Data Science / Health-related Short Courses — Harvard T.H. Chan / Medical School
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Description
Curriculum
Instructor
https://www.edx.org/learn/r-programming/harvard-university-data-science-r-basics
Course Overview
| Item | Description |
|---|---|
| Course Title | Data Science / Health-related Short Courses |
| Institution | Harvard T.H. Chan School of Public Health / Harvard Medical School |
| Platform | edX |
| Instructor | Various Harvard Faculty (subject-specific) |
| Level | Beginner to Advanced (depends on specific course) |
| Delivery Type | Self-paced, Fully Online |
| Certificate | Verified Certificate available |
| Access to Content | Free (audit mode) |
| Duration | 4–12 weeks (varies by course) |
| Effort Required | 3–10 hours per week |
| Prerequisites | Varies; some courses require basic statistics or programming knowledge |
2. Learning Outcomes (General)
| No. | Learning Outcome |
|---|---|
| 1 | Understand fundamental concepts in data science applied to health |
| 2 | Perform statistical analysis using R or Python |
| 3 | Apply data visualization techniques for health datasets |
| 4 | Use machine learning methods for predictive modeling in health contexts |
| 5 | Analyze epidemiological and clinical data |
| 6 | Apply data-driven decision-making in public health |
| 7 | Understand bioinformatics and genomic data analysis (selected courses) |
| 8 | Complete a final data project or case study (capstone) |
3. Example Modules (Typical)
| Week | Module Title | Topics Covered | Project/Exercise |
|---|---|---|---|
| 1 | Introduction to Data Science | Data types, software setup, basics of R/Python | Exploratory data analysis |
| 2 | Statistical Methods | Regression, hypothesis testing | Analysis of sample health datasets |
| 3 | Data Visualization | Plots, dashboards, reporting | Create visual report |
| 4 | Machine Learning | Supervised/unsupervised learning, classification | Predictive health model |
| 5 | Epidemiology | Disease modeling, incidence/prevalence | Epidemiological case study |
| 6 | Bioinformatics (Optional) | Genomics data analysis | Genomic dataset analysis |
| 7–8 | Capstone Project | Integrative project using course data | Final report & presentation |
4. Course Materials
| Material Type | Description |
|---|---|
| Video Lectures | Harvard faculty recorded lectures |
| Readings | Research papers, case studies, textbooks |
| Labs / Exercises | Hands-on coding and data analysis |
| Discussion | Peer forums and faculty guidance |
| Capstone | Applied project in data science and health |
5. Skills Gained
| Category | Skills |
|---|---|
| Programming | R, Python, data manipulation |
| Statistics | Regression, hypothesis testing, predictive modeling |
| Visualization | Graphs, dashboards, health reporting |
| Epidemiology | Disease modeling, public health analytics |
| Bioinformatics | Optional genomic data analysis |
| Project Management | Design and implement applied data project |
6. Assessment Structure
| Component | Weight / Importance |
|---|---|
| Labs / Assignments | 40–50% |
| Quizzes | 10–20% |
| Capstone Project | 30–40% |
| Participation | 10% (if cohort-based) |
7. Certificate Information
| Item | Description |
|---|---|
| Issuer | HarvardX / edX |
| Verification | Unique serial number and URL |
| Format | Digital certificate |
| Yes | |
| Credential Type | Verified Certificate |
8. Summary
These courses equip learners with data science and analytical skills specifically applied to health and public health contexts.
Learners gain hands-on experience in programming, statistical analysis, machine learning, and epidemiology, culminating in a capstone project that demonstrates the ability to use data to solve real-world health challenges.
1 Student8 Courses
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