Harvard University: Data Science: R Basics Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Data Exploration & Preprocessing

Estimated time: 4 hours

• Review of tools and frameworks commonly used in data science practice
• Best practices in data exploration and preprocessing
• Hands-on data cleaning and transformation techniques
• Working with real-world datasets in R

Module 2: Statistical Analysis & Probability

Estimated time: 1-2 hours

• Fundamentals of probability and statistical inference
• Applying statistical methods using R
• Review of industry-standard statistical frameworks
• Interactive practice with real data examples

Module 3: Machine Learning Fundamentals

Estimated time: 3 hours

• Introduction to supervised and unsupervised learning
• Building basic machine learning models in R
• Case study analysis with real-world datasets
• Interactive lab: implementing ML solutions

Module 4: Model Evaluation & Optimization

Estimated time: 2 hours

• Techniques for evaluating model performance
• Hyperparameter tuning and optimization strategies
• Case study analysis on model improvement

Module 5: Data Visualization & Storytelling

Estimated time: 3-4 hours

• Creating effective data visualizations in R
• Using visualization tools and frameworks
• Principles of data storytelling and presentation
• Hands-on exercises with real datasets

Module 6: Advanced Analytics & Feature Engineering

Estimated time: 2-3 hours

• Introduction to advanced analytics techniques
• Feature engineering methods in R
• Case study analysis with practical applications
• Interactive lab: building enhanced data models

Prerequisites

• Basic understanding of high school level mathematics
• Familiarity with fundamental statistical concepts
• No prior programming experience required

What You'll Be Able to Do After

• Perform exploratory data analysis using R
• Apply statistical methods to real-world datasets
• Build and evaluate basic machine learning models
• Create compelling data visualizations and narratives
• Implement feature engineering and preprocessing workflows