Supervised vs. Unsupervised Learning

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This lesson establishes the map you'll use for the rest of the course: supervised learning (regression, classification) versus unsupervised learning (clustering, dimensionality reduction), and when each applies. You'll see real business scenarios mapped to the correct category before any algorithm-specific detail.

Getting this framing right early prevents a common beginner mistake — reaching for a classification algorithm on a problem that's actually a clustering problem, or vice versa.

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