Data Science course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This Professional Certificate offers a comprehensive, university-level introduction to data science, designed by Harvard to build strong foundations in statistics, programming, and data analysis. The program spans approximately 16–24 weeks with a recommended 8–10 hours per week, combining theoretical concepts with hands-on practice using real-world datasets. Learners will gain proficiency in R, statistical inference, regression, machine learning, and complete a capstone project demonstrating end-to-end data science skills. Lifetime access ensures flexible, self-paced learning aligned with career advancement goals.
Module 1: Data Science Foundations
Estimated time: 32 hours
- Introduction to R programming basics
- Data wrangling and transformation techniques
- Data visualization using ggplot2
- Developing statistical thinking skills
Module 2: Probability and Inference
Estimated time: 32 hours
- Fundamentals of probability theory
- Statistical inference and hypothesis testing
- Confidence intervals and p-values
- Applying statistical reasoning to datasets
Module 3: Regression and Machine Learning
Estimated time: 32 hours
- Linear regression modeling
- Supervised machine learning basics
- Cross-validation and model evaluation
- Predictive analytics techniques
Module 4: Data Analysis and Visualization
Estimated time: 24 hours
- Exploratory data analysis
- Advanced visualization with ggplot2
- Interpreting patterns in real-world data
Module 5: Inference and Modeling
Estimated time: 24 hours
- Statistical modeling principles
- Prediction and uncertainty quantification
- Case studies in inferential reasoning
Module 6: Final Project
Estimated time: 40 hours
- Clean and preprocess a real-world dataset
- Analyze and model data using R
- Publish a report with visualizations and insights
Prerequisites
- Basic algebra and mathematical reasoning
- Comfort with logical thinking and problem-solving
- No prior programming experience required, but familiarity with R is beneficial
What You'll Be Able to Do After
- Write R code for data manipulation and analysis
- Apply statistical methods to real-world datasets
- Build and evaluate regression and machine learning models
- Create publication-quality data visualizations
- Demonstrate data science competence through a portfolio-ready capstone project