From sample spaces and random variables through the Central Limit Theorem, statistical inference, and Bayesian reasoning — the mathematical toolkit every quantitative builder needs.
Probability and statistics are inseparable: probability gives you the model, statistics lets you learn it from data. This course moves from the foundational axioms through distributions, expectation, limit theorems, hypothesis testing, and Bayesian inference — each module centered on worked problems you can run. No prior calculus is assumed for the early modules; multivariable calculus becomes useful in Module 5 onward.
Built by Lakshya Kumar
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Sign in to applyComplete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Identify a measurable phenomenon you care about, collect or simulate data, perform a full statistical analysis (exploratory, hypothesis test, and Bayesian update), and write a 6–10 page report defending your methodology and conclusions.
I'm studying Probability & Statistics: sample spaces, random variables, expectation, common distributions (Binomial, Normal, Poisson, Exponential, Beta, Gamma), the Central Limit Theorem, hypothesis testing (t-tests, chi-squared, ANOVA, multiple testing), and Bayesian inference (priors, posteriors, MCMC). Context: 1. My background: [e.g. "software engineer who uses ML libraries but doesn't understand the math", "data analyst who runs tests but doesn't understand p-values"] 2. What I want to be able to do: [e.g. "design A/B tests I trust", "understand confidence intervals", "build Bayesian models in PyMC"] 3. One concept I'm stuck on: [e.g. "I can calculate p-values but don't understand what they mean"] Answer: - Given my background, should I start at Module 1 or skip ahead? - For the concept in (3): give me a concrete two-paragraph intuition plus a Python snippet I can run. - What's the single most important practical skill from this course for goal (2)?
Concise graduate-level reference. Pair with Modules 6–10.