Full syllabus

Beginner

Python Programming Basics

  1. Syntax, Variables and Data Structures
  2. Functions, Modules and Files
  3. Notebooks and Environments

Git and the Command Line

  1. Command Line Basics
  2. Git and GitHub

Statistics and Probability Intuition

  1. Descriptive Statistics
  2. Probability and Distributions
  3. Sampling, Confidence and Hypothesis Tests

Intermediate

SQL for Data Science

  1. SELECT, Filtering, Joins and Aggregation
  2. Window Functions and CTEs

NumPy, pandas, EDA and Visualisation

  1. NumPy and pandas Essentials
  2. Exploratory Data Analysis
  3. Data Visualisation

Classic Machine Learning

  1. Regression and Classification Models
  2. Model Evaluation and Validation
  3. Feature Engineering

Advanced

Experimentation and A/B Testing

  1. Designing an A/B Test
  2. Analysing Results and Common Pitfalls

Deep Learning Overview and Communicating Results

  1. Neural Networks and Deep Learning Basics
  2. Storytelling and Communicating Results

Data Science Practice and Interview Prep

  1. The Data Science Interview Loop
  2. Take-Home Assignments and Portfolio Projects