HarvardX: Data Science: Capstone course Syllabus

Overview: This capstone course offers a comprehensive, end-to-end data science project experience that challenges learners to apply foundational skills to a real-world problem. Over approximately 8–10 weeks with a time commitment of 8–10 hours per week, you will progress through key stages of a data science workflow—from problem definition to final presentation. Emphasis is placed on practical application, reproducibility, and professional communication, culminating in a portfolio-ready project that demonstrates job-ready skills. This course synthesizes knowledge from the Harvard Data Science series and prepares learners for real-world analytics roles.

Module 1: Capstone Project Definition and Planning

Estimated time: 8 hours

• Define a clear data science problem statement and success criteria
• Explore and assess real-world datasets for relevance and quality
• Plan an analytical approach and methodology
• Set up reproducible workflows and project structure

Module 2: Data Wrangling and Exploratory Analysis

Estimated time: 16 hours

• Clean and preprocess messy, large-scale datasets
• Handle missing values, outliers, and inconsistent formatting
• Perform exploratory data analysis (EDA) to identify patterns
• Select and engineer features relevant to the modeling task

Module 3: Modeling and Evaluation

Estimated time: 16 hours

• Build predictive models using appropriate algorithms
• Compare model performance using valid metrics
• Iterate and tune models for improved results
• Interpret model outputs and understand limitations

Module 4: Final Presentation and Reporting

Estimated time: 10 hours

• Summarize findings and translate technical results into insights
• Develop clear visualizations and narrative structure
• Deliver a professional presentation of methodology and outcomes

Module 5: Professional Workflows and Best Practices

Estimated time: 6 hours

• Apply version control and documentation standards
• Ensure reproducibility of analysis and modeling steps
• Follow ethical guidelines in data handling and reporting

Module 6: Final Project

Estimated time: 24 hours

• Deliverable 1: A well-documented data science report
• Deliverable 2: A working predictive model with evaluation metrics
• Deliverable 3: A final presentation showcasing end-to-end project work

Prerequisites

• Completion of foundational courses in data science (e.g., R or Python programming, data visualization, statistics)
• Familiarity with data wrangling and exploratory data analysis techniques
• Basic understanding of machine learning concepts and modeling workflows

What You'll Be Able to Do After

• Apply end-to-end data science skills to real-world problems
• Clean, preprocess, and analyze complex, messy datasets
• Build, evaluate, and iterate on predictive models
• Communicate technical findings effectively through reports and presentations
• Demonstrate professional data science workflows in portfolios and interviews