Mathew K AnalyticsProbability and Statistics in python
A free 35-lesson course. Every lesson has a video walkthrough and a Jupyter notebook you can download or open in Google Colab. Tools covered: NumPy, pandas, Matplotlib, seaborn, SciPy, scikit-learn.
25 minLesson 2
Setting Up Python Jupyter Notebooks for Probability and Statistics Analysis
7 minLesson 3
Understanding Data Types: Categorical, Numerical, Continuous, and Discrete Explained
10 minLesson 4
Master Descriptive Statistics: Learn Mean, Median, and Mode in Python
15 minLesson 5
Measures of Spread Explained: Variance, Standard Deviation & Interquartile Range
14 minLesson 6
Fundamentals of Data Visualization: Histograms, Boxplots, and Scatterplots Explained
11 minLesson 7
Foundations of Probability: Understanding Events, Outcomes, and Sample Space
11 minLesson 8
Understanding Conditional Probability and Independence in Probability Theory
24 minLesson 9
Master Bayes Theorem: Real-Life Examples & Step-by-Step Probability Explained
9 minLesson 10
Understanding Bernoulli, Binomial, and Poisson Discrete Probability Distributions
14 minLesson 11
Understanding Continuous Probability Distributions: Uniform, Normal, and Exponential Explained
13 minLesson 12
Understanding Statistical Sampling and the Law of Large Numbers in Biblical Context
14 minLesson 13
Understanding the Central Limit Theorem: Key Concepts and Practical Applications
12 minLesson 15
Hypothesis Testing Explained: Concepts, Errors, and How to Interpret P-Values in Python
11 minLesson 16
Understanding t-Tests, Chi-Square Tests, and ANOVA in Python for Statistical Analysis
16 minLesson 17
Understanding Bootstrapping for Estimation and Confidence Intervals in Statistics
12 minLesson 18
Understanding Permutation Tests: A Flexible Approach to Hypothesis Testing
17 minLesson 19
Understanding Monte Carlo Simulations in Python for Statistical Modeling and Analysis
21 minLesson 20
Mastering Random Number Generation in Python Using NumPy: Techniques & Applications
29 minLesson 21
Practical Applications of Simulation Techniques in Statistical Analysis and Decision-Making
13 minLesson 22
Understanding Covariance and Correlation: Pearson and Spearman Explained
12 minLesson 23
Simple Linear Regression in Python: Model Fitting & Interpretation Guide
11 minLesson 24
Understanding Multiple Linear Regression with Python’s Statsmodels Library
12 minLesson 25
Understanding Regression Diagnostics: Residuals and Key Assumptions Explained
10 minLesson 26
Practical Regression Project: Predicting Housing Prices with Python and Data Analysis
12 minLesson 28
Logistic Regression Explained: Master Coefficients & Odds Ratios Easily
14 minLesson 29
Master Accuracy, Precision & Recall for Evaluating Classification Models in Python
12 minLesson 30
Understanding ROC Curves and AUC for Evaluating Classification Models in Python
24 minLesson 33
Understanding Stationarity, ACF, PACF, and ARIMA Fundamentals in Time Series Analysis
24 minLesson 34
Understanding Bayesian Statistics: Foundations of Priors and Posteriors Explained
15 minLesson 35
Bayesian Estimation Explained: Using SciPy and PyMC for Practical Inference in Python
21 minLesson 36
Practical Applications of Bayesian Inference: Step-by-Step Examples and Analysis
16 minLesson 37
Understanding the Law of Large Numbers Through Dice Roll Simulations in Python
19 minLesson 38
Hypothesis Testing Explained: Applying Statistical Methods to Real-World Data
12 minLesson 40
Comprehensive Time Series Forecasting Project: From Data Preparation to Deployment
34 minLesson 41