The Master of Science (SM) in Health Data Science is designed to provide rigorous quantitative training and essential statistical and computing skills needed to manage and analyze health science data to address important questions in public health and biomedical sciences.
The 16-month program blends strong statistical and computational training to solve emerging problems in public health and the biomedical sciences. This training will enable students to manage and analyze massive, noisy data sets and learn how to interpret their findings. The program will provide training in three principal pillars of health data science: statistics, computing, and health sciences.
Students in the program will learn to:
1. Wrangle and transform data to perform meaningful analyses;
2. Visualize and interpret data and effectively communicate results and findings
3. Apply statistical methods to draw scientific conclusions from data
4. Utilize statistical models and machine learning
5. Apply methods for big data to reveal patterns, trends, and associations
6. Employ high-performance scientific computing and software engineering
7. Collaborate with a team on a semester-long, data driven research project
The SM in Health Data Science is designed to be a terminal professional degree, giving students essential skills for the job market. At the same time, it provides a strong foundation for students interested in obtaining a PhD in biostatistics or other quantitative or computational science with an emphasis in data science and its applications in health science.
Prerequisites:
Candidates for admission to the SM in Health Data Science program should have successfully completed the following:
1. An undergraduate degree in mathematical sciences or allied fields (e.g. statistics, computational biology, mathematics, economics, statistics, computer science, physics, or engineering), with a strong interest in health science
2. Practical knowledge of computer scripting and programming as well as some experience with a statistical computing package such as R or Python
3. Calculus through multivariable integration
4. One semester of linear algebra or matrix methods
5. Applicants should also show excellence in written and spoken English
Test Scores:
GRE: The Graduate Record Examination (GRE) is required of all applicants.
TOEFL: The Harvard Chan School requires a minimum of 600 on the paper-based test, 250 on the computer-based test, and 100 on the Internet-based 4-part test (we prefer individual section scores of 23 or better). Official scores from TOEFL may be reported to the School at code number 3456. If you have already started your SOPHAS application, please report your scores to code 5688.
IELTS: The International English Language Testing System (IELTS) will also be accepted with a score of 7.0 or greater. Please have an official score report sent directly to the Harvard Chan School.
| 序号 | 课程介绍 | Curriculum |
| 1 | 统计推断基础 | Basics of Statistical Inference |
| 2 | 数据科学概论 | Introduction to Data Science |
| 3 | 数据科学2 | Data Science II |
| 4 | 大数据计算 | Computing for Big Data |
| 5 | 应用机器学习 | Applied Machine Learning |
| 6 | 健康数据科学实践 | Health Data Science Practice |
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