Data Scientist

Learning path

Take these courses in order, from beginner to mid-level.

1. 1

   Step 1: STEM Foundations: The Universal Skill Stack

   The shared base every STEM career uses: working with numbers and units, testing ideas fairly, handling data and code, and explaining your work.

   Level: Beginner4 modules · 12 lessons · 7h 18mAlso used in 34 other careers
2. 2

   Step 2: Digital Literacy for Tech & STEM Work

   The everyday computer, internet, safety and AI skills every tech and STEM job assumes you already have.

   Level: Beginner4 modules · 12 lessons · 6h 8mAlso used in 34 other careers
3. 3

   Step 3: Professional Communication & Teamwork

   Write clear emails and documents, present and run meetings, work well on teams, and navigate résumés, interviews and networking.

   Level: Beginner4 modules · 12 lessons · 7h 24mAlso used in 34 other careers
4. 4

   Step 4: Math for Tech & STEM

   The practical math behind tech and STEM work: numbers, algebra, functions, geometry, logic and counting, with short code exercises.

   Level: Beginner5 modules · 15 lessons · 7h 32mAlso used in 34 other careers
5. 5

   Step 5: Spreadsheets for Data Work

   Clean tables, correct formulas, lookups, pivot tables and honest charts in Google Sheets, LibreOffice Calc or Excel.

   Level: Beginner3 modules · 9 lessons · 6h 35mAlso used in 16 other careers
6. 6

   Step 6: Git & the Command Line

   Use the terminal with confidence, track your work with Git, and collaborate on GitHub with pull requests.

   Level: Beginner3 modules · 9 lessons · 5h 57mAlso used in 15 other careers
7. 7

   Step 7: Python Fundamentals

   Learn to program in Python from zero: values, decisions, loops, functions, data structures, files, modules and a small data project.

   Level: Beginner5 modules · 15 lessons · 9h 53mAlso used in 22 other careers
8. 8

   Step 8: SQL Fundamentals

   Query, summarise, join and change data in a relational database with SQL, practised in your browser.

   Level: Beginner4 modules · 12 lessons · 5h 44mAlso used in 11 other careers
9. 9

   Step 9: Statistics Fundamentals

   Describe data, reason with probability, and draw honest conclusions from samples, with short fill-in-the-blank Python exercises.

   Level: Beginner4 modules · 12 lessons · 6h 50mAlso used in 14 other careers
10. 10

    Step 10: Foundations for Software, Data, Design & Tech Education Careers

    Understand tech roles and career ladders, pick a realistic learning route, build portfolio evidence, and navigate postings, interviews and your first 90 days.

    Level: Beginner3 modules · 9 lessons · 7h 22mAlso used in 20 other careers
11. 11

    Step 11: Intro to Data Analysis with Python

    Load, clean, reshape and explore real data with Python and pandas, then turn the results into clear findings.

    Level: Intermediate4 modules · 13 lessons · 12h 55mAlso used in 10 other careers
12. 12

    Step 12: Data Structures & Algorithms

    Measure efficiency with Big-O, then use searching, sorting, hash maps, stacks, linked lists, trees, graphs and dynamic programming in Python.

    Level: Intermediate4 modules · 12 lessons · 7h 37mAlso used in 7 other careers
13. 13

    Step 13: SQL for Analysis

    Go beyond the basics with subqueries, CTEs, window functions and the analytics patterns analysts use every day, practised in your browser.

    Level: Intermediate4 modules · 12 lessons · 7h 18mAlso used in 5 other careers
14. 14

    Step 14: Data Visualization & Storytelling

    Turn analysis into charts people read correctly: visual principles, matplotlib and seaborn, small multiples, dashboards and a short data story.

    Level: Intermediate3 modules · 9 lessons · 7h 29mAlso used in 3 other careers
15. 15

    Step 15: Calculus & Linear Algebra for STEM

    The calculus and linear algebra behind science, engineering and machine learning: derivatives, integrals, vectors, matrices, gradients and eigenvalues, with Python exercises.

    Level: Intermediate4 modules · 12 lessons · 7h 48mAlso used in 11 other careers
16. 16

    Step 16: Probability & Statistical Inference

    Model uncertainty with random variables and distributions, then estimate, test and update beliefs from data, with Python simulations you run in the browser.

    Level: Intermediate4 modules · 12 lessons · 9h 26mAlso used in 7 other careers
17. 17

    Step 17: Machine Learning Foundations

    Learn how machines learn from data: implement regression, trees, metrics, k-means, PCA and anomaly detection from scratch in NumPy, and practise honest evaluation.

    Level: Intermediate4 modules · 12 lessons · 6h 49mAlso used in 1 other career
18. 18

    Step 18: Applied Machine Learning

    Go from classic ML to the models used at work: neural networks, embeddings, CNNs, gradient boosting and LLM apps, plus evaluation, experiment tracking and a capstone.

    Level: Mid-level4 modules · 12 lessons · 10h 4mAlso used in 1 other career
19. 19

    Step 19: Regression & Experimental Design

    Fit and check multiple regression and GLMs, design randomised, blocked and factorial experiments, and turn analyses into clear, reproducible reports for the researchers you work with.

    Level: Mid-level4 modules · 12 lessons · 8hAlso used in 5 other careers

Skills you'll build

Free resources to get started

Hand-picked courses and reading from trusted free providers.

• freeCodeCamp

  Trustedcourse

  Data Analysis with Python

  Free certification covering NumPy, Pandas, Matplotlib, and Seaborn through five real projects.

• freeCodeCamp

  Trustedcourse

  Machine Learning with Python

  Free certification introducing TensorFlow, neural networks, and core ML techniques.

• Harvard / edX

  Trustedcourse

  CS50: Introduction to Computer Science

  Harvard's free intro CS course — widely considered one of the best on-ramps to programming.

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