Probability for Data Science
Distributions, Bayes and expected value — with Python simulations.
Taught by Bibek Sharma · Data Scientist
What you'll learn
- Calculate conditional probabilities and test whether two events in a dataset are independent
- Apply Bayes' theorem to diagnostic tests, fraud flags and classifier precision while accounting for base rates
- Compute expected value and variance for random variables and use them to compare business decisions
- Choose the right distribution for a scenario (binomial, Poisson, geometric, normal, exponential) and query it with scipy.stats
- Demonstrate the central limit theorem with NumPy simulations and identify the situations where it breaks down
- Solve classic probability interview problems and verify each answer with a short Python simulation
About this course
Probability for Data Science is an intermediate course for analysts, aspiring data scientists and engineers who want to reason clearly about uncertainty instead of memorising formulas. You will learn conditional probability, independence and Bayes' theorem, work with random variables, expected value and variance, and get comfortable with the probability distributions data scientists meet every day: binomial, Poisson, geometric, normal and exponential. Every idea is checked with short Python simulations in NumPy and SciPy, so you can watch the maths behave before you trust it. You will see the central limit theorem emerge from heavily skewed data, learn when it fails, and finish by solving classic probability interview questions such as Monty Hall and the birthday problem. Prerequisites are comfortable Python basics and school algebra. The course pairs naturally with Statistics for Data Science, which builds on these ideas for confidence intervals and hypothesis testing.
Course content
7 lessons- 1Probability as a Language for UncertaintySample spaces, events and the three rules every probability obeys, checked with a first simulation.Preview
- 2Conditional Probability and IndependenceHow new information changes probabilities, when events are independent, and the law of total probability.
- 3Bayes' Theorem and Thinking in Base RatesFlip conditional probabilities around, respect base rates, and update beliefs as evidence arrives.
- 4Random Variables, Expected Value and VarianceTurn outcomes into numbers, compute averages and spread, and use expected value to make decisions.
- 5The Distributions Data Scientists Actually UseBernoulli, binomial, Poisson, geometric, normal and exponential: what each models and how to use it in SciPy.
- 6The Central Limit Theorem by SimulationWatch averages of skewed data turn normal, and learn when the theorem does and does not rescue you.
- 7Probability Interview Problems, Solved Two WaysClassic puzzles from data science interviews, solved with reasoning first and checked with simulation.