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1. How do you describe the importance of data in analyt-ics? Can we think of analytics without data? Explain. 2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? 3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data? 4. What are the most common metrics that make for analytics-ready data? 5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to edu-cation, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations. .
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1. How do you describe the importance of data in analytics Can we t.docx
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
.
1. Discussion (Chapter 3) Why are the originalraw data not readily.docx
1. Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.
2. How do you describe the importance of data in analyt-ics? Can we think of analytics without data? Explain.
3. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
4. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
5. What are the most common metrics that make for analytics-ready data
6. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to edu-cation, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
.
Discussion 1. How do you describe the importance of data in ana.docx
Discussion:
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Exercise:
12: Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education,
climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site.
Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
Be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
.
1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analytics?
Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business
analytics, what are the main inputs and outputs to the
analytics continuum?
3. Where do the data for business analytics come from?
What are the sources and the nature of those incoming
data?
4. What are the most common metrics that make for
analytics-ready data?
Exercise Question:
Go to data.gov—a U.S. government–sponsored data
portal that has a very large number of data sets on a
wide variety of topics ranging from healthcare to education,
climate to public safety. Pick a topic that you
are most passionate about. Go through the topic- specific
information and explanation provided on the site.
Explore the possibilities of downloading the data, and
use your favorite data visualization tool to create your
own meaningful information and visualizations.
Discussion :
Create a discussion thread (with your name) and answer the following question:
Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.
Note: The first post should be made by Wednesday 11:59 p.m., EST. I am looking for active engagement in the discussion. Please engage early and often.
Your response should be 250-300 words. Respond to two postings provided by your classmates.
There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations
.
Discussion Question #1 How do you describe the importance of .docx
Discussion Question #1
: How do you describe the importance of data in analytics? Can we think about analytics without data? Explain.
Discussion Question #2
: Considering the new and broad definition of business analytics, what are the main inputs and outputs to analytics continuum?
Discussion Question #3
: Where do the data for business analytics come from? What are the sources and nature of those incoming data?
Discussion Question #4
: What are the most common metrics that make for analytics-ready data?
Exercise #12:
Go to data.gov – a US government-sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data and use your favorite data visualization tool to create your own meaningful information and visualization. Show your visualization on your assignment submission.
.
When submitting work, be sure to include an APA cover page and inclu.docx
When submitting work, be sure to include an APA cover page and include APA formatted references (and APA in-text citations) to support the work this week.
All work must be original (not copied from any source).
Discussion
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Excersise
5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
.
Practical and Actionable Threat Intelligence Collection
A great deal of the existing human rights reporting and analysis aggregate and strip away contextual information in order to produce “quantified knowledge” that is technically reliable and useful for governmental decision making. The results produced often end up too delayed, partial, distorted, and misleading to be used by local actors and human rights defenders to directly respond to the threats that they face. Those who could benefit most from the human rights knowledge being collected and shared in the digital world are those that existing repositories of information serve the least.
In this presentation I will provide concrete guidance on approaches for adopting data-rich, practical, and actionable threat information collection. In this content heavy 1.5 hour talk I will discuss a range of tools and techniques for seeking out sources of actionable information, distinguishing valuable information from useless but interesting information, and streamlining your information collection and analysis process to allow you to focus on your real work.
This talk WON’T be focused on collecting or sharing threat intelligence and/or human rights research aimed at evidence creation or changing the public dialogue. It WILL be focused on helping you identify, collect, and use publicly available sources of information to respond to your changing threat landscape.
1. How do you describe the importance of data in analytics Can .docx
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
5. Define data mining. Why are there many names and definitions for data mining?
6. What are the main reasons for the recent popularity of data mining?
7. Discuss what an organization should consider before making a decision to purchase data mining software.
8. Distinguish data mining from other analytical tools and techniques.
9. Discuss the main data mining methods. What are the fundamental differences among them? ?
.
Recommended
1. How do you describe the importance of data in analytics Can we t.docx
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
.
1. Discussion (Chapter 3) Why are the originalraw data not readily.docx
1. Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.
2. How do you describe the importance of data in analyt-ics? Can we think of analytics without data? Explain.
3. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
4. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
5. What are the most common metrics that make for analytics-ready data
6. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to edu-cation, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
.
Discussion 1. How do you describe the importance of data in ana.docx
Discussion:
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Exercise:
12: Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education,
climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site.
Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
Be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
.
1. How do you describe the importance of data in analyticsC.docx
1. How do you describe the importance of data in analytics?
Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business
analytics, what are the main inputs and outputs to the
analytics continuum?
3. Where do the data for business analytics come from?
What are the sources and the nature of those incoming
data?
4. What are the most common metrics that make for
analytics-ready data?
Exercise Question:
Go to data.gov—a U.S. government–sponsored data
portal that has a very large number of data sets on a
wide variety of topics ranging from healthcare to education,
climate to public safety. Pick a topic that you
are most passionate about. Go through the topic- specific
information and explanation provided on the site.
Explore the possibilities of downloading the data, and
use your favorite data visualization tool to create your
own meaningful information and visualizations.
Discussion :
Create a discussion thread (with your name) and answer the following question:
Discussion (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.
Note: The first post should be made by Wednesday 11:59 p.m., EST. I am looking for active engagement in the discussion. Please engage early and often.
Your response should be 250-300 words. Respond to two postings provided by your classmates.
There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations
.
Discussion Question #1 How do you describe the importance of .docx
Discussion Question #1
: How do you describe the importance of data in analytics? Can we think about analytics without data? Explain.
Discussion Question #2
: Considering the new and broad definition of business analytics, what are the main inputs and outputs to analytics continuum?
Discussion Question #3
: Where do the data for business analytics come from? What are the sources and nature of those incoming data?
Discussion Question #4
: What are the most common metrics that make for analytics-ready data?
Exercise #12:
Go to data.gov – a US government-sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data and use your favorite data visualization tool to create your own meaningful information and visualization. Show your visualization on your assignment submission.
.
When submitting work, be sure to include an APA cover page and inclu.docx
When submitting work, be sure to include an APA cover page and include APA formatted references (and APA in-text citations) to support the work this week.
All work must be original (not copied from any source).
Discussion
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Excersise
5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
.
Practical and Actionable Threat Intelligence Collection
A great deal of the existing human rights reporting and analysis aggregate and strip away contextual information in order to produce “quantified knowledge” that is technically reliable and useful for governmental decision making. The results produced often end up too delayed, partial, distorted, and misleading to be used by local actors and human rights defenders to directly respond to the threats that they face. Those who could benefit most from the human rights knowledge being collected and shared in the digital world are those that existing repositories of information serve the least.
In this presentation I will provide concrete guidance on approaches for adopting data-rich, practical, and actionable threat information collection. In this content heavy 1.5 hour talk I will discuss a range of tools and techniques for seeking out sources of actionable information, distinguishing valuable information from useless but interesting information, and streamlining your information collection and analysis process to allow you to focus on your real work.
This talk WON’T be focused on collecting or sharing threat intelligence and/or human rights research aimed at evidence creation or changing the public dialogue. It WILL be focused on helping you identify, collect, and use publicly available sources of information to respond to your changing threat landscape.
1. How do you describe the importance of data in analytics Can .docx
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
5. Define data mining. Why are there many names and definitions for data mining?
6. What are the main reasons for the recent popularity of data mining?
7. Discuss what an organization should consider before making a decision to purchase data mining software.
8. Distinguish data mining from other analytical tools and techniques.
9. Discuss the main data mining methods. What are the fundamental differences among them? ?
.
Content analytics
The document discusses content analytics and its ability to extract insights from unstructured data. It defines content analytics as applying business intelligence practices to "Big Content" like emails, documents, social media posts, etc. Content analytics solutions can analyze unstructured content, deliver better business understanding and visibility through patterns, trends, and correlations. It brings the power of business intelligence to an organization's entire information, not just structured data. The document also discusses key benefits, challenges, industries where content analytics is used, and leading providers in the market.
Complete the following assignment in one MS word document Chapt.docx
Complete the following assignment in one MS word document:
Chapter 3 –discussion question #1-4 & exercise 12
When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
All work must be original (not copied from any source).
1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.
2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Exercise-12:
Go to data.gov—a U.S. government-sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
.
Five creative search solutions using text analytics
Five creative search solutions using text analytics are presented:
1) Analyzing contracts to extract vendor, product, and spending data for a government agency.
2) Building a company database from articles to provide consolidated search results for subscribers.
3) Identifying diseases and treatments from medical articles to summarize recommended practices.
4) Recognizing authors, locations, and institutions from publications to build a knowledge base on medical research locations.
5) Classifying scientific article topics using statistics to improve filtering and understanding on a government information site.
Internet Intelligence Approach
The document outlines a 5-step approach to internet intelligence:
1. Define the question by asking the client what they want or need to know and placing the question in context.
2. Evaluate potential sources based on relevancy, number of users, added services, and price.
3. Select appropriate sources like search engines, publications, or web 2.0 based on the question.
4. Find answers through keyword selection, syntax, and timeframe to provide timely intelligence.
5. Produce intelligence by matching answers to the question and supporting decisions with facts.
Evidence Based Healthcare Design
This document discusses case based reasoning and its application in data mining and databases. Case based reasoning involves solving current problems by adapting solutions from similar past problems. The author defines case based reasoning and describes the typical four step structure of a case base database used in case based reasoning: 1) retrieval of similar past cases, 2) reuse of solutions from these similar cases, 3) revision of these solutions if needed, and 4) retention of the revised solutions as new cases. The article examines how case based reasoning, data mining techniques, and databases can be used together across various industries.
Predictive analytics: hot and getting hotter
Fern Halper is an analyst who has observed growing interest in predictive analytics from companies seeking competitive advantages and deeper customer insights. While the technology has existed for decades, businesses are now recognizing its value. Vendors are developing easier to use tools in response, hoping both statisticians and regular business users can build basic models. Open source is also becoming more important, with ecosystems of support emerging around languages like R.
Beginners_s_Guide_Data_Analytics_1661051664.pdf
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
Unveiling the Power of Data Analytics Transforming Insights into Action.pdf
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
how you can use data analytics
The document discusses how government agencies can use data analytics to address challenges and improve services. It provides examples of how the State of Indiana is using data analytics to combat the opioid epidemic, how the US Patent and Trademark Office made patent data more searchable, and how the Government Accountability Office tracks Medicare fraud. The document also discusses descriptive, diagnostic, predictive, and prescriptive analytics and how real-time and forensic analyses drive value. Overall, the document promotes how data analytics can help government agencies deliver better outcomes.
Data Collection Tool Used For Information About Individuals
The document discusses surveys as a data collection tool used to gather information about individuals. Surveys can be conducted in various ways such as printed questionnaires, telephone interviews, mail, in-person interviews, online, etc. However, standardized procedures are used to ensure every participant is asked the same questions in the same way to make the results reliable and generalizable. The document then discusses some questions and concerns about survey length, question types, and methodology.
How Artificial Intelligence Can Overcome Healthcare Data Security Challenges ...
As healthcare organizations today face more security threats than ever, artificial intelligence (AI) combined with human judgment is emerging as the perfect pair to improve healthcare data security. Together, they power a highly accurate privacy analytics model that allows organizations to review access points to patient data and detect when a system’s EHR is potentially exposed to a privacy violation, attack, or breach. With specific techniques, including supervised and unsupervised machine learning and transparent AI methods, health systems can advance toward more predictive, analytics-based, collaborative privacy analytics infrastructures that safeguard patient privacy.
Chapter 11 Survey DataOverview Identify the different ty.docx
Chapter 11: Survey DataOverview: Identify the different types of questions used in survey researchBe aware of the many threats to validity inherent to the survey research processIdentify how the sampling strategy should be represented in the resulting dataIdentify factors to reduce sample attritionList benefits and weakness of each type of survey medium
Why SurveySurvey research is primarily used to gather data From either participants in a systemOr consumers of a program / policy (citizens) Could be consumers of a productThat either do not exist or are available in another form
General Surveying RulesAvoid Value-Laden Questions“How should the City of Ventura best control & monitor lawn watering”? Answers are three various policiesNo Double-Barreled Questions“Do you agree with the following statement: The Thousand Oaks Police Department Officers are both well trained and well equipped”. Yes/NO Watch Out for Loaded Language“How often in one week do you use illegal drugs”? Avoid Jargon“Given existing legislation which fish and invertebrate barrier mechanisms do you believe is most useful to reduce the spread of Dreissena polymorpha in existing finfish onshore aquaculture sites”?
Types of Data Typically Collected by SurveyIndicator VariablesDemographic, geographic, organizational affiliation Open-ended QuestionsNo structure on the answer “Why does…”Likert-type QuestionsProvide a statementAsk for their agreement or disagreement on scale 1-51 = strongly agree2 = agree3 = neutral4 = disagree5 = strongly disagree
More Types of Data Typically Collected by SurveyGrouped Data Questions (categories are provided)Example, when asking income or age$15,000 – 30,000 / 18-25 years old$30,000 – 45,000 / 26 - 40Single and Multiple Response Questions“Check a box”“Mark all that apply” Continuous Data QuestionsBest, most versatile, kind of data to haveUsually hard to collect with a survey (age is easy example)
Techniques for Data Collection Using SurveysEmbedded experimentsEmbedding “tests” within your surveyValidity and Bias Tests You can increase validity by:Using established questions Use multiple measure of your “construct” (internal validity)Beta-test the question
Important Issues to Consider When Employing SurveysQuestion Location“Hard” questions later in the surveySurvey MediumsIn-person: very time consuming Telephone: once the standard, still done, less so Mail: expensive Electronic: becoming more and more common IncentivesGift cards “bribes”
Strengths of Survey DataSpeed of data captureCapturing data otherwise not availableGenerating “bridging” data to enhance the value of existing data
Weaknesses of Survey Data CollectionResponse Rates25% still ok for validityItem NonresponseRespondents “skip” a question AttritionIf you are collecting surveys overtime (longitudinal data), sometimes people move, die, etc. Test Effect
Chapter 12:
Coding and Data DisplayOverview: Basics of Data AnalysisCodingApproaches to D ...
Data science and data analytics major similarities and distinctions (1)
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
CSIA 360 PAPER #1 CAN WE ENSURE THAT OPEN DATA IS USEFUL AND SECURE? (UMUC)
The document provides a scenario for a cybersecurity consulting firm tasked with writing a white paper for a federal agency on ensuring open data is both useful and secure. The agency currently distributes valuable datasets through paid DVDs but plans to provide open data through a government website, raising security concerns from customers. The white paper should address: 1) An introduction to open data policies and benefits; 2) Examples of how open data is used; 3) Security issues regarding confidentiality, integrity, availability, authenticity and non-repudiation and current mitigations; and 4) Best practices for ensuring security of open data. Research is provided on relevant policies, uses of open data, security issues and potential solutions, and best practices to
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An Introduction to Monitoring and Evaluation of Healthcare Projects. Monitoring and Evaluation is an integral component for the success of any donor-funded project as it provides accountability and well-informed decisions through the use of data and plan that guides implementation
Copy of OSTP RFI on Big Data and Privacy
This document was originally published by OSTP here:
http://www.ofr.gov/(S(rfkilxaktjiadgtykwxaljqm))/OFRUpload/OFRData/2014-04660_PI.pdf
The original link is now broken, so this copy is provided for the transparency and commentary.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docx
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
Unleashing government’s ‘innovation mojo’ an interview with the us chief tec...
1) Todd Park explains how as US Chief Technology Officer, he aims to accelerate innovation in government by replicating the Health Data Initiative model in other sectors like energy and education. This involves making more government data publicly available and usable.
2) Park also wants to promote a "lean startup" approach within government agencies, with small cross-functional teams rapidly prototyping solutions over short iterations rather than long planning cycles.
3) To demonstrate this approach, Park launched the Presidential Innovation Fellows program pairing entrepreneurs with government innovators on high-profile projects over 6 months to signal this as an accepted way of working.
Big Data & Business Analytics: Understanding the Marketspace
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
Fundamentals of Data Analytics Outline
The document provides an outline for a training on fundamentals of data analytics. It introduces the presenter, Daniel Meyer, who has over 20 years of experience in higher education, business process outsourcing, and financial services. The agenda covers topics such as descriptive, predictive, and prescriptive analytics, finding and using data, and driving decisions with data analytics. It also discusses challenges around big data and unstructured data, and the importance of business intelligence, data visualization, and data-driven decision making.
1. Report contentThe report should demonstrate your understa.docx
1. Report content
The report should demonstrate your understanding of good project management and health and safety management as appropriate within the context of your chosen project and event.
The report will present the context/background of the chosen project, describe the project, and present student’s critical reflection and thoughts on the management of one particular event/issue of project. The impacts of the event/issue on (1) people, (2) cost, (3) time, (4) health and safety, (5) sustainability, and (6) Ethics will be explored. Using the theory and tools presented in the lectures across the module as well as their own independent research, students should suggest and discuss solutions to (1) overcome the challenges and manage the risks associated with the event/issue, and (2) improve the efficiency, sustainability and ethics of the management of the event/issue.
Appendices and references must be used to demonstrate study that has been undertaken and to provide sources for points made in the body of the report. This will include copies of any individual or group student work undertaken during the module.
The student should refer to the learning materials and readings provided across the module, but are also recommended to give appropriate regard to any additional useful material available online in terms of theory and practice.
.
1. Research the assessment process for ELL students in your state. W.docx
1. Research the assessment process for ELL students in your state. What is the process your district goes through to properly identify students for ESL program placement?
2. Planning for effective instruction is the key to academic success for students. Using data to inform instruction is a regular process. Discuss how teachers can use longitudinal data along with other formative classroom assessments to design effective instruction.
200-300
.
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When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.
All work must be original (not copied from any source).
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2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?
3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?
4. What are the most common metrics that make for analytics-ready data?
Exercise-12:
Go to data.gov—a U.S. government-sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic-specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.
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Chapter 11: Survey DataOverview: Identify the different types of questions used in survey researchBe aware of the many threats to validity inherent to the survey research processIdentify how the sampling strategy should be represented in the resulting dataIdentify factors to reduce sample attritionList benefits and weakness of each type of survey medium
Why SurveySurvey research is primarily used to gather data From either participants in a systemOr consumers of a program / policy (citizens) Could be consumers of a productThat either do not exist or are available in another form
General Surveying RulesAvoid Value-Laden Questions“How should the City of Ventura best control & monitor lawn watering”? Answers are three various policiesNo Double-Barreled Questions“Do you agree with the following statement: The Thousand Oaks Police Department Officers are both well trained and well equipped”. Yes/NO Watch Out for Loaded Language“How often in one week do you use illegal drugs”? Avoid Jargon“Given existing legislation which fish and invertebrate barrier mechanisms do you believe is most useful to reduce the spread of Dreissena polymorpha in existing finfish onshore aquaculture sites”?
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Important Issues to Consider When Employing SurveysQuestion Location“Hard” questions later in the surveySurvey MediumsIn-person: very time consuming Telephone: once the standard, still done, less so Mail: expensive Electronic: becoming more and more common IncentivesGift cards “bribes”
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Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
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The report should demonstrate your understanding of good project management and health and safety management as appropriate within the context of your chosen project and event.
The report will present the context/background of the chosen project, describe the project, and present student’s critical reflection and thoughts on the management of one particular event/issue of project. The impacts of the event/issue on (1) people, (2) cost, (3) time, (4) health and safety, (5) sustainability, and (6) Ethics will be explored. Using the theory and tools presented in the lectures across the module as well as their own independent research, students should suggest and discuss solutions to (1) overcome the challenges and manage the risks associated with the event/issue, and (2) improve the efficiency, sustainability and ethics of the management of the event/issue.
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Feedback on changes needed to the Risk Assessment Plan
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3. In Book I, Thrasymachus’ ironic argument ad hominem is :
A. Socrates needs a wet-nurse.B. Socrates is ugly.C. Socrates should put himself to bed.D. Socrates should not have gone to last night’s banquet.
4. In Book II, Glaucon tells the myth of a ring, the point of which is to illustrate:
A. That we prize material goods above all else.B. That the rich decide what is just and unjust.C. That anyone will commit injustice when they can get away without punishment.D. That myth-telling is essential to philosophy.
5. In Book III, Socrates suggests the city adopt a noble lie, according to which:
A. There are three sorts of beings: humans, angels, and demons.B. Into our natures were mixed one of three metals: gold, silver, or bronze. C. Everyone will live virtuously in a just city.D. The just city lasts forever.
.
1. Objective Learn why and how to develop a plan that encompasses a.docx
1. Objective: Learn why and how to develop a plan that encompasses all components of a security system.
Use the information found at http://nces.ed.gov/pubs98/safetech/chapter5.asp
to research how determining possible physical threats may affect the choice of physical security countermeasures while planning new or updated security systems.
2. Objective: Determine the placement of physical barriers in integration with other components of the security system.
Research the different types of physical barriers and how they fit the needs of different types of facilities. Use the information found at
http://www.fs.fed.us/t-d/phys_sec/deter/index.htm.
APA Format , references & citations.
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1. Open the attached Excel Assignment.xlsx” file and name it LastN.docx
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.
1. must be a research article from either pubmed or google scholar..docx
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2. the article you select must have an abstract, introduction/ background, materials &methods, results, conclusion
3. summarize the article you selected
4. no plagiarism
5. must include reference
.
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