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6–9 months

Data Scientist Roadmap

Statistics, Python, ML and storytelling — the complete path.

Who this is for: graduates in maths, statistics, engineering, commerce or economics, and analysts who want to move into modelling. You should be comfortable with school-level algebra. Plan for 12–15 hours a week; at that pace the path takes six to nine months.

What a data scientist actually does. Most of the job is not building models. It is turning a vague business question ("why are sales down in Pune?") into something measurable, getting the data out of SQL and spreadsheets, cleaning it, and explaining the answer to people who do not read code. Modelling is maybe a fifth of the work. This roadmap is weighted the same way: two full stages go to data handling, statistics and visualisation before machine learning starts.

How to use it: keep one notebook per stage and one dataset you care about (cricket scores, your city's weather, your family shop's sales). Reusing the same data across stages shows you how each new skill changes the answers you can give.

Portfolio advice: three finished projects beat ten half-finished ones. For each, write a short README that states the question, the data source, what you found, and what you would do next. Hiring managers read the README before the code.

Common mistakes: jumping to deep learning before you can do a proper train/test split; reporting accuracy on an imbalanced dataset; and showing charts without saying what decision they support.

1

Foundations

6 weeks

Get data in, clean it and summarise it. By the end you should be able to load a CSV or SQL table, fix types and missing values, and answer a business question with a group-by. Everything later depends on this stage.

  • Python basics
  • NumPy
  • Pandas
  • SQL / MySQL

Milestone project: Analyse a public dataset (e.g. IPL, Indian census)

Learn Pandas on BeeProton →
2

Statistics & maths

4 weeks

Learn to tell signal from noise. Means, spread and distributions describe data; hypothesis tests and confidence intervals tell you whether a difference is real; enough linear algebra lets you understand what models do with your features.

  • Descriptive statistics
  • Probability
  • Hypothesis testing
  • Linear algebra basics

Milestone project: A/B test analysis report

Learn Statistics on BeeProton →
3

Visualisation & EDA

3 weeks

Look before you model. A disciplined exploratory analysis (distributions, outliers, relationships and segments) usually finds the main insight before any machine learning happens. Learn to choose the chart that answers the question.

  • Matplotlib & Seaborn
  • Plotly
  • EDA workflow
  • Dashboarding

Milestone project: EDA notebook with insights

Learn Matplotlib on BeeProton →
4

Machine learning

8 weeks

Build models the honest way: split data properly, start from a simple baseline, pick a metric that matches the business cost of errors, and use pipelines so preprocessing never leaks test data into training.

  • Regression & classification
  • Trees & ensembles
  • Clustering
  • Model evaluation
  • Feature engineering

Milestone project: Churn prediction end-to-end

Learn Machine Learning on BeeProton →
5

Advanced & deployment

6 weeks

Add deep learning and language models where they genuinely help, then make your work usable by others: a small Streamlit app or FastAPI endpoint that someone can open without running your notebook.

  • Deep learning basics
  • NLP & LLMs
  • Streamlit / FastAPI
  • MLOps basics

Milestone project: Deploy a model as a web app

Learn Deep Learning on BeeProton →