Data Scientist / Analyst
Last updated · Confirm dates, fees and eligibility on the official website before you apply.
₹8–35 LPA · SQL, Python, stats
- Route in
- Typically Bachelor of Technology (B.Tech). 1 course lead here
- Entry pay
- ₹800,000 – ₹3,500,000 / annual Any experience
What the work involves
Day to day
Turning data into decisions - querying and cleaning it, analysing it, building models where useful, and explaining the result to people who will act on it. Cleaning and communicating take far more time than modelling.
Who this suits
Fit
Statistically comfortable people who can write clearly and argue from evidence. Demand spans every sector rather than only technology, and analyst roles are one of the more accessible entries for graduates from economics, statistics and science backgrounds.
The honest reality
Read this one
The gap between the job and its reputation is wide: most of it is SQL, dashboards and data quality problems, not machine learning. The field was oversold hard, and a large number of short bootcamps produced graduates who cannot pass a technical screen - the market is now crowded at the bottom and still short at the top. Business stakeholders may ignore analysis that contradicts what they wanted. Statistical fundamentals are what separate people here, and they cannot be acquired in six weeks.
The route in, step by step
6 steps from where you are now.
A quantitative degree Required
B.Tech, B.Sc in statistics, mathematics or economics. Numeracy matters more than the label.
Learn SQL and statistics before machine learning Required
SQL is the most requested and most commonly missing skill in analytics hiring. Statistics - distributions, hypothesis testing, regression - is what stops you producing confident nonsense.
Learn Python and the working libraries Required
pandas, scikit-learn, and visualisation. Then machine learning properly - understanding when a model is inappropriate matters more than knowing many algorithms.
Build a portfolio on real, messy data Required
End-to-end projects with data you had to clean yourself. Tutorial datasets prove nothing because everyone has done them.
Start as an analyst, not a scientist Required
Almost nobody enters as a data scientist directly. Data or business analyst first, learning the domain, then modelling work. Domain knowledge is what makes a model useful.
Data scientist, ML engineer, or analytics leadership Optional
Modelling roles, machine learning engineering - which is closer to software than to statistics - or leading an analytics function.
Courses that lead here
1 mapped route into this career.
The roles this becomes
3 lanes out of the same starting point.
What it pays
Indicative bands.
| Stage | Pay band | What changes |
|---|---|---|
| Any experience | ₹800,000 – ₹3,500,000 / annual | Indicative range imported from the career map. Unverified - confirm and add a source before publishing. |
These are ranges, not offers. Pay varies by city, employer size, sector and your own skill more than by job title. Treat the band as the shape of the market, not as a number you can hold anyone to.
Common questions
The ones people actually ask about this work.
How much of the job is machine learning?
Much less than expected. Most of it is SQL, data cleaning, dashboards and explaining findings to people who will act on them.
Are bootcamps enough to get hired?
Frequently not. The entry level is crowded with graduates who cannot pass a technical screen, and statistical fundamentals are what separate candidates.
Do I need a computer science degree?
No. Economics, statistics and science graduates enter regularly, provided they can code and reason statistically to a real standard.
What if the business ignores my analysis?
It happens often. Persuasion and clear communication are as much part of the role as the analysis itself.
Test this against your own priorities
Pay, hours and entry route matter differently to different people. Compare this against the alternative you are actually weighing, rather than against the average.