Webinar slides

1. Webinar for Aspirants of Data Analytics
Data Analytics
Prakash Pimpale
Joint Director, C-DAC Mumbai
A premier R&D organization of the Ministry of Electronics
and Information Technology (MeitY), Govt. of India

2. Introduction Applications Career Prospects

3. Data
Data are a set of values of
qualitative or quantitative
variables about one or more
entities.

4. Analytics
Analytics is the systematic
computational analysis of data.
It is used for the discovery,
interpretation, and
communication of meaningful
patterns in data. It also entails
applying data patterns towards
effective decision-making.

5. Science
Systematic enterprise that
builds and organizes knowledge
in the form of testable
explanations and predictions
about the universe

6. Derived Def
• Data Analytics
• Analytics is the
systematic computational analysis
of set of values of qualitative or
quantitative variables about one or
more entities.
• Data Science
• Systematic enterprise that builds
and organizes knowledge in the
form of testable explanations and
predictions about the universe set
of values of qualitative or
quantitative variables about one or
more entities.

7. Data Science
Data Analytics
Data Scientist
Data Engineer
Data Wrangler
Analytics Engineer
Visualization Engineer

8. Data Analytics
is not 'just'
Computer Programming
Visualization
Machine Learning
Includes integrated applications of above
and lots of Domain Understanding.

9. Data Analytics Constitutes
Data
Existing Digitised System or Data –
eCommerce site/app transactions,
banking applications with customer
and transaction records, survey
results, etc.
Data Infrastructure
Storage, Processing and Management
– Databases, transformation tools,
bigdata infra (like Apache Spark,
Cassandra, HDFS), etc.
Data Analytics/Science Tools
Algorithms, visualizations and
programming tools – statistical
analysis, classification, prediction
algorithms, charting libraries, Python,
R, etc.

10. Why now
The Data
And this is just the social
applications!
There is additional data
being generated through
Enterprise and Consumer
applications!
DOMO

11. Why now
The Infrastructure

12. Why now
The Machine Learning
Mobidev

13. Data Analytics Pipeline
Source
1
Store and Manage
2
Pre-process
3
Explore
4
Analyse/Model/Predict/Present
5

14. A lot must happen prior to the real analytics

15. An Important Pillar

16. A non-Machine Learning Based Solutions
Non-ML System
Rules
Input
Output
A Developer
With package 8 lpa

17. ML System (Train) Rules
Input
Output
A Data Science/Analytics Developer
ML System
(Production)
New
Inputs
Output
Machine Learning Based Solutions

18. An Example of
Machine Learning

19. Analysis

20. Visualization
Swine flu mortality
comparison for major Indian
states for the first and second
waves

21. Visualization
Enron email dataset visualized

22. Visualization
• Cambridge Intelligence

23. Major Challenges
Lack of Skills how do we do all of these?
Domain Knowledge what does 'customer churn' mean, which of these products are for male and which of these for female?
Problem Formulation and choice
of techniques
which one of classification, prediction, numeric, non-numeric or just exploratory, etc.?
Cleaning of the Data what is that we don't need from this or what is misleading in it?
Integration of the data from
multiple data sources
if it's not in single place, how do we integrate these with minimum error and maximum data?
Choice of the Right Data and
Data Sources
which of the data is useful and what is the right place to get it?
Availability of Data is data for what you want to solve available?

24. Some more
challenges and
resolutions
More at https://analyticsindiamag.com/7-challenges-
faced-by-data-scientists-in-data-processing-in-2020/​

25. Applications
Data is the new Oil!

26. Applications

27. Applications

28. Applications

29. Applications

30. Prospect
Survey by Harvard Business Review
Analytics Services on lessons learned from
early adopter of this group of
technologies

31. Prospect Another survey by
Accenture "Closing the
Data Value Gap"

32. Prospect
The same survey mentions one of the
major reasons behind the gap – lack
of skills or skilled resources in Data
Analytics

33. How can you
get into this..
Build Skills.

34. Practitioners Speak
What do you expect from Fresh Data Analytics Developer of Data Scientist in your team?
I will speak for the projects I have been part of. I have always expected my developers to
be good at Problem Solving and Python. Familiarity of the Data Analytics tool stack is
something that will be an add on, but good problem-solving skills is a must!
Prasad Pawar
Senior Data Scientist working with TCS.

35. Practitioners Speak
What kinds of role a fresh Data Scientist will get to
perform?
When I got into this industry, I was expected to just create
models given data. I was not much worried about where it
came from. But now in some of my projects the analytics
developers are expected to know everything in the data
analytics stack. When the project has small team this is de
facto requirement. But yes, being 'really good' at some part
of the stack will make you visible and go longer.
Nitin Agarwal
Senior Data Scientist
from AI@Scale Team, Fractal

36. Practitioners
Speak
What do you do as a Data Analyst?
I joined as Data Analytics developer recently. I am
working on a cloud-based analytics tool. The tool was
not taught in the course but the concepts, lifecycle of
data analytics projects, functioning of various tools
which was taught to me in the course helps me a lot.
I am confident that with that foundational
understanding I can explore, learn and work with any
tool that may be required.
Rahul Shilpakar
Data Analyst working for a MNC
& an alumni of PG-DBDA, C-DAC

37. Thank you!