![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]](https://image.slidesharecdn.com/dsroadmap-220527091025-92a96bdc/85/Analytics-Data-Science-2-320.jpg)
Analytics & Data Science
- 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