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Data Science

Turning messy data into honest answers: cleaning it, modelling it, testing what causes what, and showing it so people understand.

For: anyone who works with spreadsheets or dashboards, anyone thinking of studying data science or analytics, and anyone who wants to read a chart or a study without being fooled.

Shaped by the ACM Data Science Task Force's curriculum guidelines (2021), the National Academies' Data Science for Undergraduates (2018) and programmes such as Berkeley's Data 8/Data 100 and MIT's statistics and data science courses; examples use invented or public datasets, and every model is computed live.

16readings
3.2 hoursof reading
4stages
15 reads · 1 deep dive
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Stage 1

Year 1 · The language

4 readings · 46 min
  1. 01ReadInteractiveQuiz10 min

    Calculus I: Slopes, Totals and the Bridge Between Them

    Rates of change and accumulation, under every model.
  2. 02ReadInteractiveQuiz13 min

    Linear Algebra: Machines That Move Space

    Data is a matrix; most methods are linear algebra.
  3. 03ReadInteractiveQuiz11 min

    Programming Fundamentals: Instructions, State and Taking Things Apart

    Code is how a data scientist does anything at scale.
  4. 04ReadInteractiveQuiz12 min

    Probability & Statistics: Reasoning with Uncertainty

    Probability and statistics: the core of the discipline.
Stage 2

Year 2 · Working with data

4 readings · 47 min
  1. 05ReadInteractiveQuiz13 min

    Data Wrangling & Exploratory Analysis: Look Before You Model

    Most of the job: getting data clean and looking at it first.
  2. 06ReadInteractiveQuiz11 min

    Data Visualization & Communication: A Chart Is an Argument

    A finding no one understands has not been found.
  3. 07ReadInteractiveQuiz12 min

    Data Structures: How Arrangement Decides Speed

    How data is held in memory, and why it decides speed.
  4. 08ReadInteractiveQuiz11 min

    Databases: Why Your Data Survives a Power Cut

    Where real data lives, and how to ask it questions.
Stage 3

Year 3 · Modelling

4 readings · 50 min
  1. 09ReadInteractiveQuiz13 min

    Regression & Statistical Modeling: The Line, and What It Means

    Regression: the workhorse model and how to read it.
  2. 10ReadInteractiveQuiz14 min

    Experiments & Causal Inference: Why a Coin Flip Beats a Clever Model

    Correlation is not causation; experiments and the methods that stand in for them.
  3. 11ReadInteractiveQuiz12 min

    Econometrics: How to Find What Caused What

    How economists draw causes out of observational data.
  4. 12ReadInteractiveQuiz11 min

    Machine Learning: Programs That Write Their Own Rules

    Machine learning: prediction from data.
Stage 4

Year 4 · At scale and in the world

4 readings · 51 min
  1. 13ReadInteractiveQuiz14 min

    Deep Learning: How Stacked Simple Layers Learn to See

    Neural networks and why they took over.
  2. 14ReadInteractiveQuiz12 min

    Big Data & Data Engineering: What Changes When Data Outgrows One Computer

    When data outgrows one computer.
  3. 15Deep diveInteractiveQuiz15 min

    Data Ethics, Privacy & Fairness: Who a Model Can Harm

    Privacy, bias and who a model can harm.
  4. 16ReadInteractiveQuiz10 min

    Algorithms: Why Growth Beats Speed

    Algorithms and their cost, when the data is huge.
When you finish

the textbooks named at the end of each subject. See also the library's How Large Language Models Actually Work (29/02) and Computational Linguistics (36/30).

Study it in depth, for free

These pieces give you the intuition. To learn the major properly, these are the courses and texts universities use, free to read or audit, and the standard textbooks if you want to own one. Links checked November 2026.

The whole major, free: OSSU Data Science is a free self-taught degree-style path covering programming, statistics, machine learning and data engineering; follow it and use the books and MIT courses below as companions.

Free courses and texts

  1. Introduction to Computer Science and Programming in Python · MIT OCW, 6.0001 · Python programming; year 1
  2. Introduction to Computational Thinking and Data Science · MIT OCW, 6.0002 · simulation, statistics, clustering; year 1-2
  3. Introduction to Probability and Statistics · MIT OCW, 18.05 · probability, Bayesian and frequentist inference; year 1-2
  4. Linear Algebra · MIT OCW, 18.06SC · the maths behind most data methods; year 1-2
  5. OpenStax Introductory Statistics · OpenStax · free textbook for statistics basics; year 1
  6. Think Stats 2e · Allen Downey, Green Tea Press · statistics with Python, free to read; year 2
  7. Python Data Science Handbook · Jake VanderPlas · NumPy, pandas, Matplotlib, scikit-learn; year 2
  8. R for Data Science (2e) · Wickham, Çetinkaya-Rundel & Grolemund · data wrangling and visualisation in R; year 2
  9. An Introduction to Statistical Learning · James, Witten, Hastie & Tibshirani · regression to machine learning, free PDF; year 2-3
  10. Statistics for Applications · MIT OCW, 18.650 · mathematical statistics; year 3
  11. Introduction to Machine Learning · MIT OCW, 6.036 · ML methods; year 3
  12. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning · MIT OCW, 18.065 · SVD, optimisation, deep learning maths; year 3-4
  13. The Analytics Edge · MIT OCW, 15.071 · applied analytics in R; year 3-4
  14. Statistics and Probability · Khan Academy · short practice-based lessons to shore up the statistics; year 1

The standard textbooks

  • An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani) · free PDF at statlearning.com; applied ML
  • The Elements of Statistical Learning (Hastie, Tibshirani & Friedman) · deeper theory; free PDF from the authors
  • Statistical Inference (Casella & Berger) · the standard mathematical statistics text
  • Pattern Recognition and Machine Learning (Christopher Bishop) · probabilistic ML
  • Introduction to Linear Algebra (Gilbert Strang) · linear algebra for data
  • Python for Data Analysis (Wes McKinney) · pandas workflow; for self-learners
  • Designing Data-Intensive Applications (Martin Kleppmann) · data engineering and systems
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