Setting Up Your Data Science Environment

NumPy Arrays and Vectorized OperationsNext

Before writing a single line of analysis code, you need a working environment: Python, a package manager, and an interactive notebook. This lesson walks through installing Anaconda or a lightweight pip-based setup, and explains why Jupyter notebooks became the default workspace for exploratory data work rather than a plain script editor.

You'll also learn the core libraries you'll rely on throughout the course — NumPy, pandas, and matplotlib — and why each exists as a separate tool rather than one monolithic package.

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