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Statistics with Python

4 mini courses, 14 lessons: describing data, probability, inference and A/B tests, and correlation

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14 lessons · ~3 hours · 28-question final exam · pass mark 70%

About the course

What you’ll learn

The statistics every developer, analyst and ML engineer needs, with Python instead of tables of formulas. Summarise and plot data, reason about probability and Bayes' theorem, understand distributions and the central limit theorem, build confidence intervals, run hypothesis tests and A/B tests, and avoid classic traps like confusing correlation with causation. Pass every lesson quiz and the final exam to earn a verifiable certificate.

Descriptive statisticsProbabilityStatistical inferenceA/B testingData analysis

Part of the career tracks: Data Analyst, Machine Learning Engineer

How the certificate works

  1. Read each lesson’s study guide, then the full lesson and any notebooks or exercises.
  2. Pass every lesson quiz with 70% or more.
  3. Pass the final exam: 28 questions in 42 minutes.
  4. Get a certificate with a public verification link, and add it to LinkedIn.

Syllabus

14 lessons in 4 mini courses

Each mini course takes about an hour. Already know one? Test out in the app and skip it.

1. Describing data

Summarise and visualise a dataset honestly.

1
Data, Populations and Samples
10 min · Population: everyone you care about; sample: those you measure.
2
Mean, Median and Mode
10 min · Mean: sum ÷ count; median: middle value; mode: most common.
3
Spread: Range, Variance and Standard Deviation
10 min · Range = max − min; sensitive to extremes.
4
Visualising Distributions
10 min · Histograms show shape: symmetric, skewed, bimodal.

2. Probability

Calculate probabilities, use Bayes' theorem and recognise common distributions.

5
Probability Rules
10 min · Probabilities run from 0 to 1.
6
Bayes' Theorem
12 min · Bayes: P(A | B) = P(B | A) P(A) / P(B).
7
Random Variables and Expected Value
10 min · A random variable gives a number to each outcome.
8
Binomial, Poisson and Normal
12 min · Binomial: successes in n tries with probability p; mean np.

3. Inference and testing

Build confidence intervals, run hypothesis tests and read A/B test results.

9
Sampling Distributions and the CLT
12 min · Sample means form a sampling distribution.
10
Confidence Intervals
10 min · A confidence interval shows the uncertainty of an estimate.
11
Hypothesis Tests and p-values
12 min · H₀: no effect; the alternative: some effect.
12
A/B Testing
12 min · A/B tests randomly assign users to versions and compare a metric.

4. Relationships in data

Measure relationships and avoid confusing correlation with causation.

13
Correlation Is Not Causation
10 min · r runs from −1 to 1 and measures straight-line relationships.
14
Regression in Practice
12 min · Regression predicts y from features; coefficients hold others constant.

Written by Vikosha. Lessons, questions and exercises are original. Licence: Original Vikosha course..