Data Science Roadmap

1. Careers in Data Science and Analytics
Sujoy Roychowdhury

2. The Data Science Market in India
Data Science Jobs in India [1]
[1] https://www.analyticsinsight.net/analytics-insight-predicts-137630-new-jobs-in-data-science-in-india-by-2025/
[2] https://www.livemint.com/news/india/data-science-jobs-pay-zoom-as-digital-landscape-shifts-11616958845320.html
“Courses on business analytics, artificial
intelligence, and machine learning are the most
popular courses among IIM-B students. It helps
them target core analytics companies as well as
those that look for a combination of analytics and
management skills during placements," said
professor U. Dinesh Kumar, chairperson, MBA in
business analytics, IIM Bangalore.[2]

3. Roles in Data Science
Business Facing
Roles
Domain SME
Business
Development / Tech
pre-Sales/Tech Sales
Business Analyst
Technology
Roles
• Analytics Consultant
• Data Engineer
• Data Scientist
• Full Stack Developer
• Visualization Expert
Deployment and
Rollout Roles
• ML Engineer

4. Business Facing Roles
 These roles are intended for people coming with a broad
experience of business domains and understand challenges
in that domain
 Needs to have an awareness of AI models, processes and
systems
 Needs to be able to communicate with various stakeholders
and teams
 Deep technical experience not needed. Coding skills not
needed / very little needed.
 Focus on 1 or 2 business domains – preferably ones with
experience in
 Spend time reading and learning about different AI
approaches in this field – including what did not work / is
difficult
 Read on AI approaches overall and be consistently up-to-
date
 Be humble and aware of your limitations – the difference
between a POC / a paper and a real world implementation
can be very vast
WHAT HOW

5. Technology Roles
 These roles require a fair amount of development skills and
technology knowledge
 AI awareness and skills depend on
 Technical expertise needed based on experience and role
 Choose what you want to do and prepare accordingly
 Practice what you learn
 Read and be updated
 Be humble and understand this area is truly vast and rapidly
evolving. Constant learning is the only solution
WHAT HOW

6. THE HYPE AND THE REALITY
WHAT IS ANALYTICS OR DATA SCIENCE – OR WHAT IT IS AND WHAT IT IS NOT
TOO MANY TERMS – ANALYTICS / BUSINESS ANALYTICS / ADVANCED ANALYTICS / MACHINE LEARNING / ARTIFICIAL INTELLIGENCE
A LOT OF MUNDANE WORK PASSING OFF AS DATA SCIENCE
SERIOUS LACK OF SKILLS AND KNOWLEDGE

7. THE MBA “DATA SCIENTIST”

8. DATA SCIENCE CONSULTANT
 Working with the business to understand, ideate and conceptualize a data science concept
 Strong concepts of the principles and assumptions to engage in the right method and speak “properly”
 Domain knowledge greatly helps in this role
 Small POCs and demonstrations for initial solution
 Ability and willingness to be hands-on when required
 Remain abreast of latest changes
 Natural role for MBAs with a flair for technology and an interest in ( if not passion ) for data science

9. PRACTISING DATA SCIENTIST
 Business interactions on a more technical scale
 Strong hands-on experience (60-70%)
 Ability to be able to pick up new involved (often mathematical) topics from various subjects e.g. economics, computer science
 Drive full solutions – and if required code in them
 Tough challenge
 Need a lot of self-learning
 Difficult to establish credibility
 Need a lot of passion

10. ONE TIME ANALYTICS vs. ANALYTICAL SOLUTIONS
In many ways the difference between the above roles is primarily
between doing one-time analytics and industrialized analytical
solutions

11. Data Scientist vs. Data Engineer vs. Full Stack Developer vs. ML Engineer
Primary difference is role in an engineering solution
Depth of data science vs. depth of IT system integration vs. depth of data
processing knowledge

12. CHALLENGES IN THE INDUSTRY
Analytics is NOT IT ….
Full of half-baked analytics professionals 
Needs passion which is largely lacking
Regression towards the mean

13. ORGANIZATIONAL CHALLENGES
What to do with analytics and why ?
Analytics is NOT IT ….
The data science thinking