Statistics & Probability for Data Science
Master descriptive statistics, probability theory, distributions, hypothesis testing and statistical inference for data science.
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Course Content
Foundations
Core concepts and groundwork
Measures of Central Tendency — Mean, Median, Mode
Reading
Measures of Spread — Variance, Std Dev, IQR
Reading
Skewness, Kurtosis and Data Shape
Reading
Five-Number Summary and Boxplots
Reading
Covariance and Pearson Correlation
Reading
Practice — Descriptive Stats on Sales Data
Exercise
Core Skills
Essential techniques and patterns
Sample Space, Events and Probability Axioms
Reading
Conditional Probability and Independence
Reading
Bayes Theorem and Its Applications
Reading
Counting — Permutations and Combinations
Reading
Law of Total Probability and Chain Rule
Reading
Practice — Probability Simulations in Python
Exercise
Applied Practice
Hands-on, real-world scenarios
Discrete Distributions — Binomial and Poisson
Reading
Continuous Distributions — Normal and Exponential
Reading
Central Limit Theorem and Sampling
Reading
Fitting Distributions to Data with scipy
Reading
Beta, Gamma and Other Key Distributions
Reading
Practice — Distribution Fitting on Real Data
Exercise
Advanced Topics
Deeper, more complex material
Population vs Sample and Sampling Bias
Reading
Confidence Intervals — Construction and Meaning
Reading
Hypothesis Testing — Null and Alternative
Reading
p-values, Significance Level and Type I/II Errors
Reading
Power Analysis and Sample Size Calculation
Reading
Practice — A/B Test Analysis in Python
Exercise
Production & Scale
Building for the real world
t-tests — One-Sample, Two-Sample, Paired
Reading
Chi-Square Test for Independence
Reading
ANOVA — One-Way and Two-Way
Reading
Non-Parametric Tests — Mann-Whitney, Kruskal
Reading
Correlation Tests — Spearman, Kendall
Reading
Practice — Choose and Apply the Right Test
Exercise
Mastery & Capstone
Projects and final review
Project Brief — Analyse a Clinical Trial Dataset
Reading
Compute Descriptive Stats and Distributions
Exercise
Run Hypothesis Tests and Interpret p-values
Exercise
Build Confidence Intervals for Key Metrics
Exercise
Capstone — Submit Statistics Analysis Project
Project
100% Free · All lessons unlocked
Topic Overview
What you'll learn
- Core concepts and fundamentals of Statistics & Probability for Data Science
- Industry best practices and design patterns
- Hands-on exercises with real-world scenarios
- Performance optimization and advanced techniques
- Building production-ready applications