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Free Data Science Course

Become a data scientist from zero — 100% free, self-paced, no signup. From your first Python line to machine learning models, AI APIs and a portfolio that gets interviews: 6 modules, a 30-day study plan, and a career roadmap, all in one place.

🚀 Start Learning Free

🗺️ Your Learning Path

Six modules, beginner to job-ready — follow them in order.

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Module 1 · Python Foundations

Weeks 1–2 · 20 lessons · Beginner

Write your first Python programs, then master NumPy arrays and pandas DataFrames — the two tools every data scientist touches daily.

PythonNumPypandasGit
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Module 2 · Statistics & Probability for Data

Weeks 3–4 · 18 lessons · Beginner

Learn exactly the math you will use: averages, distributions, hypothesis testing and correlation — explained with real datasets, not dry formulas.

Descriptive statsProbabilitySamplingA/B testing
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Module 3 · Data Wrangling & Visualization

Weeks 5–6 · 22 lessons · Intermediate

Clean messy real-world data, join tables, query with SQL, and turn numbers into charts and dashboards people actually understand.

Data cleaningSQLMatplotlibDashboards
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Module 4 · Machine Learning Fundamentals

Weeks 7–9 · 25 lessons · Intermediate

Train your first models: regression, classification and clustering — and learn how to evaluate them honestly so you trust your results.

scikit-learnRegressionClassificationValidation
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Module 5 · AI APIs & Model Fine-Tuning

Week 10 · 12 lessons · Intermediate

Go beyond basics: call modern AI APIs, build smart data apps with them, and learn how fine-tuning adapts models to your own data.

AI APIsPrompt designFine-tuningAI apps
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Module 6 · Capstone Projects + Interview Prep

Weeks 11–12 · 15 lessons · Advanced

Build 3 end-to-end projects on real public datasets, publish them on Kaggle and GitHub, and practice interviews until you are ready.

KaggleGitHubCase studiesInterviews

📅 30-Day Study Plan

1–2 focused hours a day — the same plan our learners follow.

Days 1–7
Setup + core fundamentals
  • Type every example by hand — muscle memory matters.
  • Set up Git on day one and commit your practice daily.
  • Master statistics fundamentals before moving ahead.
Days 8–14
Core concepts + mini projects
  • Refactor an old project with your new NumPy skills.
  • Teach one concept to a friend — teaching cements mastery.
  • Time-box debugging: 45 minutes, then ask a clear question.
Days 15–21
Portfolio projects + deployment
  • Rebuild your weakest project from scratch.
  • Write case-study posts about your builds.
  • Contribute one small fix to an open-source project.
Days 22–30
Advanced topics + career prep
  • Practice mock interviews weekly — speaking about code is a skill.
  • Study real job postings and map your projects to them.
  • Record a 3-minute demo video of your best project.

💼 Career Roadmap

The exact path from beginner to junior data roles or freelance AI gigs.

  1. Get strong in Python & statistics basics3–4 months of consistent practice. This foundation makes everything after it easier.
  2. Master pandas, NumPy & visualizationSlice, clean and chart real datasets — this is what daily data work looks like.
  3. Study machine learning fundamentalsRegression, classification and how models are evaluated. Understand before you optimize.
  4. Build 3 portfolio projectsUse real public datasets (Kaggle is free and full of them) and publish your code.
  5. Learn modern AI APIs & fine-tuningThen apply for junior data roles or freelance AI gigs — with proof you can ship.
💡 Pro tips: Applied statistics beats theory for real jobs. Publish on Kaggle and GitHub — recruiters actively search both. Pick one industry domain (health, finance, retail) and learn its data; domain knowledge doubles your value. Keep up with new AI tools monthly — this field moves faster than any other.

📚 Related Reading

Guides from the US AI library that pair perfectly with this course.

📊What Data Scientists DoWhat data scientists actually do all day, and how to become one — the beginner overview. 🐍Learn Python 2026The fastest path to Python in 2026 — the language Module 1 is built on. 🗄️Learn SQL From ZeroQuery real databases like a pro — essential for Module 3 data wrangling.

❓ Frequently asked questions

Is this Data Science course really free?

Yes — all 6 modules, the 30-day study plan and the career roadmap are 100% free. No signup, no payment, no trial limits.

Do I need to know Python before starting?

No. Module 1 starts from zero — Python basics first, then NumPy and pandas, the two libraries every data scientist uses daily.

How long does the course take?

With 1–2 hours a day, the 30-day foundation plan takes about a month; the full curriculum to job-ready projects takes around 12 weeks.

Do I need a strong math background?

Applied statistics matters more than heavy math theory. Module 2 teaches exactly the probability and statistics you will actually use on real data.

What will I build by the end?

Capstone portfolio projects on real public datasets — the same kind of work recruiters look for on GitHub and Kaggle.

What software do I need?

Just a web browser and a free Python setup (Anaconda or plain Python with pip). Everything else — datasets and tools — is free online.