 3 – 8 is a pretty straightforward verse with great leadership lessons. Be humble, put others before yourself, and be a servant leader.
From the very beginning of any interrogation, the accused has constitutional rights not to speak to police and also to have an attorney present. The Eighth Amendment to the Constitution prohibits cruel and unusual punishments placed upon any persons in the U.S. With these rights in mind I will only go as far as the Constitution allows when interrogating this suspect even if the suspect admits where the child is if the admission was coerced that admission could get thrown out of court. I would never compromise the investigation. There are other ways to find the abducted girl through detective work than just interrogating the suspect. The cost of illegal interrogations is documented in the number of lost prosecutions. Literally, thousands of cases across the country have had to be dismissed because prosecutors could not trust that the evidence provided by police officers was legitimate or the officer had lost credibility as a witness in all cases because of his or her wrongdoing (P.
DB Response prompt ZAKChapter 7, Q1.Customers are expecting.docxedwardmarivel
DB Response prompt ZAK
Chapter 7, Q1.
Customers are expecting more from their service providers. Rather than traditionally accepting boilerplate offerings from service providers, customers desire that service providers cater to their requests. Organizations providing services must keep up with the customer’s demand or risk losing business to others who will. Many service providers have been adopting lean principles to accommodate the needs of their customers in successful attempts to decrease waste, increase efficiency, improve customer service and satisfaction (Daft, 2016, p. 275). From online music providers, customers expect music tracks personalized for their tastes. From airlines, customers can expect preflight seat and meal selections. Amazon.com provides custom personalization to a customers’ home pages by placing personally directed advertisements and products which the customer is more likely to order from the company. Amazon book recommendations are personalized to the specific customer and are provided based upon previous books read. With customers expecting customized and catered experiences, companies need to keep up with this demand and embrace mass customization in order to obtain and retain customers.
Chapter 7, Q2.
While many facets of businesses may involve craft technology, it is still important for business schools to teach management. Some businesses which only expect their leaders to gain knowledge and expertise from experience, may be creating a bureaucratic and restricted model for their business. Companies which rely only on internal training for their leaders can miss opportunities from potential leaders coming in from the outside. Business schools which teach management can provide potential leaders with a foundation to draw from. Teaching management can expose students to issues and opportunities experienced by others, not just ones restricted to one specific company. Teaching management from a textbook is just one method of conveying information. Just as one would not necessarily be proficient in piloting a boat from reading a book, a textbook about doing so would provide the student with underlying concepts which could dramatically increase the success of the student when they move to an actual boat. This textbook based training would be further enhanced with some practical experience.
Chapter 8, Q1.
Technology has progressed allowing real time instant messaging and virtual meetings. High level managers can indeed expect technology to allow them to do their jobs with little face-to-face communication, but they should question if that is something they really want to do. There are currently methods available which could be used effectively to communicate with subordinates, employees and stockholders, such as recorded feeds which would be able to reach every associated individual. These however may not provide a sense of personalization from the managers. Leaders in an organization may resort to using tec.
DB Topic of Discussion Information-related CapabilitiesAnalyze .docxedwardmarivel
DB Topic of Discussion: Information-related Capabilities
Analyze 2 of the 14 information-related capabilities and explain how the joint force can use these capabilities to affect the three dimensions of the information environment. Give examples of real-world or life events for the capabilities and how can you use these concepts as a CSM/SGM.
Consumer Brand Metrics Q3 2015
Eater Archetypes:
Brand usage and preferences by consumer segment
The restaurant industry has long relied on demographic factors to
identify and prioritize consumer groups. For example, many
brands currently obsess over attracting Millennials—some
without pausing to consider the variations among consumers
within this demographic cohort. In addition to life stages,
consumer attitudes about health, value, convenience and the
overall role of foodservice in their lives drive significant
differences in preferences and behavior.
With these distinctions in mind, we have updated the Consumer
Brand Metrics (CBM) survey with questions that allow us to
segment consumers into one of seven Eater Archetypes. Each
segment has a distinct psychographic profile, which is outlined in
our recent Consumer Foodservice Landscape. Accordingly, their
patronage of the segments and brands tracked in CBM varies.
This paper explores some differences we can discern after the
initial quarterly results, including the archetypes’ segment usage,
brand patronage and occasion dynamics. Examining CBM data by
Eater Archetype reveals nuances that complement a demographic
profile of a chain’s guests.
By Colleen Rothman, Manager, Consumer Insights
To learn more about the Consumer Brand Metrics program or to sign up for future
Spotlight by Consumer Brand Metrics white papers, please contact Bart Henyan,
Senior Marketing Manager, at [email protected]
Consumer Brand Metrics Q3 2015
Segmenting consumers by psychographic factors, rather than
just demographic characteristics, can lead to a better
understanding of the consumers that matter to your brand and
how to appeal to them.
Key Takeaways
Busy Balancers and Functional Eaters drive usage across
restaurants and convenience stores. Full-service restaurant
(FSR) operators may also consider targeting Foodservice
Hobbyists and Affluent Socializers, as these archetypes
comprise more than a quarter of FSR patrons, on average.
How does foodservice segment usage vary by archetype?
Driven by unique needs and motivations, Eater Archetypes
gravitate to a wide variety of brands. For example,
McDonald’s, Burger King and Whataburger each
disproportionately attract unique archetypes (Habitual
Matures, Bargain Hunters and Functional Eaters,
respectively).
Which chains do each archetype visit most frequently?
Archetypes that patronize the same restaurant may not use
the brand the same way. For example, usage varies by
daypart, with afternoon snacks skewing to Busy Balancers
and late-night meals d.
DB Instructions Each reply must be 250–300 words with a minim.docxedwardmarivel
DB Instructions:
Each reply must be 250–300 words with a minimum of 1 scholarly source. The scholarly source used for your thread and response should be in addition to the class textbooks.
Reference Book: Young, M. (2017). Learning the Art of Helping. Boston, MA: Pearson. ISBN: 9780134165783.
.
DB Defining White Collar CrimeHow would you define white co.docxedwardmarivel
DB: Defining White Collar Crime
How would you define white collar crime? What are the advantages and disadvantages of the various terms, such as “white collar crime,” “crimes of the powerful,” “elite deviance,” etc., used to describe the type of crimes.
300 Word Minimum
.
DB ASSIGNMENTFor this Discussion Board you will be developing a th.docxedwardmarivel
DB ASSIGNMENT
For this Discussion Board you will be developing a thematic unit for preschoolers. Choose your overarching theme and explain the main parts or features of your unit. Summarize the activities you will use to integrate content areas into you unit.
·
Your activities need to focus on the creative arts as well as content areas and include activities that are open-ended and allow children to make choices.
·
Your unit needs to be your own and not one that you have discovered on the internet or in a teacher’s manual.
Read your classmates units carefully and respond to them by sharing another open-ended activity that could be included in their unit.
PLZ RESPOND TO THESE STUDENT ABOUT WHAT THEY WROT ABOUT THE DB ASSIGNMENT
STUDENT 1 (100 WORDS OR MORE)
The month of April is a wonderful time to talk about the weather so I chose it as my theme. We are going to learn the different types of weather, the impact weather has on our lives, and what causes different weather patterns. We will be using the reading, science, art, and music centers to ensure we include all the different ways children can learn. Although most themes for children this young are only a few weeks long we will be using the entire month in order to experience different types of weather and include the two field trips that are planned. We will be using both experienced-based and emerging curriculum (Isbell & Raines,2013) so that the children are comfortable learning things they already have experience with and challenging them with new knowledge. We will be introducing new vocabulary about the weather and taking clues from our discussions on what the children want to explore further.
On the first day we will read the book "Oh say can you say, what's the weather today" by Tish Rabe. This book uses a familiar character, The Cat in the Hat, to introduce new words to the reader and even has a vocabulary list in the back to help define the words. Copies of this book and other weather related books will be added to the reading center for the children to look at during their free time. During circle time we will discuss some of the new words and what they mean. Observing the children as they talk about the weather the teacher will be able to decide where their interest is and what she needs to focus on. Knowing that children learn best what they are already interested in (Isbell & Raines,2013) is key to keeping these lessons fun and making sure the children get the most out of our projects.
The science center will be a major focus for this months theme. A water table and wind machine is added to give the children hands on learning opportunities. We will make a weather chart that will be hung in the science center and every day a child will go to the window, check the weather and add the appropriate label, a sun for sunny, a cloud for cloudy, etc. Giving the child the freedom to choose the correct symbol even if more than one applies helps all the children to accept the ideas o.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
1. DDBA 8307 Week 4 Assignment Template
John Doe
DDBA 8307-6
Dr. Jane Doe
1
One-Way ANOVA
Type text here. You will describe and defend using the one-way
ANOVA test for your analysis. Use at least two outside
resources—that is, resources not provided in the course
resources, readings, etc. These citations will be presented in the
References section. This exercise will give you practice for
addressing Rubric Item 2.13b, which states, “Describes and
defends, in detail, the statistical analyses that the student will
conduct….” This section should be no more than two
paragraphs.
Research Question
Type research question here. See the Week 4 Assignment
Exemplar for more detail.
Hypotheses
Type null and alternative hypotheses here. See the Week 4
Assignment Exemplar for more detail.
2. Results
4
Type results here based upon the SPSS output. See the Week 4
Assignment Exemplar for more detail.
References
Type references here in proper APA format.
Appendix – One-Way ANOVA SPSS Output
Data Science &
Big Data Analytics
Discovering, Analyzing, Visualizing
and Presenting Data
EMC Education Services
4. Rosewood Drive, Danvers, MA
01923, (978) 750-8400, fax (978) 646-8600. Requests to the
Publisher for permission should be addressed to the Permissions
Department, John Wiley & Sons, Inc.,
111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax
(201) 748-6008, or online at http: I /www. wiley. com/
go/permissions.
limit ofliability/DisclaimerofWarranty: The publisher and the
author make no representations or warranties with respect to the
accuracy or completeness of
the contents of this work and specifically disclaim all
warranties, including without limitation warranties of fitness for
a particular purpose. No warranty may be
created or extended by sales or promotional materials. The
advice and strategies contained herein may not be suitable for
every situation. This work is sold with
the understanding that the publisher is not engaged in rendering
legal, accounting, or other professional services. If professional
assistance is required, the
services of a competent professional person should be sought.
Neither the publisher nor the author shall be liable for damages
arising herefrom. The fact that an
organization or Web site is referred to in this work as a citation
and/or a potential source of further information does not mean
that the author or the publisher
endorses the information the organization or website may
provide or recommendations it may make. Further, readers
should be aware that Internet websites
listed in this work may have changed or disappeared between
when this work was written and when it is read.
For general information on our other products and services
please contact our Customer Care Department within the United
States at (877) 762-2974, outside the
United States at (317) 572-3993 orfax (317) 572-4002.
5. Wiley publishes in a variety of print and electronic formats and
by print-on-demand. Some material included with standard print
versions of this book may not be
included in e-books or in print-on-demand.lf this book refers to
media such as a CD or DVD that is not included in the version
you purchased, you may download
this material at http: I /book support. wiley. com. For more
information about Wiley products, visit www. wiley. com.
library of Congress Control Number: 2014946681
Trademarks: Wiley and the Wiley logo are trademarks or
registered trademarks of John Wiley & Sons, Inc. and/or its
affiliates, in the United States and other coun-
tries, and may not be used without written permission. All other
trademarks are the property of their respective owners. John
Wiley & Sons, Inc. is not associated
with any product or vendor mentioned in this book.
Credits
Executive Editor
Carol Long
Project Editor
Kelly Talbot
Production Manager
Kathleen Wisor
Copy Editor
6. Karen Gill
Manager of Content Development
and Assembly
Mary Beth Wakefield
Marketing Director
David Mayhew
Marketing Manager
Carrie Sherrill
Professional Technology and Strategy Director
Ba rry Pruett
Business Manager
Amy Knies
Associate Publisher
Jim Minatel
Project Coordinator, Cover
Patrick Redmond
Proofreader
Nancy Carrasco
Indexer
Johnna Van Hoose Dinse
Cover Designer
Mallesh Gurram
7. About the Key Contributors
David Dietrich heads the data science education team within
EMC Education Services, where he leads the
curriculum, strategy and course development related to Big Data
Analytics and Data Science. He co-au-
thored the first course in EMC's Data Science curriculum, two
additional EMC courses focused on teaching
leaders and executives about Big Data and data science, and is a
contributing author and editor of this
book. He has filed 14 patents in the areas of data science, data
privacy, and cloud computing.
David has been an advisor to severa l universities looking to
develop academic programs related to data
analytics, and has been a frequent speaker at conferences and
industry events. He also has been a a guest lecturer at universi-
ties in the Boston area. His work has been featured in major
publications including Forbes, Harvard Business Review, and
the
2014 Massachusetts Big Data Report, commissioned by
Governor Deval Patrick.
Involved with analytics and technology for nearly 20 years,
David has worked with many Fortune 500 companies over his
career, holding mu lti ple roles involving analytics, including
managing ana lytics and operations teams, delivering analytic
con-
sulting engagements, managing a line of analytical software
products for regulating the US banking industry, and developing
8. Sohware-as-a-Service and BI-as-a-Service offerings.
Additionally, David collaborated with the U.S. Federal Reserve
in develop-
ing predictive models for monitoring mortgage portfolios.
Barry Heller is an advisory technical education consultant at
EMC Education Services. Barry is a course developer and cu r-
riculum advisor in the emerging technology areas of Big Data
and data science. Prior to his current role, Barry was a consul-
tant research scientist leadi ng numerous analytical initiatives
within EMC's Total Customer Experience
organization. Early in his EMC career, he managed the
statistical engineering group as well as led the
data warehousing efforts in an Enterprise Resource Planning
(ERP) implementation. Prior to joining EMC,
Barry held managerial and analytical roles in reliability
engineering functions at medical diagnostic and
technology companies. During his career, he has applied his
quantitative skill set to a myriad of business
applications in the Customer Service, Engineering, Ma
nufacturing, Sales/Marketing, Finance, and Legal
arenas. Underscoring the importance of strong executive
stakeholder engagement, many of his successes
have resulted from not only focusing on the technical details of
an analysis, but on the decisions that will be resulting from
the analysis. Barry earned a B.S. in Computational Mathematics
from the Rochester Institute ofTechnology and an M.A. in
Mathematics from the State University of New York (SUNY)
New Paltz.
Beibei Yang is a Technical Education Consultant of EMC
9. Education Services, responsible for developing severa l open
courses
at EMC related to Data Science and Big Data Analytics. Beibei
has seven years of experi ence in the IT industry. Prior to EMC
she
worked as a sohware engineer, systems manager, and network
manager for a Fortune 500 company where she introduced
new technologies to improve efficiency and encourage
collaboration. Beibei has published papers to
prestigious conferences and has filed multiple patents. She
received her Ph.D. in computer science from
the University of Massachusetts Lowell. She has a passion
toward natural language processing and data
mining, especially using various tools and techniques to find
hidden patterns and tell storie s with data.
Data Science and Big Data Analytics is an exciting domain
where the potential of digital information is
maximized for making intelligent business decisions. We
believe that this is an area that will attract a lot of
talented students and professiona ls in the short, mid, and long
term.
Acknowledgments
EMC Education Services embarked on learning this subject with
the intent to develop an "open" curriculum and
certification. It was a challenging journey at the time as not
many understood what it would take to be a true
data scientist. After initial research (and struggle), we were able
10. to define what was needed and attract very
talented professionals to work on the project. The course, "Data
Science and Big Data Analytics," has become
well accepted across academia and the industry.
Led by EMC Education Services, this book is the result of
efforts and contributions from a number of key EMC
organizations and supported by the office of the CTO, IT,
Global Services, and Engi neering. Many sincere
thanks to many key contributors and subject matter experts
David Dietrich, Barry Heller, and Beibei Yang
for their work developing content and graphics for the chapters.
A special thanks to subject matter experts
John Cardente and Ganesh Rajaratnam for their active
involvement reviewing multiple book chapters and
providing valuable feedback throughout the project.
We are also grateful to the fol lowing experts from EMC and
Pivotal for their support in reviewing and improving
the content in this book:
Aidan O'Brien Joe Kambourakis
Alexander Nunes Joe Milardo
Bryan Miletich John Sopka
Dan Baskette Kathryn Stiles
Daniel Mepham Ken Taylor
Dave Reiner Lanette Wells
Deborah Stokes Michael Hancock
11. Ellis Kriesberg Michael Vander Donk
Frank Coleman Narayana n Krishnakumar
Hisham Arafat Richard Moore
Ira Sch ild Ron Glick
Jack Harwood Stephen Maloney
Jim McGroddy Steve Todd
Jody Goncalves Suresh Thankappan
Joe Dery Tom McGowa n
We also thank Ira Schild and Shane Goodrich for coordinating
this project, Mallesh Gurram for the cover design, Chris Conroy
and Rob Bradley for graphics, and the publisher, John Wiley
and Sons, for timely support in bringing this book to the
industry.
Nancy Gessler
Director, Education Services, EMC Corporation
Alok Shrivastava
Sr. Direc tor, Education Services, EMC Corporation
12. Contents
Introduction ................ . .. . .....• . •.. ... .... •..... .. .. . .. .
.......... .. ... . ..................... •.•...... xvii
Chapter 1 • Introduction to Big Data Analytics ................... . . .
....................... 1
1.1 Big Data Overview ..................... ....... .....•... • ...... . . .
........ • .. ... . . ... ....... ....... 2
1.1.1 Data Structures .. . .. . . . .. ................ ... ... . .. . ...... . ..
.. .... . .................... ..... . .. . . . .. 5
1.1.2 Analyst Perspective on Data Repositories .
............................. . .......... .......•. ... ... .. .. 9
1.2 State of the Practice in Analytics
................................................................. . 11
1.2.1 Bl Versus Data Science .............. .... ....... . .. . ........... . .
. .... . ....................... .. .... 12
1.2.2 Current Analytical Architecture ... . .... .• . . ................
.... .............. .... .... ...... •.. . ..... 13
1.2.3 Drivers of Big Data .................................................... . .
. .. ................. .. ... . . 15
1.2.4 Emerging Big Data Ecosystem and a New Approach to
Analytics .. ....... ...... . ............ .. ....... 16
1.3 Key Roles for the New Big Data Ecosystem ....... ..... .........
. ....... . ..... .. .................... 19
1.4 Examples of Big Data Analytics ... .... .......... .... . ... .......
... .... . ...... . .................... 22
Summary .............. ............ ... ... ......... .... • ... •....... ........ .. •
..•... . ................ 23
Exercises ..................... .... ..... .. ...... . ......•......... .. .. . ... ....
. ..•.................... 23
Bibliography ........................... .... .. ... ... ... •................... .. •
...... ..... ..... ....... 24
25. 12.3.2 Evolution of a Graph ................ ..... .... ............. ...... .
...... •.•... •. •.•......... •.... 380
12.3.3 Common Representation Methods .............. .. ............ ..
. . . •. • .. . .... • . . ................ 386
12.3.4 How to Clean Up a Graphic ................... •. . . .... . ..... .
.......... . . . ..... . ... .......... ... .387
12.3.5 Additional Considerations ..... ................. .... ... . ..... .. .
. . . •.•. .. ... . •.• ...... . ...... ... . 392
Summary ............ .. .........................•...... • ... • . ... .........•...
•..................... 393
Exercises ........... . . .... . ................. .. .. . . . .... • ................. .
. .. . .. • .......... . ....... 394
References and Further Reading ... .. ............ .... ...... .....
......... . .... . . .................... 394
Bibliography .... . . ... ......... .... . ........................ • .................
.. . .. .. . ... . . ... ...... 394
Index .. . .............. . .. . .. . .. . . .. . . ........... . . . .. . .. . . . .......
. . . ... . . .. . .. .. . .. . . . ... .. . . ............... . 397
Foreword
Technological advances and the associated changes in practical
daily life have produced a rapidly expanding
"parallel universe" of new content, new data, and new
information sources all around us. Regardless of how one
defines it, the phenomenon of Big Data is ever more present,
ever more pervasive, and ever more important. There
is enormous value potential in Big Data: innovative insights,
improved understanding of problems, and countless
opportunities to predict-and even to shape-the future. Data
Science is the principal means to discover and
tap that potential. Data Science provides ways to deal with and
26. benefit from Big Data: to see patterns, to discover
relationships, and to make sense of stunningly varied images
and information.
Not everyone has studied statistical analysis at a deep level.
People with advanced degrees in applied math-
ematics are not a commodity. Relatively few organizations have
committed resources to large collections of data
gathered primarily for the purpose of exploratory analysis. And
yet, while applying the practices of Data Science
to Big Data is a valuable differentiating strategy at present, it
will be a standard core competency in the not so
distant future.
How does an organization operationalize quickly to take
advantage of this trend? We've created this book for
that exact purpose.
EMC Education Services has been listening to the industry and
organizations, observing the multi-faceted
transformation of the technology landscape, and doing direct
research in order to create curriculum and con-
tent to help individuals and organizations transform themselves.
For the domain of Data Science and Big Data
Analytics, our educational strategy balances three things:
people-especially in the context of data science teams,
processes-such as the analytic lifecycle approach presented in
this book, and tools and technologies-in this case
with the emphasis on proven analytic tools.
So let us help you capitalize on this new "parallel universe" that
surrounds us. We invite you to learn about
Data Science and Big Data Analytics through this book and
hope it significantly accelerates your efforts in the
transformational process.
27. Introduction
Big Data is creating significant new opportunities for
organizations to derive new value and create competitive
advantage from their most valuable asset: information. For
businesses, Big Data helps drive efficiency, quality, and
personalized products and services, producing improved levels
of customer satisfaction and profit. For scientific
efforts, Big Data analytics enable new avenues of investigation
with potentially richer results and deeper insights
than previously available. In many cases, Big Data analytics
integrate structured and unstructured data with real-
time feeds and queries, opening new paths to innovation and
insight.
This book provides a practitioner's approach to some of the key
techniques and tools used in Big Data analytics.
Knowledge ofthese methods will help people become active
contributors to Big Data analytics projects. The book's
content is designed to assist multiple stakeholders: business and
data analysts looking to add Big Data analytics
skills to their portfolio; database professionals and managers of
business intelligence, analytics, or Big Data groups
looking to enrich their analytic skills; and college graduates
investigating data science as a career field.
The content is structured in twelve chapters. The first chapter
introduces the reader to the domain of Big Data,
the drivers for advanced analytics, and the role of the data
scientist. The second chapter presents an analytic project
lifecycle designed for the particular characteristics and
challenges of hypothesis-driven analysis with Big Data.
Chapter 3 examines fundamental statistical techniques in the
28. context of the open source R analytic software
environment. This chapter also highlights the importance of
exploratory data analysis via visualizations and reviews
the key notions of hypothesis development and testing.
Chapters 4 through 9 discuss a range of advanced analytical
methods, including clustering, classification,
regression analysis, time series and text analysis.
Chapters 10 and 11 focus on specific technologies and tools that
support advanced analytics with Big Data. In
particular, the Map Reduce paradigm and its instantiation in the
Hadoop ecosystem, as well as advanced topics
in SOL and in-database text analytics form the focus of these
chapters.
XVIII ! INTRODUCTION
Chapter 12 provides guidance on operationalizing Big Data
analytics projects. This chapter focuses on creat·
ing the final deliverables, converting an analytics project to an
ongoing asset of an organization's operation, and
creating clear, useful visual outputs based on the data.
EMC Academic Alliance
University and college faculties are invited to join t he
Academic Alliance program to access unique "ope n"
curriculum-based education on the following top ics:
• Data Science and Big Data Analytics
• Information Storage and Management
• Cloud Infrastructure and Services
29. • Backup Recovery Systems and Architecture
The program provides faculty with course re sources to prepare
students for opportunities that exist in today's
evolving IT industry at no cost. For more information, visit
http: // education . EMC . com/ academicalliance.
EMC Proven Professional Certification
EMC Proven Professional is a leading education and
certification program in the IT industry, providing compre-
hensive coverage of information storage technologies,
virtualization, cloud computing, data science/ Big Data
analytics, and more.
Being proven means investing in yourself and formally
validating your expertise.
This book prepares you for Data Science Associate (EMCDSA)
certification. Visit http : I I educat i on . EMC
. com for details.
INTRODUCTION TO BIG DATA ANAL YTICS
Much has been written about Big Data and the need for
advanced analytics within industry, academ ia,
and government. Availa bility of new data sources and the rise
of more complex analytical opportunities
30. have created a need to rethink existing data architectures to
enable analytics that take advantage of Big
Data. In addition, sig nificant debate exists about what Big Data
is and what kinds of skil ls are required to
make best use of it. This chapter explains severa l key concepts
to clarify what is meant by Big Data, why
adva nced analyt ics are needed, how Data Science differs from
Business Intelligence (BI), and what new
roles are needed for the new Big Data ecosystem.
1.1 Big Data Overview
Data is created constantly, and at an ever-increasing rate.
Mobile phones, social media, imaging technologies
to determine a medical diagnosis-all these and more create new
data, and that must be stored somewhere
for some purp ose. Devices and sensors automatically generate
diagnostic information that needs to be
stored and processed in real time. Merely keeping up with this
huge influx of data is difficult, but su bstan-
tially more cha llenging is analyzing vast amounts of it,
especially when it does not conform to traditional
notions of data structure, to identify meaningful patterns and
extract useful information. These challenges
of the data deluge present the opportunity to transform business,
government, science, and everyday life.
Several industries have led the way in developing their ability
to gather and exploit data:
• Credit ca rd companies monitor every purchase their
customers make and can identify fraudulent
31. purchases with a high degree of accuracy using rules derived by
processing billions of transactions.
• Mobi le phone companies analyze subscribers' calling patterns
to determine, for example, whether a
caller's frequent contacts are on a rival network. If that rival
network is offeri ng an attractive promo-
tion t hat might cause the subscriber to defect, the mobile phone
company can proactively offer the
subscriber an incentive to remai n in her contract.
• For compan ies such as Linked In and Facebook, data itself is
their primary product. The valuations of
these compan ies are heavi ly derived from the data they gather
and host, which contains more and
more intrinsic va lue as the data grows.
Three attributes stand out as defining Big Data characteristics:
• Huge volume of data: Rather than thousands or millions of
rows, Big Data can be billions of rows and
millions of columns.
• Complexity of data t ypes and st ructures: Big Data reflects
the variety of new data sources, forma ts,
and structures, including digital traces being left on the web and
other digital repositories for subse-
quent analysis.
• Speed of new dat a crea tion and growt h: Big Data can
describe high velocity data, with rapid data
ingestion and near real time analysis.
Although the vol ume of Big Data tends to attract the most
attention, genera lly the variety and veloc-
32. ity of the data provide a more apt defi nition of Big Data. (Big
Data is sometimes described as havi ng 3 Vs:
volu me, vari ety, and velocity.) Due to its size or structure, Big
Data cannot be efficiently analyzed using on ly
traditional databases or methods. Big Data problems req uire
new tools and tech nologies to store, manage,
and realize the business benefit. These new tools and
technologies enable creation, manipulation, and
1.1 Big Data Overview
management of large datasets and t he storage environments that
house them. Another definition of Big
Data comes from the McKi nsey Global report from 2011:
Big Data is data whose s cale, dis tribution, diversity, and/ or
timeliness require th e
use of new technical architectures and analytics to e nable
insights that unlock ne w
sources of business value.
McKinsey & Co.; Big Data: The Next Frontier for Innovation,
Competit ion, and
Prod uctivity [1]
McKinsey's definition of Big Data impl ies that orga nizations
will need new data architectures and ana-
lytic sandboxes, new tools, new analytical methods, and an
integration of multiple skills into the new ro le
of the data scientist, which will be discussed in Section 1.3.
Figure 1-1 highlights several sources of the Big
33. Data deluge.
What's Driving Data Deluge?
Mobile
Sensors
Smart
Grids
Social
Media
Geophysical
Exploration
FtGURE 1-1 What 's driving the da ta deluge
Video
Surveillance
• Medical Imaging
Video
Rendering
Gene
Seque ncing
The rate of data creation is accelerating, driven by many of the
items in Figure 1-1.
Social media and genetic sequencing are among the fastest-
growing sources of Big Data and examples
of untraditional sources of data being used for analysis.
34. For example, in 2012 Facebook users posted 700 status updates
per second worldwide, which can be
leveraged to deduce latent interests or political views of users
and show relevant ads. For instance, an
update in wh ich a woman changes her relationship status from
"single" to "engaged" wou ld t rigger ads
on bri dal dresses, wedding plann ing, or name-changing
services.
Facebook can also construct social graphs to ana lyze which
users are connected to each other as an
interconnected network. In March 2013, Facebook released a
new featu re called "Graph Search," enabling
users and developers to search social graphs for people with
similar interests, hobbies, and shared locations.
INTRODUCTION TO BIG DATA ANALYTICS
Another example comes from genomics. Genetic sequencing and
human genome mapping provide a
detailed understanding of genetic makeup and lineage. The
health care industry is looking toward these
advances to help predict which illnesses a person is li kely to
get in his lifetime and take steps to avoid these
maladies or reduce their impact through the use of personalized
med icine and treatment. Such tests also
highlight typical responses to different medications and
pharmaceutical drugs, heightening risk awareness
of specific drug treatments.
While data has grown, the cost to perform this work has fall en
35. dramatically. The cost to sequence one
huma n genome has fallen from $100 million in 2001 to $10,000
in 2011, and the cost continues to drop. Now,
websites such as 23andme (Figure 1-2) offer genotyp ing for
less than $100. Although genotyping analyzes
on ly a fraction of a genome and does not provide as much
granularity as genetic sequencing, it does point
to the fact that data and complex analysis is becoming more
prevalent and less expensive to deploy.
23 pairs of
chromosomes.
One unique you.
Bring your ancestry to life.
F1ncl out what percent or your DNA comes !rom
populations around the world. rang1ng from East As1a
Sub-Saharan Alllca Europe, and more. B1eak
European ancestry down 1010 d1st1nct regions such as
the Bnush Isles. Scnnd1navla Italy and Ashkenazi
Jewish. People IVIh mixed ancestry. Alncan
Amencans. Launos. and Nauve Amencans w111 also
get a detailed breakdown.
20.5%
( .t A! n
Find relatives across
continents or across
the street.
Build your family tree
and enhance your
ex erience.
36. : 38.6%
· s, b·S 1h Jn Afr c.an
24.7%
Europe.,,
•
' Share your knowledge. Watch it
row.
FIGURE 1-2 Examples of what can be learned through
genotyping, from 23andme.com
1.1 Big Dat a Overview
As illustrated by the examples of social media and genetic
sequencing, ind ividuals and organizations
both derive benefits from analysis of ever-larger and more comp
lex data sets that require increasingly
powerful analytical capabilities.
1.1.1 Data Structures
Big data can come in multiple forms, including structured and
non -structured data such as financial
data, text files, multimedia files, and genetic mappings.
Contrary to much of the traditional data ana lysis
performed by organizations, most of the Big Data is
unstructured or semi-structured in nature, which
requires different techniques and tools to process and analyze.
[2) Distributed computing environments
37. and massively parallel processing (MPP) architectures that
enable parallelized data ingest and analysis are
the preferred approach to process such complex data.
With this in mind, this section takes a closer look at data
structures.
Figure 1-3 shows four types of data structures, with 80-90% of
future data growth coming from non-
structured data types. [2) Though different, the four are
commonly mixed. For example, a classic Relational
Database Management System (RDBMS) may store call logs for
a software support call center. The RDBMS
may store characteristics of the support calls as typical
structured data, with attributes such as time stamps,
machine type, problem type, and operating system. In addition,
the system will likely have unstructured,
quasi- or semi-structured data, such as free-form call log
information taken from an e-mail ticket of the
problem, customer chat history, or transcript of a phone call
describing the technical problem and the solu-
tion or aud io file of the phone call conversation. Many insights
could be extracted from the unstructured,
quasi- or semi-structu red data in the call center data.
'0
Q)
E
u
2
iii
Q)
38. 0
~
Big Data Characteristics: Data Structures
Data Growth Is Increasingly Unstructured
I
Structured
FIGURE 1-3 Big Data Growth is increasingly unstructured
INTRODUCTION TO BIG DATA ANALYTICS
Although analyzing structured data tends to be the most familiar
technique, a different technique is
required to meet the challenges to analyze semi-structured data
(shown as XML), quasi-structured (shown
as a clickstream), and unstructured data.
Here are examples of how each of the four main types of data
structures may look.
o Structured data: Data containing a defined data type, format,
and structure (that is, transaction data,
online analytical processing [OLAP] data cubes, traditional
RDBMS, CSV files, and even simple spread-
sheets). See Figure 1-4.
SUMMER FOOD SERVICE PROGRAM 11
Data as of August 01. 2011)
Fiscal Number of Peak (July) Meals Total Federal
Year Sites Participation Served Expenditures 2]
39. ---Thousands-- -MiL- -Million$-
1969 1.2 99 2.2 0.3
1970 1.9 227 8.2 1.8
1971 3.2 569 29.0 8.2
1972 6.5 1,080 73.5 21.9
1973 11.2 1,437 65.4 26.6
1974 10.6 1,403 63.6 33.6
1975 12.0 1,785 84.3 50.3
1976 16.0 2,453 104.8 73.4
TQ3] 22.4 3,455 198.0 88.9
1977 23.7 2,791 170.4 114.4
1978 22.4 2,333 120.3 100.3
1979 23.0 2,126 121.8 108.6
1980 21.6 1,922 108.2 110.1
1981 20.6 1,726 90.3 105.9
1982 14.4 1,397 68.2 87.1
1983 14.9 1,401 71.3 93.4
1984 15.1 1,422 73.8 96.2
1985 16.0 1,462 77.2 111.5
1986 16.1 1,509 77.1 114.7
1987 16.9 1,560 79.9 129.3
1988 17.2 1,577 80.3 133.3
1989 18.5 1.652 86.0 143.8
1990 19? 1 ~Q? 91? 1~11
FIGURE 1-4 Example of structured data
o Semi-structured data: Textual data files with a discernible
pattern that enables parsing (such
as Extensible Markup Language [XML] data files that are self-
describing and defined by an XML
schema). See Figure 1-5.
o Quasi-structured data: Textual data with erratic data formats
40. that can be formatted with effort,
tools, and time (for instance, web clickstream data that may
contain inconsistencies in data values
and formats). See Figure 1-6.
o Unstructured data: Data that has no inherent structure, which
may include text documents, PDFs,
images, and video. See Figure 1-7.
1.1 Big Data Ove rvi ew
Quasi-structured data is a common phenomenon that bears
closer scrutiny. Consider the following
example. A user attend s the EMC World conference and
subsequently runs a Google search online to find
information related to EMC and Data Scien ce. This would
produce a URL such as https: I /www . googl e
. c om/ #q=EMC+ data +scienc e and a list of results, such as in
the first graphic of Figure 1-5.
- ~ ....- . .
•• 0
o:.~t.a c!':a=-set.•"~t.t-e">
<z:.~ca l':cc.p-eq-.:.:.v•"X-:J;.-cc:r.;:a c.:.t:~" cc::te::c.•"
:.::·~d.Q"e , c~.=cr:."!•: ">
<t.:.e:"!>~~C - :ead.:. ~o Clc~d Co~~e.:.~~, 3~Q' Dace., a ::d
T:~sced ! ! Sol~t.:.o~s</t.:.t!e>
clc::d cc::,r·..:e.:.::r; . ">
<l.:.::k =e:•"se;·:es!':eee" 1':=-et•" / R. /a;;e;;t c;s / ccv.rrp""' /
41. jo;n:e· ~ ze: c':." >
<l.:.::k =~:•"St.i':es!:eet." !-:.=-et•" / B1/a.s:t::;s t c,;u /
1ooorrapo g c / rra ·-. . C!!!! '" >
<l.:. ::Jc :-el""" !!t.)-'les!'l.eec " l':=e~•" / 5~ /a.:.;ets / c .,, /
corr:rtgjJ/ .. c!lcO""'. ve:-,.cade:- c:~s">
<l.:.::.k =e:• " st.:,·:esl':ee:t. " !':.:et• " 15· / a;;ee, t
c:z:Jisgrur:c ... / -e:;n;o;gs· ve:-tco;c• c='a ">
<~c::.;::t. :.:,1=e•" t.ex::. / : ·e:;asc::.pt. " s:-c• '" // c l a; t o ;n:
P' ' p;•"" ccrrt-.~ .. dce:t:t.- ; - ><I sc:l.p:t.>
< :~ c:.:.;::t. .!l:c •"' / R. /a:.sec:J(<~.;/ cgrr;;:c""/rred•--.1z ..
_2 I 6 I 2 .;;,;. "'j;. ~ 3 "' ></ ~c:.:.pt.>
FIGURE 1-5 Example of semi-structured data
Tool!un
QUKkt~b~
b:plorerbars
Go to
Stop
R<foosh
Zoom(IOO'Jil
Tcxtsa:e
&>coding
Sty!<
C• rct brOWSing
42. Source
Stc:unt frpclt
lnt~ ~loON I 0. tt u re-
Wdlpoge pnv.cy potoey_
P""""'JI>ond
Ful scr~
Ctri•Q
h e
F5
F7
Fll
After doing this search, the user may choose the second link, to
read more about the headline "Data
Scientist- EM( Educa tion, Training, and Certification." This
brings the user to an erne . com site focu sed on
this topic and a new URL, h t t p s : I / e d ucation . e rne . com/
guest / campa i gn / data_ science
INTRODUCTION TO BIG DATA ANALYTICS
1
. aspx, that displays the page shown as (2) in Figure 1-6. Arrivi
43. ng at this site, the user may decide to click
to learn more about the process of becoming certified in data
science. The user chooses a link to ward the
top of the page on Certifications, bringing the user to a new
URL: ht tps : I I education. erne. com/
guest / certifica tion / framewo rk / stf / data_science . aspx,
which is (3) in Figure 1-6.
Visiting these three websites adds three URLs to the log files
monitoring the user's computer or network
use. These three URLs are:
https: // www.google . com/# q=EMC+data+ s cience
https: // education . emc.com/ guest / campaign/ data science .
aspx
https : // education . emc . com/ guest / certification/ framework
/ stf / data_
science . aspx
- - ...... - .._.. ............. _
O.Uk*-andi'IO..~T~ · OIC~ o
---·- t..._ ·-- . -- ·-A-- ------·----- .. -,.. _ , _____ ....
0.. ldHIWI • DtC (Ot.aiiOI. l....,... and~ 0 --- -~-~· 1 .. ....... _
.. _....._. __ , ___ -~-·-·
· ~----"' .. ~_.,.. ..... -
:c ~::...~ and Cenbbcrt 0
t-e •·,-'""""... '•'-""'•• ..,...._ _ ... --......
~ .... __ .... .....,.,_.... ... ,...._~·
-
~O•Uik~R........, A0.1t-~~_,...h", • £MC O --------.. ... .- . '""
..._. ______ , ______ ...., -
- ···-.. ... -~--.-- ....
https:/ /www.google.com/#q
44. 3
------
---
,_ __
----
~-:::.::.::·--===-=-== .. ------·---------·------..---::=--.....::..-..=-
.:.-.=-.......
-- ·------·---
-·---·--·---·~--·-· -----------·--·--., ______ ... ___ ____ _ -·-------
---·-______ , _______ _
- -------~ · --· -----
>l __ _ __ , , _ _ _
... , ------., :::... ::
FiGURE 1-6 Example of EMC Data Science search results
1.1 Big Data Overview
FIGURE 1-7 Example of unstructured data: video about
Antarctica expedition [3]
This set of three URLs reflects the websites and actions taken to
find Data Science inform ation related
to EMC. Together, this comprises a clicksrream that can be
parsed and mined by data scientists to discover
45. usage patterns and uncover relation ships among clicks and
areas of interest on a website or group of sites.
The four data types described in this chapter are sometimes
generalized into two groups: structured
and unstructu red data. Big Data describes new kinds of data
with which most organizations may not be
used to working. With this in mind, the next section discusses
common technology arch itectures from the
standpoint of someone wanting to analyze Big Data.
1.1.2 Analyst Perspective on Data Repositories
The introduction of spreadsheets enabled business users to crea
te simple logic on data structured in rows
and columns and create their own analyses of business
problems. Database administrator training is not
requ ired to create spreadsheets: They can be set up to do many
things qu ickly and independently of
information technology (IT) groups. Spreadsheets are easy to
share, and end users have control over the
logic involved. However, their proliferation can result in "many
versions of the t ruth." In other words, it
can be challenging to determine if a particular user has the most
relevant version of a spreadsheet, with
the most current data and logic in it. Moreover, if a laptop is
lost or a file becomes corrupted, the data and
logic within the spreadsheet could be lost. This is an ongoing
challenge because spreadsheet programs
such as Microsoft Excel still run on many computers worldwide.
With the proliferation of data islands (or
spread marts), the need to centralize the data is more pressing
than ever.
46. As data needs grew, so did mo re scalable data warehousing
solutions. These technologies enabled
data to be managed centrally, providing benefits of security,
failover, and a single repository where users
INTRODUCTION TO BIG DATA ANALYTICS
could rely on getting an "official" source of data for finan cial
reporting or other mission-critical tasks. This
structure also enabled the creation ofOLAP cubes and 81
analytical tools, which provided quick access to a
set of dimensions within an RD8MS. More advanced features
enabled performance of in-depth analytical
techniques such as regressions and neural networks. Enterprise
Data Warehouses (EDWs) are critica l for
reporting and 81 tasks and solve many of the problems that
proliferating spreadsheets introduce, such as
which of multiple versions of a spreadsheet is correct. EDWs-
and a good 81 strategy-provide direct data
feeds from sources that are centrally managed, backed up, and
secured.
Despite the benefits of EDWs and 81, these systems tend to
restri ct the flexibility needed to perform
robust or exploratory data analysis. With the EDW model, data
is managed and controlled by IT groups
and database administrators (D8As), and data analysts must
depend on IT for access and changes to the
47. data schemas. This imposes longer lead ti mes for analysts to
get data; most of the time is spent waiting for
approvals rather than starting meaningful work. Additionally,
many times the EDW rul es restrict analysts
from building datasets. Consequently, it is com mon for
additional systems to emerge containing critical
data for constructing analytic data sets, managed locally by
power users. IT groups generally dislike exis-
tence of data sources outside of their control because, unlike an
EDW, these data sets are not managed,
secured, or backed up. From an analyst perspective, EDW and
81 solve problems related to data accuracy
and availabi lity. However, EDW and 81 introduce new
problems related to flexibility and agil ity, which were
less pronounced when dealing with spreads heets.
A solution to this problem is the analytic sandbox, which
attempts to resolve the conflict for analysts and
data scientists with EDW and more formally managed corporate
data. In this model, the IT group may still
manage the analytic sandboxes, but they will be purposefully
designed to enable robust analytics, while
being centrally managed and secured. These sandboxes, often
referred to as workspaces, are designed to
enable teams to explore many datasets in a controlled fashion
and are not typically used for enterprise-
level financial reporting and sales dashboards.
Many times, analytic sa ndboxes enable high-performance
computing using in-database processing-
48. the analytics occur within the database itself. The idea is that
performance of the analysis will be better if
the analytics are run in the database itself, rather than bringing
the data to an analytical tool that resides
somewhere else. In-database analytics, discussed further in
Chapter 11, "Advanced Analytics- Technology
and Tools: In-Database Analytics." creates relationships to
multiple data sources within an organization and
saves time spent creating these data feeds on an individual
basis. In-database processing for deep analytics
enables faster turnaround time for developing and executing
new analytic models, while reducing, though
not eli minating, the cost associated with data stored in local,
"shadow" file systems. In addition, rather
than the typical structured data in the EDW, analytic sandboxes
ca n house a greater variety of data, such
as raw data, textual data, and other kinds of unstructured data,
without interfering with critical production
databases. Table 1-1 summarizes the characteristics of the data
repositories mentioned in this section.
TABLE 1-1 Types of Data Repositories, from an Analyst
Perspective
Data Repository Characteristics
Spreadsheets and
data marts
("spreadmarts")
Spreadsheets and low-volume databases for record keeping
49. Analyst depends on data extracts.
Data Warehouses
Analytic Sandbox
(works paces)
1.2 State of the Practice in Analytics
Centralized data containers in a purpose-built space
Suppo rt s Bl and reporting, but restri cts robust analyses
Ana lyst d ependent o n IT and DBAs for data access and
schema changes
Ana lysts must spend significant t ime to g et aggregat ed and d
isaggre-
gated data extracts f rom multiple sources.
Data assets gathered f rom multiple sources and technologies fo
r ana lysis
Enables fl exible, high-performance ana lysis in a
nonproduction environ-
ment; can leverage in-d atabase processing
Reduces costs and risks associated w ith data replication into
"shadow" file
systems
50. "Analyst owned" rather t han "DBA owned"
There are several things to consider with Big Data Analytics
projects to ensure the approach fits w ith
the desired goals. Due to the characteristics of Big Data, these
projects le nd them selves to decision su p-
port for high-value, strategic decision making w ith high
processing complexi t y. The analytic techniques
used in this context need to be iterative and fl exible, due to the
high volume of data and its complexity.
Performing rapid and complex analysis requires high throughput
network con nections and a consideration
for the acceptable amount of late ncy. For instance, developing
a real- t ime product recommender for a
website imposes greater syst em demands than developing a
near· real·time recommender, which may
still pro vide acceptable p erform ance, have sl ight ly greater
latency, and may be cheaper to deploy. These
considerations requi re a different approach to thinking about
analytics challenges, which will be explored
further in the next section.
1.2 State of the Practice in Analytics
Current business problems provide many opportunities for
organizations to become more analytical and
data dri ven, as shown in Table 1 ·2.
51. TABLE 1-2 Business Drivers for Advanced Analytics
Business Driver Examples
Optimize business operations
Identify business ri sk
Predict new business opportunities
Comply w ith laws or regu latory
requirements
Sales, pricing, profitability, efficiency
Customer churn, fraud, default
Upsell, cross-sell, best new customer prospects
Anti-Money Laundering, Fa ir Lending, Basel II-III, Sarbanes-
Oxley(SOX)
INTRODUCTION TO BIG DATA ANALYTICS
Table 1-2 outlines four categories of common business problems
that organizations contend with where
they have an opportunity to leverage advanced analytics to
create competitive advantage. Rather than only
performing standard reporting on these areas, organizations can
apply advanced analytical techniques
to optimize processes and derive more value from these common
tasks. The first three examples do not
52. represent new problems. Organizations have been trying to
reduce customer churn, increase sales, and
cross-sell customers for many years. What is new is the
opportunity to fuse advanced analytical techniques
with Big Data to produce more impactful analyses for these
traditional problems. The last example por-
trays emerging regulatory requirements. Many compliance and
regulatory laws have been in existence for
decades, but additional requirements are added every year,
which represent additional complexity and
data requirements for organizations. Laws related to anti-money
laundering (AML) and fraud prevention
require advanced analytical techniques to comply with and
manage properly.
1.2.1 81 Versus Data Science
The four business drivers shown in Table 1-2 require a variety
of analytical techniques to address them prop-
erly. Although much is written generally about analytics, it is
important to distinguish between Bland Data
Science. As shown in Figure 1-8, there are several ways to
compare these groups of analytical techniques.
One way to evaluate the type of analysis being performed is to
examine the time horizon and the kind
of analytical approaches being used. Bl tends to provide reports,
dashboards, and queries on business
questions for the current period or in the past. Bl systems make
it easy to answer questions related to
quarter-to-date revenue, progress toward quarterly targets, and
understand how much of a given product
was sold in a prior quarter or year. These questions tend to be
closed-ended and explain current or past
behavior, typically by aggregating historical data and grouping
it in some way. 81 provides hindsight and
some insight and generally answers questions related to "when"
53. and "where" events occurred.
By comparison, Data Science tends to use disaggregated data in
a more forward-looking, exploratory
way, focusing on analyzing the present and enabling informed
decisions about the future. Rather than
aggregating historical data to look at how many of a given
product sold in the previous quarter, a team
may employ Data Science techniques such as time series
analysis, further discussed in Chapter 8, "Advanced
Analytical Theory and Methods: Time Series Analysis," to
forecast future product sales and revenue more
accurately than extending a simple trend line. In addition, Data
Science tends to be more exploratory in
nature and may use scenario optimization to deal with more
open-ended questions. This approach provides
insight into current activity and foresight into future events,
while generally focusing on questions related
to "how" and "why" events occur.
Where 81 problems tend to require highly structured data
organized in rows and columns for accurate
reporting, Data Science projects tend to use many types of data
sources, including large or unconventional
datasets. Depending on an organization's goals, it may choose to
embark on a 81 project if it is doing reporting,
creating dashboards, or performing simple visualizations, or it
may choose Data Science projects if it needs
to do a more sophisticated analysis with disaggregated or varied
datasets.
Exploratory
Analytical
54. Approach
Explanatory
I
, .. -- ---,
1 Busin ess 1
1 Inte lligence 1
, .... _____ ..,
Past
fiGUR E 1 ·8 Comparing 81 with Data Science
1.2.2 Current Analytical Architecture
1 .2 State ofthe Practice In Analytlcs
Predictive Analytics and Data Mini ng
(Data Sci ence)
Typical • Optimization. predictive modo lin£
Techniques forocastlnC. statlatlcal analysis
and • Structured/unstructured data. many
Data Types types of sources, very Ioree datasata
Common
Questions
Typical
Techniques
and
Data Types
55. Tim e
Common
Questions
• What II ... ?
• What's tho optlmaltconarlo tor our bualnoss?
• What wtll happen next? What II these trend$
continuo? Why Is this happonlnt?
Busi ness Intelligence
• Standard and ad hoc reportlnc. dashboards.
alerts, queries, details on demand
• Structured data. traditional sourcoa.
manac:eable datasets
• What happened lut quarter?
• How many units sold?
• Whore Is the problem? In whic h situations?
Future
As described earlier, Data Science projects need workspaces
that are purpose-built for experimenting with
data, with flexible and agile data architectures. Most
organizations still have data warehouses that provide
excellent support for traditional reporting and simple data
analysis activities but unfortunately have a more
56. difficult time supporting more robust analyses. This section
examines a typical analytical data architecture
that may exist within an organization.
Figure 1-9 shows a typical data architecture and several of the
challenges it presents to data scientists
and others trying to do advanced analytics. This section
examines the data flow to the Data Scientist and
how this individual tits into the process of getting data to
analyze on proj ects.
INTRODUCTION TO BIG DATA ANALYTICS
FIGURE 1-9 Typical analytic architecture
i..l ,_,
It
An alysts
Dashboards
Reports
Al erts
1. For data sources to be loaded into the data wa rehouse, data
needs to be well understood,
structured, and normalized with the appropriate data type defini
t ions. Although th is kind of
centralization enabl es security, backup, and fai lover of highly
57. critical data, it also means that data
typically must go through significant preprocessing and
checkpoints before it can enter this sort
of controll ed environment, which does not lend itself to data
exploration and iterative analytic s.
2. As a result of t his level of control on the EDW, add itional
local systems may emerge in the form of
departmental wa rehou ses and loca l data marts t hat business
users create to accommodate thei r
need for flexible analysis. These local data marts may not have
the same constraints for secu-
ri ty and structu re as the main EDW and allow users to do some
level of more in-depth analysis.
However, these one-off systems reside in isolation, often are not
synchronized or integrated with
other data stores, and may not be backed up.
3. Once in the data warehouse, data is read by additional
applications across the enterprise for Bl
and reporting purposes. These are high-priority operational
processes getting critical data feeds
from the data warehouses and repositories.
4. At the end of this workfl ow, analysts get data provisioned
for their downstream ana lytics.
Because users generally are not allowed to run custom or
intensive analytics on production
databases, analysts create data extracts from the EDW to
analyze data offline in R or other local
analytical tools. Many times the se tools are lim ited to in-
memory analytics on desktops analyz-
ing sa mples of data, rath er than the entire population of a
dataset. Because the se analyses are
based on data extracts, they reside in a separate location, and
the results of the analysis-and
58. any insights on the quality of the data or anomalies- rarely are
fed back into the main data
repository.
Because new data sources slowly accum ulate in the EDW due
to the rigorous validation and
data struct uring process, data is slow to move into the EDW,
and the data schema is slow to change.
1.2 State of the Practice in Analytics
Departmental data warehouses may have been originally
designed for a specific purpose and set of business
needs, but over time evolved to house more and more data,
some of which may be forced into existing
schemas to enable Bland the creation of OLAP cubes for
analysis and reporting. Although the EDW achieves
the objective of reporting and sometimes the creation of
dashboards, EDWs generally limit the ability of
analysts to iterate on the data in a separate nonproduction
environment where they can conduct in-depth
analytics or perform analysis on unstructured data.
The typical data architectures just described are designed for
storing and processing mission-critical
data, supporting enterprise applications, and enabling corporate
reporting activities. Although reports and
dashboards are still important for organizations, most
traditional data architectures inhibit data exploration
and more sophisticated analysis. Moreover, traditional data
architectures have several additional implica-
tions for data scientists.
59. o High-value data is hard to reach and leverage, and predictive
analytics and data mining activities
are last in line for data. Because the EDWs are designed for
central data management and reporting,
those wanting data for analysis are generally prioritized after
operational processes.
o Data moves in batches from EDW to local analytical tools.
This workflow means that data scientists
are limited to performing in-memory analytics (such as with R,
SAS, SPSS, or Excel), which will restrict
the size of the data sets they can use. As such, analysis may be
subject to constraints of sampling,
which can skew model accuracy.
o Data Science projects will remain isolated and ad hoc, rather
than centrally managed. The implica-
tion of this isolation is that the organization can never harness
the power of advanced analytics in a
scalable way, and Data Science projects will exist as
nonstandard initiatives, which are frequently not
aligned with corporate business goals or strategy.
All these symptoms of the traditional data architecture result in
a slow "time-to-insight" and lower
business impact than could be achieved if the data were more
readily accessible and supported by an envi-
ronment that promoted advanced analytics. As stated earlier,
one solution to this problem is to introduce
analytic sandboxes to enable data scientists to perform
advanced analytics in a controlled and sanctioned
way. Meanwhile, the current Data Warehousing solutions
continue offering reporting and Bl services to
support management and mission-critical operations.
1.2.3 Drivers of Big Data
60. To better understand the market drivers related to Big Data, it is
helpful to first understand some past
history of data stores and the kinds of repositories and tools to
manage these data stores.
As shown in Figure 1-10, in the 1990s the volume of
information was often measured in terabytes.
Most organizations analyzed structured data in rows and
columns and used relational databases and data
warehouses to manage large stores of enterprise information.
The following decade saw a proliferation of
different kinds of data sources-mainly productivity and
publishing tools such as content management
repositories and networked attached storage systems-to manage
this kind of information, and the data
began to increase in size and started to be measured at petabyte
scales. In the 2010s, the information that
organizations try to manage has broadened to include many
other kinds of data. In this era, everyone
and everything is leaving a digital footprint. Figure 1-10 shows
a summary perspective on sources of Big
Data generated by new applications and the scale and growth
rate of the data. These applications, which
generate data volumes that can be measured in exabyte scale,
provide opportunities for new analytics and
driving new value for organizations. The data now comes from
multiple sources, such as these:
INTRODUCTION TO BIG DATA ANALYTICS
• Medical information, such as genomic sequencing and diag
nostic imagi ng
• Photos and video footage uploaded to the World Wide Web
61. • Video surveillance, such as the thousands of video ca meras
spread across a city
• Mobile devices, which provide geospatiallocation data of the
users, as well as metadata about text
messages, phone calls, and application usage on smart phones
• Smart devices, which provide sensor-based collection of
information from smart electric grids, smart
bu ildings, and many other public and ind ustry infrastructures
• Nontraditional IT devices, including the use of radio-freq
uency identifica tion (RFID) reader s, GPS
navigation systems, and seismic processing
MEASURED IN MEASURED IN WILL BE MEASURED IN
TERABYTES PET A BYTES EXABYTES
lTB • 1.000GB lPB • l .OOOTB lEB l .OOOPB
IIEII
You(D
.... ~ .. ·,
A n '' . ~
I b ~
~
~
SMS
w: '-----"
ORACLE =
62. 1.9905 20005 201.05
( RDBMS & DATA (CONTENT & DIGITAL ASSET (NO-SQL
& KEY VALUE)
WAREHOUSE) MANAGEMENT)
FIGURE 1-10 Data evolution and the rise of Big Data sources
Th e Big Data t rend is ge nerating an enorm ous amount of
information from many new sources. This
data deluge requires advanced analytics and new market players
to take adva ntage of these opportunities
and new market dynamics, which wi ll be discussed in the
following section.
1.2.4 Emerging Big Data Ecosystem and a New Approach to
Analytics
Organ izations and data collectors are realizing that the data
they ca n gath er from individuals contain s
intrinsic value and, as a result, a new economy is emerging. As
this new digital economy continues to
1.2 State of the Practice in Analytics
evol ve, the market sees the introduction of data vendors and
data cl eaners that use crowdsourcing (such
as Mechanica l Turk and Ga laxyZoo) to test the outcomes of
machine learning techniques. Other vendors
offer added va lue by repackaging open source tools in a
simpler way and bri nging the tools to market.
Vendors such as Cloudera, Hortonworks, and Pivotal have
63. provid ed thi s value-add for the open source
framework Hadoop.
As the new ecosystem takes shape, there are four main groups
of playe rs within this interconnected
web. These are shown in Figure 1-11.
• Data devices [shown in the (1) section of Figure 1-1 1] and the
"Sensornet" gat her data from multiple
locations and continuously generate new data about th is data.
For each gigabyte of new data cre-
ated, an additional petabyte of data is created about that data.
[2)
• For example, consider someone playing an online video game
through a PC, game console,
or smartphone. In this case, the video game provider captures
data about the skill and levels
attained by the playe r. Intelligent systems monitor and log how
and when the user plays the
game. As a consequence, the game provider can fine -tune the
difficulty of the game,
suggest other related games that would most likely interest the
user, and offer add itional
equipment and enhancements for the character based on the
user's age, gender, and
interests. Th is information may get stored loca lly or uploaded
to the game provider's cloud
to analyze t he gaming habits and opportunities for ups ell and
cross-sell, and identify
archetypica l profiles of specific kinds of users.
• Smartphones provide another rich source of data . In add ition
to messag ing and basic phone
64. usage, they store and transmit data about Internet usage, SMS
usage, and real-time location.
This metadata can be used for analyzing traffic patterns by sca
nning the density of smart-
phones in locations to track the speed of cars or the relative
traffi c congestion on busy
roads. In t his way, GPS devices in ca rs can give drivers real-
time updates an d offer altern ative
routes to avoid traffic delays .
• Retail shopping loyalty cards record not just the amo unt an
individual spends, but the loca-
tions of stores that person visits, the kind s of products
purchased, the stores where goods
are purchased most ofte n, and the combinations of prod ucts
purchased together. Collecting
this data provides insights into shopping and travel habits and
the likelihood of successful
advertiseme nt targeting for certa in types of retail promotions.
• Data collectors [the blue ovals, identified as (2) within Figure
1-1 1] incl ude sa mple entities that
col lect data from the dev ice and users.
• Data resul ts from a cable TV provider tracking the shows a
person wa tches, which TV
channels someone wi ll and will not pay for to watch on
demand, and t he prices someone is
will ing to pay fo r premiu m TV content
• Retail stores tracking the path a customer takes through their
store w hile pushing a shop-
ping cart with an RFID chip so they can gauge which products
get the most foot traffic using
geospatial data co llected from t he RFID chips
65. • Data aggregators (the dark gray ovals in Figure 1-11, marked
as (3)) make sense of the data co llected
from the various entities from the "Senso rN et" or the "Internet
ofThings." These org anizatio ns
compile data from the devices an d usage pattern s collected by
government agencies, retail stores,
INT RODUCTION TO BIG DATA ANALYTIC S
and websites. ln turn, t hey can choose to transform and package
the data as products to sell to list
brokers, who may want to generate marketing lists of people
who may be good targets for specific ad
campaigns.
• Data users and buyers are denoted by (4) in Figu re 1-11.
These groups directly benefit from t he data
collected and aggregated by others within the data value chain.
• Retai l ba nks, acting as a data buyer, may want to know
which customers have the hig hest
likelihood to apply for a second mortgage or a home eq uity line
of credit. To provide inpu t
for this analysis, retai l banks may purchase data from a data
aggregator. This kind of data
may include demograp hic information about people living in
specific locations; people who
appear to have a specific level of debt, yet still have solid credit
scores (or other characteris-
tics such as paying bil ls on time and having savings accounts)
that can be used to infer cred it
worthiness; and those who are sea rching the web for
information about paying off debts or
66. doing home remodeling projects. Obtaining data from these
various sources and aggrega-
tors will enable a more targeted marketing campaign, which
would have been more chal-
lenging before Big Data due to the lack of information or high-
performing technologies.
• Using technologies such as Hadoop to perform natural
language processing on
unstructured, textual data from social media websites, users can
gauge the reaction to
events such as presidential campaigns. People may, for
example, want to determine public
sentiments toward a candidate by analyzing related blogs and
online comments. Similarl y,
data users may want to track and prepare for natural disasters by
identifying which areas
a hurricane affects fi rst and how it moves, based on which
geographic areas are tweeting
about it or discussing it via social med ia.
r:t Data
.::J Devices {'[I t Ptto...r r.r..., l UC)(.K VlOLU l !I ill UO.
AI''
(,.MI
CfitUII CAfW COtPl!UR
RfAO(H
~ .~
Iff [) llOfO MfOICAI
IMoC'oi"G
Law
67. EniCHCefllefll
Data
Users/ Buyers
0
Media
FIGURE 1-11 Emerging Big Data ecosystem
Do live!)'
So Mea
'I If,. [Ill AN [
Privato
Investigators
/ lawyors
1.3 Key Roles for the New Big Data Ecosyst e m
As il lustrated by this emerging Big Data ecosystem, the kinds
of data and the related market dynamics
vary greatly. These data sets ca n include sensor data, text,
structured datasets, and social med ia . With this
in mind, it is worth recall ing that these data sets will not work
wel l within trad itional EDWs, which were
architected to streamline reporting and dashboards and be
centrally managed.lnstead, Big Data problems
and projects require different approaches to succeed.
Analysts need to partner with IT and DBAs to get the data they
68. need within an analytic sandbox. A
typical analytical sandbox contains raw data, agg regated data,
and data with mu ltiple kinds of structure.
The sandbox enables robust exploration of data and requires a
savvy user to leverage and take advantage
of data in the sandbox environment.
1.3 Key Roles for the New Big Data Ecosystem
As explained in the context of the Big Data ecosystem in
Section 1.2.4, new players have emerged to curate,
store, produce, clean, and transact data. In addition, the need
for applying more advanced ana lytica l tech-
niques to increasing ly complex business problems has driven
the emergence of new roles, new technology
platforms, and new analytical methods. This section explores
the new roles that address these needs, and
subsequent chapters explore some of the analytica l methods
and technology platforms.
The Big Data ecosystem demands three ca tegories of roles, as
shown in Figure 1-12. These roles were
described in the McKinsey Global study on Big Data, from May
2011 [1].
Three Key Roles of The New Data Ecosystem
Role
Deep Analytical Talent
Data Savvy Professionals
Technology and Data Enablers
69. Data Scientists
.. Projected U.S. tal ent
gap: 1.40 ,000 to 1.90,000
.. Projected U.S. talent
gap: 1..5 million
Note: RcuresaboYe m~ • projected talent CDP In US In 201.8.
as ihown In McKinsey May 2011 article "81& Data: l he Nut
rront* t ot
Innovation. Competition. and Product~
FIGURE 1-12 Key roles of the new Big Data ecosystem
The first group- Deep Analytical Talent- is technically savvy,
with strong analytical skills. Members pos-
sess a combi nation of skills to handle raw, unstructured data
and to apply complex analytical techniques at
INTRODUCTION TO BIG DATA ANALYTICS
massive scales. This group has advanced training in quantitative
disciplines, such as mathematics, statistics,
and machine learning. To do their jobs, members need access to
a robust analytic sandbox or workspace
where they can perform large-scale analytical data experiments.
Examples of current professions fitting
into this group include statisticians, economists,
mathematicians, and the new role of the Data Scientist.
The McKinsey study forecasts that by the year 2018, the United
States will have a talent gap of 140,000-
70. 190,000 people with deep analytical talent. This does not
represent the number of people needed with
deep analytical talent; rather, this range represents the
difference between what will be available in the
workforce compared with what will be needed. In addition,
these estimates only reflect forecasted talent
shortages in the United States; the number would be much
larger on a global basis.
The second group-Data Savvy Professionals-has less technical
depth but has a basic knowledge of
statistics or machine learning and can define key questions that
can be answered using advanced analytics.
These people tend to have a base knowledge of working with
data, or an appreciation for some of the work
being performed by data scientists and others with deep
analytical talent. Examples of data savvy profes-
sionals include financial analysts, market research analysts, life
scientists, operations managers, and business
and functional managers.
The McKinsey study forecasts the projected U.S. talent gap for
this group to be 1.5 million people by
the year 2018. At a high level, this means for every Data
Scientist profile needed, the gap will be ten times
as large for Data Savvy Professionals. Moving toward becoming
a data savvy professional is a critical step
in broadening the perspective of managers, directors, and
leaders, as this provides an idea of the kinds of
questions that can be solved with data.
The third category of people mentioned in the study is
Technology and Data Enablers. This group
represents people providing technical expertise to support
analytical projects, such as provisioning and
administrating analytical sandboxes, and managing large-scale
71. data architectures that enable widespread
analytics within companies and other organizations. This role
requires skills related to computer engineering,
programming, and database administration.
These three groups must work together closely to solve complex
Big Data challenges. Most organizations
are familiar with people in the latter two groups mentioned, but
the first group, Deep Analytical Talent,
tends to be the newest role for most and the least understood.
For simplicity, this discussion focuses on
the emerging role of the Data Scientist. It describes the kinds of
activities that role performs and provides
a more detailed view of the skills needed to fulfill that role.
There are three recurring sets of activities that data scientists
perform:
o Reframe business challenges as analytics challenges.
Specifically, this is a skill to diagnose busi-
ness problems, consider the core of a given problem, and
determine which kinds of candidate analyt-
ical methods can be applied to solve it. This concept is explored
further in Chapter 2, "Data Analytics
lifecycle."
o Design, implement, and deploy statistical models and data
mining techniques on Big Data. This
set of activities is mainly what people think about when they
consider the role of the Data Scientist:
1.3 Key Roles for the New Big Data Ecosystem
namely, applying complex or advanced ana lytical methods to a
72. variety of busi ness problems using
data. Chapter 3 t hrough Chapter 11 of this book introd uces the
reader to many of the most popular
analytical techniques and tools in this area.
• Develop insights that lead to actionable recommendations. It
is critical to note that applying
advanced methods to data problems does not necessarily drive
new business va lue. Instead, it is
important to learn how to draw insights out of the data and
communicate them effectively. Chapter 12,
"The Endgame, or Putting It All Together;' has a brief overview
of techniques for doing this.
Data scientists are generally thoug ht of as having fi ve mai n
sets of skills and behaviora l characteristics,
as shown in Figure 1-13:
• Quantitative skill: such as mathematics or statistics
• Technical aptitude: namely, software engineering, machine
learning, and programming skills
• Skeptical mind-set and critica l thin king: It is important that
data scientists can examine their work
critica lly rather than in a one-sided way.
• Curious and creative: Data scientists are passionate about data
and finding creative ways to solve
problems and portray information.
• Communicative and collaborative: Data scie ntists must be
able to articulate the business val ue
in a clear way and collaboratively work with other groups,
including project sponsors and key
73. stakeholders.
Quantitative
Technical
Skeptical
Curious and
Creative
Communlcativr
and
CDDaborati~
fiGURE 1 Profile of a Data Scientist
INTRODUCTION TO BIG DATA ANALYTICS
Data scientists are generally comfortable using this blend of
skills to acquire, manage, analyze, and
visualize data and tell compelling stories about it. The next
section includes examples of what Data Science
teams have created to drive new value or innovation with Big
Data.
1.4 Examples of Big Data Analytics
After describing the emerging Big Data ecosystem and new
roles needed to support its growth, this section
provides three examples of Big Data Analytics in different
areas: retail, IT infrastructure, and social media.
74. As mentioned earlier, Big Data presents many opportunities to
improve sa les and marketing ana lytics.
An example of this is the U.S. retailer Target. Cha rles Duhigg's
book The Power of Habit [4] discusses how
Target used Big Data and advanced analytical methods to drive
new revenue. After analyzing consumer-
purchasing behavior, Target's statisticians determin ed that the
retailer made a great deal of money from
three main life-event situations.
• Marriage, when people tend to buy many new products
• Divorce, when people buy new products and change their
spending habits
• Pregnancy, when people have many new things to buy and
have an urgency to buy t hem
Target determined that the most lucrative of these life-events is
the thi rd situation: pregnancy. Using
data collected from shoppers, Ta rget was able to identify this
fac t and predict which of its shoppers were
pregnant. In one case, Target knew a female shopper was
pregnant even before her family knew [5]. This
kind of knowledge allowed Target to offer specifi c coupons and
incentives to thei r pregnant shoppers. In
fact, Target could not only determine if a shopper was pregnant,
but in which month of pregnancy a shop-
per may be. This enabled Target to manage its inventory, knowi
ng that there would be demand for specific
75. products and it wou ld likely vary by month over the com ing
nine- to ten- month cycl es.
Hadoop [6] represents another example of Big Data innovation
on the IT infra structure. Apache Hadoop
is an open source framework that allows companies to process
vast amounts of information in a highly paral-
lelized way. Hadoop represents a specific implementation of t
he MapReduce paradigm and was designed
by Doug Cutting and Mike Cafa rel la in 2005 to use data with
varying structu res. It is an ideal technical
framework for many Big Data projects, which rely on large or
unwieldy data set s with unconventiona l data
structures. One of the main benefits of Hadoop is that it
employs a distributed file system, meaning it can
use a distributed cluster of servers and commodity hardware to
process larg e amounts of data. Some of
the most co mmon examples of Hadoop imp lementations are in
the social med ia space, where Hadoop
ca n manage transactions, give textual updates, and develop
social graphs among millions of users. Twitter
and Facebook generate massive amounts of unstructured data
and use Hadoop and its ecosystem of tools
to manage this hig h volu me. Hadoop and its ecosystem are
covered in Chapter 10, "Adva nced Ana lytics-
Technology and Tools: MapReduce and Hadoop."
Finally, social media represents a tremendous opportunity to
leverage social and professional interac-
tions to derive new insights. Linked In exemplifies a company
in which data itself is the product. Early on,
76. Linkedln founder Reid Hoffman saw the opportunity to create a
social network for working professionals.
Exercises
As of 2014, Linkedln has more than 250 million user accounts
and has added many additional features and
data-related products, such as recruiting, job seeker too ls,
advertising, and lnMa ps, whic h show a social
graph of a user's professional network. Figure 1-14 is an
example of an In Map visualization that enables
a Linked In user to get a broader view of the interconnectedness
of his contacts and understand how he
knows most of them .
fiGURE 1-14 Data visualization of a user's social network using
lnMaps
Summary
Big Data comes from myriad sources, including social media,
sensors, the Internet ofThings, video surveil-
lance, and many sources of data that may not have been
considered data even a few years ago. As businesses
struggle to keep up with changing market requirements, some
companies are finding creative ways to apply
Big Data to their growing business needs and increasing ly
complex problems. As organizations evolve
their processes and see the opportunities that Big Data can
provide, they try to move beyond t raditional Bl
77. activities, such as using data to populate reports and
dashboards, and move toward Data Science- driven
projects that attempt to answer more open-ended and complex
questions.
However, exploiting the opportunities that Big Data presents
requires new data architectures, includ -
ing analytic sandboxes, new ways of working, and people with
new skill sets. These drivers are causing
organizations to set up analytic sandboxes and build Data
Science teams. Although some organizations are
fortunate to have data scientists, most are not, because there is a
growing talent gap that makes finding
and hi ring data scientists in a timely man ner difficult. Still,
organizations such as those in web retail, health
care, genomics, new IT infrast ructures, and social media are
beginning to take advantage of Big Data and
apply it in creati ve and novel ways.
Exercises
1. What are the three characteristics of Big Data, and what are
the main considerations in processing Big
Data?
2 . What is an analytic sa ndbox, and why is it important?
3. Explain the differences between Bland Data Science.
4 . Describe the challenges of the current analytical architecture
for data scientists.
5. What are the key skill sets and behavioral characteristics of a
data scientist?
78. INTRODUCTION TO BIG DATA ANALYTICS
Bibliography
[1] C. B. B. D. Manyika, "Big Data: The Next Frontier for
Innovation, Competition, and Productivity,"
McKinsey Globa l Institute, 2011 .
[2] D. R. John Ga ntz, "The Digital Universe in 2020: Big Data,
Bigger Digital Shadows, and Biggest
Growth in the Far East," IDC, 2013.
[3] http: I l www. willisresilience . coml emc-data l ab [Online].
[4] C. Duhigg, The Power of Habit: Why We Do What We Do in
Life and Business, New York: Random
House, 2012.
[5] K. Hil l, "How Target Figured Out a Teen Girl Was Pregnant
Before Her Father Did," Forb es, February
2012.
[6] http: I l hadoop. apache . org [Online].
DATA ANALYTICS LIFECYCLE
Data science projects differ from most traditional Business
Intelligence projects and many data ana lysis
projects in that data science projects are more exploratory in
nature. For t his reason, it is critical to have a