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.
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.
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 Courses In Mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
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.
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 Courses In Mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Data Science Applications &
Use Cases
Instructor: Ekpe Okorafor
1. Accenture – Big Data Academy
2. Computer Science African University of
Science &
Technology
2. Objectives
2
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
3. Outline
3
• Big Data & Challenges
• What is Data Science
• Data Science & Academia
• Data Science & Others
• Case Studies
• Essential points
• Conclusion
4. Data All Around
4
• 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!
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?
• Some definitions link computational, statistical, and
substantive expertise.
8
9. What is Data Science?
• Other definitions focus more on technical skills alone.
9
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 machinelearning
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
Scientific Modeling
Physics-based models
Problem-Structured
Mostly deterministic, precise
Run on Supercomputer or High-
end Computing Cluster
Imag
e
General purpose classifier
Supernov
a
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)
Not
Nugent group / C3 LBL
13
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
• 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:
16
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
ReducingHospital
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
• 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
20
21. Data Science: Case Study
Health Care
• 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
21
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
• 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”.
22
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
25
• 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
26. Conclusion
26
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