Grokking Data Science Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This course takes you from Python basics to executing a complete machine learning project, all within a hands-on, in-browser environment. With approximately 60 hours of interactive content, you'll build practical data science skills through coding exercises, quizzes, and a capstone project—designed to prepare you for real-world roles and portfolio development.

Module 1: Python Fundamentals for Data Science

Estimated time: 20 hours

Python basics and syntax
NumPy array operations
Pandas data manipulation
Foundational data visualization techniques using Matplotlib

Module 2: The Fundamentals of Statistics

Estimated time: 15 hours

Statistical features and descriptive statistics
Probability concepts and rules
Common distributions: Uniform, Binomial, Normal, Poisson
Significance testing and hypothesis interpretation

Module 3: Machine Learning 101

Estimated time: 12 hours

Types of machine learning algorithms
Supervised vs. unsupervised learning
Model evaluation metrics
Performance assessment and trade-offs

Module 4: End-to-End Machine Learning Project

Estimated time: 10 hours

Exploratory data analysis (EDA)
Data preprocessing and feature engineering
Model selection, training, and fine-tuning
ML pipeline maintenance and deployment considerations

Module 5: The Real Talk

Estimated time: 3 hours

Career success strategies in data science
Overcoming imposter syndrome
Continuous learning and growth paths

Module 6: Final Project