Data Science Training in Hyderabad,Data Science training institutes in hyderabad

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Musings on Data Science and Students
Experiencing Data Analytics
Presented By
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The first war Terminology

• Analyzing data has a long history!
• There have been many terms that have been used to
  describe such endeavors
• Statistics
• Artificial Intelligence
• Machine learning
• Data analytics
• Since I happen to work in a Data Science
  program perhaps I may be allowed the indulgence
  of using that terminology

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Whatever we call it, what makes things different
now?
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The Good
Experiments, observations, and numerical
simulations in many areas of science and business
are currently generating terabytes of data, and
in some cases are on the verge of generating
petabytes and beyond. Analyses of the information
contained in these data sets have already led to
major breakthroughs in fields ranging from
genomics to astronomy and high-energy physics and
to the development of new information-based
industries.
The Bad
Given a large mass of data, we can by judicious
selection construct perfectly plausible
unassailable theoriesall of which, some of
which, or none of which may be right.
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The Hopeful
The ability to take datato be able to understand
it, to process it, to extract value from it, to
visualize it, to communicate itthats going to
be a hugely important skill in the next decades,
not only at the professional level but even at
the educational level for elementary school kids,
for high school kids, for college kids. Because
now we really do have essentially free and
ubiquitous data. So the complimentary scarce
factor is the ability to understand that data and
extract value from it.
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What is Big Data?

• The are many examples of "data", but what makes
  some of it big? The classic definition
  revolves around the three Vs.
• Volume, velocity, and variety.
• Volume There is a just a lot of it being
  generated all the time. Things get interesting
  and big, when you cant fit it all on one
  computer anymore. Why? There are many ideas
  here such as MapReduce, Hadoop, etc. that all
  revolve around being able to process data that
  goes from Terabytes, to Petabytes, to Exabytes.
• Velocity Data is being generated very quickly.
  Can you even store it all? If not, then what do
  you get rid of and what do you keep?
• Variety The data types you mention all take
  different shapes. What does it mean to store
  them so that you can play with or compare them?

http//pl.wikipedia.org/wiki/Green_Giantmediaview
er/PlikJolly_green_giant.jpg
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Is Big Data the same as Data Science?

• Are Big Data and Data Science the same thing?
• I wouldn't say so...
• Data Science can be done on small data sets.
• And not everything done using Big Data would
  necessarily be called Data Science.

Big Data
Data Science
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Is Big Data the same as Data Science?

• Are Big Data and Data Science the same thing?
• I wouldn't say so...
• Data Science can be done on small data sets.
• And not everything done using Big Data would
  necessarily be called Data Science.
• But there certainly is a substantial overlap!

Big Data
Data Science
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Can you even be certain?

• For real world problems, I claim that you will
  never be certain of any inferences from data.
• I mean, what happens to your carefully thought
  out marketing plan for some rocking slacks when
  the Martians land.
• What is unacceptable is when the data you
  actually have does not support the conclusion you
  report.

Public domain image
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Skills for Data Science
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Which is most important?
http//en.wikipedia.org/wiki/View_of_the_World_fro
m_9th_Avenue
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WPI Data Science ProgramA Collaboration
Computer Science Department
Mathematical Sciences Department
Business School
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Data Science Core
Integrative Data Science DS 501 Introduction
to Data Science (new course)  Mathematical
Analytics (Select one) MA 543/DS 502 Statistical
Methods for Data Science (new course) MA 542
Regression Analysis MA 554 Applied Multivariate
Analysis   Data Access and Management (Select
one) CS 542 Database Management Systems MIS 571
Database Applications Development CS 561 Advanced
Topics in Database Systems CS 585/DS 503 Big Data
Management (new course)   Data Analytics and
Mining (Select one) CS 548 Knowledge Discovery
and Data Mining CS 539 Machine Learning CS 586/DS
504 Big Data Analytics (new course)   Business
Intelligence and Case Studies (Select one) MIS
584 Business Intelligence MKT 568 Data Mining
Business Applications

• Data Science Certificate Program (18 credits)
• 15 CREDIT DATA SCIENCE CORE
• plus
• 3 CREDIT ELECTIVE

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Thank You
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