Data scientist
Learn to turn messy data into statistics, models and experiments that change a decision, working with AI coding tools from week 1 and building an LLM-backed analysis tool by the end, in 24 weeks.
Coming soon · planned, not written yet
Data science ranks second among skills India’s global capability centres hire forTaggd, India GCC skills in demand 2026
- 24
- weeks planned
- ~35 h
- a week
- 5
- milestone projects
- 1
- project, grown every week
These are planned figures. The weeks are written after we check the tools against current job postings and official documentation, so the final route may differ.
Built for the AI era
How AI fits this roadmap
AI assistants can now write pandas code, SQL and model boilerplate that data scientists used to type by hand, and they can also produce analysis that looks right and is wrong. That moves your value toward framing the question, checking the data and testing the result, so this roadmap has you use an assistant every week and verify its work with your own tests, checks and holdout data. In the later weeks you also build and evaluate a small feature that uses a language model, because many data science roles now include that kind of work.
The main project
DecisionLab
One real-world dataset that you clean, analyse, model and test with experiments, then wrap in a small app that includes an AI assistant for asking questions of the data, with every answer checked.
What you will be able to do
- 01
Query and clean data with SQL and pandas, and write tests that catch the mistakes an AI assistant makes in that code.
- 02
Choose and run the right statistical test or regression for a question, and explain the result and its limits in plain words.
- 03
Build, validate and compare machine learning models with scikit-learn, avoiding leakage and overfitting, and record each run.
- 04
Design and analyse an A/B test, and write a one-page recommendation that a manager could act on.
- 05
Build and evaluate a small LLM-backed feature on your data, with a test set that measures when it is wrong, and ship the project in an app with a written case study.
01The route
Five months, one month at a time.
Each month ends in a milestone project, so you always have something to show for the work.
Month 1 · Weeks 1–4
Python, SQL and AI-assisted data work
Python, SQL and pandas for loading, joining and cleaning data, plus Git. You set up an AI coding assistant, learn to prompt it, read its code and test what it writes.
MilestoneA cleaned DecisionLab dataset with a repeatable cleaning script, tests, and a log of AI suggestions you accepted and rejected
Month 2 · Weeks 5–8
Statistics and exploration
Distributions, sampling, confidence intervals, hypothesis tests and regression with statsmodels, and clear charts. You ask an AI to run an analysis, then redo it by hand to see where it went wrong.
MilestoneA short analysis report with charts, tests and stated limits, checked against an AI-written version
Month 3 · Weeks 9–14
Machine learning
Features, pipelines, classification, regression, clustering and model selection with scikit-learn, with cross-validation and leakage checks. You use AI to suggest features and code, then prove they help on held-out data.
MilestoneA validated model with a comparison of at least three approaches and a record of every run
Month 4 · Weeks 15–18
Experiments and decisions
A/B test design, sample size, analysis, common traps, and turning results into a recommendation. You use AI to draft the write-up and then check every number in it.
MilestoneAn experiment analysis and a one-page decision memo
Month 5 · Weeks 19–24
Language models, apps and the job search
Call a language model from code, ground it in your data, and build a test set to measure its errors. Put DecisionLab in a Streamlit app, write the case study, and prepare for interviews and applications.
MilestoneA deployed DecisionLab app with an evaluated AI feature, a written case study and an application plan
03What is inside
Everything a week comes with.
Every week is built the same way, so once you have done one you know how all of them work.
- 01
One project, DecisionLab
You grow one dataset project from raw files to a decision and an app, with an AI feature inside it, so every tool lands in something real.
- 02
A day-by-day plan
Each week has a Learn part and a Do part, with a checklist so you know when you are done. Most weeks include a task to do with an AI assistant and a step to check its work.
- 03
A project in every milestone
Five milestones, each ending in something you can show and link to.
- 04
Reference topics
Short topic pages with check questions you can come back to.
- 05
A job track
Interview practice, portfolio and resume help, and an application plan.
- 06
Progress in your Study plan
Add weeks to your Study plan, give them dates, and finished days are counted for you.
04Is it for you
Who this is, and is not, for.
Better to know before you start than six weeks in.
It is for you if
- You can write small programs in some language and want to work with data, statistics and models.
- You are an analyst, developer or graduate moving into data science and want to be strong with AI tools.
- You can give about 35 hours a week for six months. If you can only give less, plan for longer than 24 weeks.
It is not for you if
- You have never written code or used a terminal. Do a beginner course first.
- You want a job guarantee. Nobody can promise that, and this roadmap does not.
- You want to train new large language models from scratch. This roadmap uses and evaluates them, it does not build them.
- You want to paste AI output into a report without checking it. This roadmap trains you to check it.
?Questions
Before you start
- Is this roadmap ready?
- Not yet. This is the plan. The weeks are being written and checked against current job postings and official documentation. Leave your email and we will write to you when it opens.
- What will it cost?
- The price is not set. Roadmaps are bought once, with no subscription. We will announce it when this one opens.
- Will the plan change?
- It may. Before the weeks are written we check the tools against current job postings, so the lengths and months here are planned, not final.
- Will I need strong maths?
- You need school-level algebra. The statistics weeks teach the rest as you use it, with the ideas shown on real data first.
- Will it get me a job?
- It will give you skills and proof of work. It cannot promise a job on a fixed date, because hiring depends on the market, your location and your past work.
Not open yet
Leave your email and we will write once, when it opens.