Encoding and Scaling Features

Handling Missing Data and OutliersPrev Feature Selection and Dimensionality ReductionNext

Most machine learning algorithms require numeric input, which means categorical variables need encoding and numeric variables often need scaling. This lesson covers one-hot encoding, label encoding, and when each is appropriate, along with standardization and normalization for numeric features.

You'll also cover the data leakage trap that catches many beginners: fitting a scaler on the full dataset instead of the training set alone.

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