A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
Sdal air health and social development (jan. 27, 2014) finalkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
University of Virginia School of Data SciencePhilip Bourne
March 6, 2020 presentation to the University of Virginia Board of Visitors on the prior work and development of the School of Data Science over the next several years.
In this paper we have penetrate an era of Big Data. Through better analysis of the large volumes of data that are becoming available, there is the potential for making faster advances in many scientific disciplines and improving the profitability and success of many enterprises. However, many technical challenges described in this paper must be addressed before this potential can be realized fully. The challenges include not just the obvious issues of scale, but also heterogeneity, lack of structure, error-handling, privacy, timeliness, provenance, and visualization, at all stages of the analysis pipeline from data acquisition to result interpretation.
PAARL's 1st Marina G. Dayrit Lecture Series held at UP's Melchor Hall, 5F, Proctor & Gamble Audiovisual Hall, College of Engineering, on 3 March 2017, with Albert Anthony D. Gavino of Smart Communications Inc. as resource speaker on the topic "Using Big Data to Enhance Library Services"
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
Sdal air health and social development (jan. 27, 2014) finalkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
University of Virginia School of Data SciencePhilip Bourne
March 6, 2020 presentation to the University of Virginia Board of Visitors on the prior work and development of the School of Data Science over the next several years.
In this paper we have penetrate an era of Big Data. Through better analysis of the large volumes of data that are becoming available, there is the potential for making faster advances in many scientific disciplines and improving the profitability and success of many enterprises. However, many technical challenges described in this paper must be addressed before this potential can be realized fully. The challenges include not just the obvious issues of scale, but also heterogeneity, lack of structure, error-handling, privacy, timeliness, provenance, and visualization, at all stages of the analysis pipeline from data acquisition to result interpretation.
PAARL's 1st Marina G. Dayrit Lecture Series held at UP's Melchor Hall, 5F, Proctor & Gamble Audiovisual Hall, College of Engineering, on 3 March 2017, with Albert Anthony D. Gavino of Smart Communications Inc. as resource speaker on the topic "Using Big Data to Enhance Library Services"
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
Data Science - An emerging Stream of Science with its Spreading Reach & ImpactDr. Sunil Kr. Pandey
This is my presentation on the Topic "Data Science - An emerging Stream of Science with its Spreading Reach & Impact". I have compiled and collected different statistics and data from different sources. This may be useful for students and those who might be interested in this field of Study.
phd research proposal should be written in such a way that it makes a positive and powerful first impression about your potential to become a good researcher and allows the university to assess whether you are a good match for the mentors or supervisors and their areas of research expertise.
Check out the scope for future research proposal topics in big data 2023 - https://rb.gy/6yoy0
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
an introductory course for Librarians on using Big Data and Data Science applications on the field of Library Science. The course is a 2 hour course module for basic fundamentals of applying DS work.
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.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
1. Data Science Applications & Use
Cases
Instructor: Ekpe Okorafor
1. Accenture – Big Data Academy
2. Computer Science African University of Science &
Technology
2. Objectives
Objectives
• Understand Big Data Challenges
• What exactly is Data Science and what do Data
Scientists do
• Data Science contrasted with other disciplines
• Case Study & Use Cases
2
3. Outline
• Big Data & Challenges
• What is Data Science
• Data Science & Academia
• Data Science & Others
• Case Studies
• Essential points
• Conclusion
3
4. Data All Around
• Lots of data is being collected
and warehoused
– Scientific Experiments
– Internet of Things
– Web data, e-commerce
– Financial transactions, bank/credit transactions
– Online trading and purchasing
– Social Network
– ……many more!
4
5. Big Data
• Big Data are data sets so large or so complex that traditional methods
of storing, accessing, and analyzing their breakdown are too
expensive. However, there is a lot of potential value hidden in this
data, so organizations are eager to harness it to drive innovation and
competitive advantage.
• Big Data technologies and approaches are used to drive value out of
data rich environments in ways that traditional analytics tools and
methods cannot.
5
6. What To Do With These Data?
6
• Aggregation and Statistics
– Data warehousing and OLAP
• Indexing, Searching, and Querying
– Keyword based search
– Pattern matching (XML/RDF)
• Knowledge discovery
– Data Mining
– Statistical Modeling
• Data Driven
– Predictive Analytics
– Deep Learning
7. Big Data & Data Science
7
• “… the sexy job in the next 10 years will be
statisticians,” Hal Varian, Google Chief Economist
• The U.S. will need 140,000-190,000 predictive
analysts and 1.5 million managers/analysts by 2018.
McKinsey Global Institute’s June 2011
• New Data Science institutes being created or
repurposed – NYU, Columbia, Washington, UCB,...
• New degree programs, courses, boot-camps:
– e.g., at Berkeley: Stats, I-School, CS, Astronomy…
– One proposal (elsewhere) for an MS in “Big Data Science”
– Plans for Data Science Stream at AUST
– RDA-CODATA School of Research Data Science
8. What is Data Science?
8
• Some definitions link computational, statistical, and
substantive expertise.
9. What is Data Science?
9
• Other definitions focus more on technical skills alone.
10. What is Data Science?
10
• An area that manages, manipulates,
extracts, and interprets knowledge from
tremendous amount of data
• Data science (DS) is a multidisciplinary field
of study with goal to address the challenges
in big data
• Data science principles apply to all data –
big and small
11. What is Data Science?
11
• Theories and techniques from many fields and
disciplines are used to investigate and analyze a
large amount of data to help decision makers in
many industries such as science, engineering,
economics, politics, finance, and education
– Computer Science
• Pattern recognition, visualization, data warehousing, High
performance computing, Databases, AI
– Mathematics
• Mathematical Modeling
– Statistics
• Statistical and Stochastic modeling, Probability.
12. Data Science Vs Analysis Vs Software
Delivery
12
Component Traditional Analysis Traditional Software
Delivery
Data Science
Tools SAS, R, Excel, SQL, in-
house tools
Java, source control, Linux,
continuous integration, unit
testing, bug reports and
project management
R, Java, scientific Python libraries,
Excel, SQL, Hadoop, Hive, Pig,
Mahout and other machine learning
libraries, github for source control
and issue management
Analytical
Methods
Regressions,
classifications,
measuring prediction
accuracy and
coverage/error,
sampling
N/A Classification, clustering, similarity
detection, recommenders,
unsupervised and supervised
learning, small- and large-scale
computations, measuring prediction
accuracy and coverage/error
Team
Structure
Statisticians,
Mathematicians,
Scientists
Developers, Project
Managers, Systems
Engineers
Mathematicians, Statisticians,
Scientists, Developers, Systems
Engineers
Time Frame Either:
• Usually on-going
research and
discovery within a
team in the
organization
Or:
• Specific project to
determine answers
Regular software release
cycle, continuous delivery, etc.
Either:
• Discovery/learning phase leading
to product development
Or:
• On-going research and product
invention/improvement
13. Contrast: Scientific Computing
13
Scientific Modeling
Physics-based models
Problem-Structured
Mostly deterministic, precise
Run on Supercomputer or High-end
Computing Cluster
Supernova
Not
Image General purpose classifier
Data-Driven Approach
General inference engine replaces model
Structure not related to problem
Statistical models handle true randomness,
and un-modeled complexity.
Run on cheaper computer Clusters (EC2)
Nugent group / C3 LBL
14. Contrast: Machine Learning
14
Machine Learning
Develop new (individual) models
Prove mathematical properties of
models
Improve/validate on a few, relatively
clean, small datasets
Publish a paper
Data Science
Explore many models, build and tune
hybrids
Understand empirical properties of
models
Develop/use tools that can handle
massive datasets
Take action!
15. Contrast: Data Engineering
15
Data Science Data Engineering
Approach Scientific (Exploration) Engineering (Development)
Problems Unbounded Bounded
Path to Solution Iterative, exploratory,
nonlinear
Mostly linear
Education More is better (PhD’s
common)
BS and/or self-trained
Presentation Skills Important Not as important
Research
Experience
Important Not as important
Programming
Skills
Not as important Important
Data Skills Important Important
16. Data Science & Academia
16
• In the words of Alex Szalay, these sorts of researchers must be "Pi-shaped" as
opposed to the more traditional "T-shaped" researcher. In Szalay's view, a
classic PhD program generates T-shaped researchers: scientists with wide-
but-shallow general knowledge, but deep skill and expertise in one particular
area. The new breed of scientific researchers, the data scientists, must be Pi-
shaped: that is, they maintain the same wide breadth, but push deeper both in
their own subject area and in the statistical or computational methods that help
drive modern research:
17. Data Science & Academia
17
• In a post by Jake Vanderplas in 2014 related to SciFoo discussion on:
Academia and Data Science, the following questions below were
discussed.
• I encourage you to develop your own thoughts on them and come up
with your assessment
– Where does Data Science fit within the current structure of the
university & research institutions?
– What is it that academic data scientists want from their career?
How can academia offer that?
– What drivers might shift academia toward recognizing & rewarding
data scientists in domain fields?
– Recognizing that graduates will go on to work in both academia
and industry, how do we best prepare them for success in both
worlds?
18. Data Science Applications
18
Business Health Care Urban Leaving
Summary From car design to
insurance to pizza delivery,
businesses are using data
science to optimize their
operations and better meet
their customers’
expectations.
Tomorrow’s healthcare may
look more efficient thanks to
things like electronic health
records. It also may look a lot
more effective. Reduced
readmissions, better care, and
earlier detection are on the
horizon.
For the first time in human
history, more people live in
cities than in suburban or
rural areas. An emerging field
called “urban informatics”
combines data science with
the unique challenges facing
the world’s growing cities
What is
happening?
Two-Way Street for the
Ford Focus Electric Car
Reducing Hospital
Readmissions
Taking on Megacity Traffic
Better Fraud Detection
Boosts Customer
Satisfaction
Better Point-of-Care Decisions Fighting Crime with Data
"predictive policing"
E-Commerce Insights:
Domino’s Secret Sauce
What is possible Using Social Data to
Select Successful Retail
Locations
.
Medical Exams by Bathroom
Mirrors
Instrumenting cities
20. Data Science: Case Study
Cancer Research
20
• Cancer is an incredibly complex disease; a single tumor can have
more than 100 billion cells, and each cell can acquire mutations
individually. The disease is always changing, evolving, and adapting.
• Employ the power of big data analytics and high-performance
computing.
• Leverage sophisticated pattern and machine learning algorithms to
identify patterns that are potentially linked to cancer
• Huge amount of data processing and recognition
21. Data Science: Case Study
Health Care
21
• Stanford Medicine, Google
team up to harness power of
data science for health care
• Stanford Medicine will use the
power, security and scale of
Google Cloud Platform to
support precision health and
more efficient patient care.
• Analyzing genetic data
• Focusing on precision health
• Data as the engine that
drives research
http://med.stanford.edu/news/all-news/2016/08/stanford-medicine-google-team-up-to-harness-power-of-data-science.html
22. Data Science: Case Study
Elections
22
• The Obama campaigns in 2008 and 2012 are credited for their
successful use of social media and data mining.
• Micro-targeting in 2012
– http://www.theatlantic.com/politics/archive/2012/04/the-
creepiness-factor-how-obama-and-romney-are-getting-to-know-
you/255499/
– http://www.mediabizbloggers.com/group-m/How-Data-and-Micro-
Targeting-Won-the-2012-Election-for-Obama---Antony-Young-
Mindshare-North-America.html
• Micro-profiles built from multiple sources accessed by aps, real-
time updating data based on door-to-door visits, focused media
buys, e-mails and Facebook messages highly targeted.
• 1 million people installed the Obama Facebook app that gave
access to info on “friends”.
23. Data Science: Case Study
Internet of Things (IoT)
23
• The Internet of Things is rapidly growing. It is predicted that more than 25 billion devices
will be connected by 2020.
• The Internet of Things (IOT) will soon produce a massive volume and variety of data at
unprecedented velocity. If "Big Data" is the product of the IOT, "Data Science" is it's
soul.
25. Essential Points
• Big Data has given rise to Data Science
• Data science is rooted in solid foundations of
mathematics and statistics, computer science, and
domain knowledge
• Sexy profession – Data Scientists
• Not every thing with data or science is Data Science!
• The use cases for Data Science are compelling
25
26. Conclusion
In this section you have learned
• What Big Data Challenges are
• What exactly is Data Science and what do Data
Scientists do
• Data Science contrasted with other disciplines
• Case Study & Use Cases
26