aNet.ai · 13-week syllabus · Weeks 1–6 available now

Data Science Fundamentals

Framing questions, describing and cleaning data, honest prediction pipelines, EDA, Simpson’s paradox and data visualisation. 6 topics, 67 lessons and 402 practice questions — earn XP, keep your streak, and level up as you go.

Start learning · Data science, AI, machine learning and deep learning

~7 h of study67 lessons to goTopic 1: Thinking Like a Data Scientist

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402 questions across every topic. Answer correctly to push them further out.

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First steps — Complete your first lesson
Warming up — Complete 10 lessons
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Getting started

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Week 0 · Python for Data Science
Python for Data Science — for students new to programming
Lab 0 — Colab and PythonComing soon

Understanding data

0/20 lessons

Preparing data

0/11 lessons

3
Data Cleaning and Quality
Data quality dimensions, cleaning decisions, missing-data mechanisms and audit trails.
0/11 lessons · ~70 min · 66 questionsWeek 3 · 56 slides + Lab 3

From data to insight

0/22 lessons

Visualisation & inference

0/14 lessons

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Data Visualisation
Visual encoding, baselines, histograms and density, colour and accessibility, and honest claims.
0/14 lessons · ~98 min · 84 questionsWeek 6 · 37 slides + Lab 6
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Week 7 · Probability
Probability; conditional direction; Bayes with natural frequencies
Lab 7 — Probability and simulation · Ch. 8Coming soon
Meanwhile, in Machine Learning:Mathematical Foundations
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Week 8 · Sampling and Inference
Sampling variability; confidence intervals; tests; effect size; A/B design
Lab 8 — Intervals, resampling and power · Ch. 9Coming soon

Modelling

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Week 9 · Regression
Baselines; linear regression; metrics; residuals; cross-validation
Lab 9 — The regression workflow · Ch. 10Coming soon
Meanwhile, in Machine Learning:Linear Regression Model Evaluation
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Week 10 · Classification
Logistic regression; k-NN and trees; confusion matrix; thresholds; fairness
Lab 10 — Classification, end to end · Ch. 11, 12Coming soon
Meanwhile, in Machine Learning:Classification Nearest-Neighbor & Probabilistic Methods Decision Trees & Ensemble Learning
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Week 11 · Clustering and Dimensionality Reduction
Distance; k-means; choosing k; PCA; naming responsibly
Lab 11 — Clustering with honest uncertainty · Ch. 13Coming soon
Meanwhile, in Machine Learning:Unsupervised Learning Dimensionality Reduction

Data in the world

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Week 12 · Time Series, Text and Responsible Data
Time series and text; dashboards; privacy; contestability
Lab 12 — Time series, text and a dashboard · Ch. 14, 15Coming soon

Explore

Knowledge graphSee how every topic builds on the others.Lab workFive labs with guided sections linked to the lectures.Final mock exam114 marks, 120 minutes, with model answers and rubrics.Glossary171 key terms explained in one line each.Course resourcesStudy roadmap, code reference, exam traps and the original files.Course explorerThe full syllabus with every lesson.