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Data Science Roadmap

Python, statistics, SQL and machine learning — turn data into a well-paid career.

Data science blends programming, statistics and business sense to turn data into decisions. It's a high-paying field with roles ranging from data analyst to data scientist. This roadmap gives you a practical, project-driven path.

Start with Python and SQL (the daily tools), build a strong statistics foundation, then move into machine learning and communication. Real projects and a portfolio are what get you hired.

6–8 months Beginner → Job-ready 8 core skills

Phase 1 · Tools & foundations

Weeks 1–6

Python for data

Python plus NumPy, Pandas for data manipulation and Matplotlib/Seaborn for visualisation.

pythonpandasnumpy

SQL

Query databases confidently — joins, aggregations, window functions. Every data job needs it.

SQLjoinsqueries

Statistics & probability

Descriptive stats, distributions, hypothesis testing, correlation vs causation.

statisticsprobabilitytesting

Phase 2 · Data analysis

Weeks 7–12

Exploratory data analysis

Cleaning, transforming and exploring real datasets to find insights.

EDAcleaning

Data visualisation

Tell stories with data; dashboards with tools like Tableau/Power BI or Plotly.

visualisationdashboards

Analyst projects

End-to-end analysis projects on real datasets, documented clearly.

projectsanalysis

Phase 3 · Machine learning

Weeks 13–20

Core ML

Regression, classification, clustering with scikit-learn; model evaluation and validation.

scikit-learnMLevaluation

Feature engineering

Turning raw data into features that make models work — the real skill.

featuresengineering

Intro to deep learning

Basic neural networks and where they fit (optional but valuable).

deep learningneural nets

Phase 4 · Portfolio & job-readiness

Weeks 21–28

Capstone projects

2–3 strong, end-to-end projects (analysis + ML + clear write-up) on GitHub/Kaggle.

capstonekaggleportfolio

Communication

Explaining findings to non-technical people — a core hiring criterion.

communicationstorytelling

Interview prep

SQL, statistics, ML and case-study rounds.

interviewSQLcases

Projects to build

Recruiters probe projects, they don't count them. Ship 2–3 of these, deploy them, and put them on GitHub with a clean README:

End-to-end EDA on a real-world dataset with insights
A predictive ML model with clear evaluation
An interactive dashboard (Tableau/Power BI/Plotly)
A Kaggle competition entry

Where this roadmap leads

Follow this path and you're prepared for roles like:

Data Analyst
Data Scientist
Business Analyst
ML-adjacent roles

Do it in the app: FindMyFuture.AI turns this roadmap into a personalised, trackable path with curated resources per step — and pairs it with DSA prep and aptitude practice so you're placement-ready, not just skilled.

Frequently asked questions

Data analyst or data scientist first?

Data analyst is a great, achievable entry point (Python + SQL + visualisation). Grow into data scientist by adding machine learning and stronger statistics.

Do I need a master's degree?

No — many data professionals are self-taught with strong portfolios. Real projects and demonstrable skills matter more than a specific degree.

Start today · Free to begin

Follow this roadmap inside the app

Personalised, trackable, with curated resources for every step.

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