Published Courses on Data Science
Self-paced, intuition-first courses on data science and statistics — for anyone who prefers to learn by watching and doing, at their own pace.
Statistics Fundamentals for Data Science
Understand distributions, hypothesis testing, and statistical tests, the intuitive way
Statistics is the backbone of data science, but it's often taught as a wall of formulas that's easy to memorize and hard to truly understand. This course builds your intuition first, so every concept clicks before the math ever comes into it — from the core distributions to hypothesis testing and the statistical tests used in real practice.
- Understand how statistics uses a sample to draw and defend conclusions about a population
- Read and interpret the normal distribution, CLT, t-distribution, chi-square, and F-distribution
- Choose the right statistical test for any problem, from t-tests to ANOVA and chi-square
- Run a hypothesis test end to end: hypotheses, statistic, p-value, assumptions, decision
- 10 sections · 46 lectures · 3h · No prior statistics background needed
Introduction to HR Analytics for Non-Technical Teams
Understand distributions, group comparisons, and statistical significance in HR data
HR teams sit on more data than they know what to do with, but turning that data into a chart people actually trust is a skill most HR professionals were never taught. This course builds that skill from the ground up, using one real company dataset throughout — from reading a single distribution to comparing groups, asking the right questions, and presenting findings that hold up.
- Understand how a distribution tells you whether a number like "average salary: €60,000" is actually meaningful<\li>
- Read and compare data using bar, line, scatter, and slope charts — the core visual toolkit of HR analytics Judge whether a gap between two groups is a real pattern or just noise — the intuitive way, with no formulas
- Go from a business question to a chart that answers it honestly, and avoid the three most common ways a chart can mislead
- 11 sections · 56 lectures · 3h · No prior statistics background needed