Probability Distributions in Practice

Descriptive Statistics That Actually MatterPrev Hypothesis Testing and p-valuesNext

Normal, binomial, and Poisson distributions aren't abstract math exercises — they describe real patterns in customer behavior, defect rates, and event frequencies. This lesson explains how to recognize which distribution fits a given dataset and why that choice affects which statistical tests are valid to run.

You'll also cover the Central Limit Theorem and why it underpins so much of inferential statistics, including confidence intervals used later in A/B test analysis.

Python for Data Science

Setting Up Your Data Science Environment NumPy Arrays and Vectorized Operations Data Wrangling with pandas Exploratory Data Analysis and Visualization

Statistics and Probability Foundations

Descriptive Statistics That Actually Matter Probability Distributions in Practice Hypothesis Testing and p-values

SQL for Data Analysis

Querying and Filtering Data with SQL Joins, Aggregations, and Window Functions Writing Efficient, Readable Queries

Machine Learning Fundamentals

Supervised vs. Unsupervised Learning Linear and Logistic Regression Decision Trees and Ensemble Methods Model Evaluation and Overfitting

Feature Engineering and Data Preparation

Handling Missing Data and Outliers Encoding and Scaling Features Feature Selection and Dimensionality Reduction

Career Prep and Data Science Interviews

Common Data Science Interview Questions Explaining Technical Results to Non-Technical Stakeholders Career Paths, Salary Expectations, and Next Steps