Statistics for Data Science
The statistics every data scientist is expected to actually understand — not just import from scipy. Probability foundations, sampling distributions, hypothesis testing properly (with multiple-testing corrections), bootstrap, linear and logistic regression with assumptions, Bayesian intuition, and a causal-inference primer. Six hands-on DS cases. The natural next course after Stats & Product Analytics for Analysts.
4 weeks
4 modules, 32 lessons
2
~7 hours
100% Free Structured Micro Course
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Course Curriculum
Foundations: From Intuition to MathematicsFREE PREVIEW
Probability, random variables, distributions, sampling distributions, the Central Limit Theorem, and standard errors. The bridge from DA-level intuition to DS-level rigor.
Inference: Hypothesis Testing and BeyondFREE PREVIEW
Hypothesis testing framework, t/z/chi-square tests, multiple testing (Bonferroni, FDR), and bootstrap inference for non-parametric problems.
Modeling: Regression and BeyondFREE PREVIEW
Linear regression with assumptions, logistic regression and the GLM family, Bayesian inference, and a causal-inference primer (confounders, DAGs).
Live DS Statistical CasesFREE PREVIEW
Six interview-grade walkthroughs: diagnosing a 'significant' A/B test that isn't, bootstrap CIs for skewed metrics, fitting and interpreting churn logistic, power analysis, confounder detection, and Bayesian product reasoning.
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All videos, lessons, and practice exercises are 100% free with zero gatekeeping. Start learning immediately, or enroll with the ₹99 Verified Certificate Pass.