Harvard University: Data Science: Building Machine Learning Models Syllabus

Overview: This course provides a comprehensive introduction to machine learning within the context of data science, designed for learners with some background in statistics and programming. Through a blend of lectures, hands-on labs, and real-world case studies, you'll build foundational skills in data exploration, model development, and evaluation. The course spans approximately 14–16 hours of content across six modules, combining theory with practical application using industry-standard tools. Ideal for professionals and students aiming to advance in data science roles, this course prepares you to tackle real-world machine learning challenges with confidence.

Module 1: Data Exploration & Preprocessing

Estimated time: 3 hours

• Case study analysis with real-world datasets
• Best practices in data cleaning and transformation
• Introduction to exploratory data analysis workflows
• Techniques for handling missing and inconsistent data

Module 2: Statistical Analysis & Probability

Estimated time: 2 hours

• Foundations of probability for data science
• Statistical methods for inference and estimation
• Applying statistics to extract insights from data
• Interactive lab: Solving practical data problems

Module 3: Machine Learning Fundamentals

Estimated time: 4 hours

• Introduction to key machine learning concepts
• Supervised vs. unsupervised learning techniques
• Hands-on exercises with ML algorithms
• Best practices in model selection and training

Module 4: Model Evaluation & Optimization

Estimated time: 3 hours

• Techniques for evaluating model performance
• Overfitting, bias, and variance trade-offs
• Review of common tools and frameworks
• Strategies for hyperparameter tuning

Module 5: Data Visualization & Storytelling

Estimated time: 2 hours

• Principles of effective data visualization
• Using visuals to communicate insights
• Interactive lab: Building storytelling dashboards
• Guided project with instructor feedback

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 4 hours

• Introduction to advanced analytics techniques
• Feature engineering for improved model performance
• Review of frameworks used in production environments
• Interactive lab: End-to-end pipeline development

Prerequisites

• Basic knowledge of statistics and probability
• Familiarity with programming (Python or R recommended)
• Understanding of fundamental data science concepts

What You'll Be Able to Do After

• Work with large-scale datasets using industry-standard tools
• Design end-to-end data science pipelines for production
• Apply statistical methods to extract insights from complex data
• Build and evaluate machine learning models using real-world data
• Implement data preprocessing and feature engineering techniques