For COVID-19 vaccine updates, please review our information guide. For patient eligibility and scheduling availability, please visit VaccineTogetherNY.org.

Biostatistics and Data Science

Preparing Students for the Data-Driven Challenges of Today's World

Our Master’s track in Biostatistics and Data Science provides top-class training in biostatistics and data science techniques that are essential to collect, manage, and analyze biomedical and health data.

Close-up of hands typing on keyboard.

Our coursework offers students a foundation for data science careers in health-related fields and beyond. 

Real-World Skills

We provide comprehensive hands-on training in statistical concepts and programming. During the Master’s track in Biostatistics and Data Science, students will:

  • Use state-of-the-art statistical and data science approaches to address modern data challenges.
  • Gain invaluable real-world exposure under the guidance of experienced biostatisticians and data scientists.
  • Build experience in the field through a faculty-mentored research project.
  • Take advantage of NYC’s proximity to leading educational institutions and some of the largest pharmaceutical hubs in the country.
  • Create close professional relationships with a diverse faculty, through low student-to-faculty class ratios.
  • Exposure to specializations such as health services research, cost-effectiveness, and comparative-effectiveness.

Unique Expertise

Our track in Biostatistics and Data Science is unique as it focuses on data mining and machine learning techniques yet retains the rigor of a traditional Biostatistics program.

Students from all over the world join this track with backgrounds in science (e.g., statistics, mathematics, biology, etc.), engineering, health and medicine.

Graduates are prepared for data science careers in the public and private biomedical, healthcare, insurance and pharmaceutical sectors, both in academia and industry.

The Master’s track in Biostatistics and Data Science has close ties to other programs within the Weill Cornell Medical College and Cornell University, the Department of Statistics and Data Science at Cornell University, the Cornell Tech campus in New York City, and NewYork-Presbyterian. Students can complete the M.S. track in Biostatistics and Data Science in 12 months. Students must complete at least 34 credits to graduate.

Prerequisites for Admission

Information Sessions

Program Director

Xi Kathy Zhou, Ph.D., M.S.

(number of credits in parenthesis)

Fall Term

Spring Term

Summer Term

Sample Course Sequence

Please find a sample course schedule for the M.S. in Biostatistics & Data Science program below. Students need a minimum of 34 credits to graduate. Choose a total of four electives over one year. 

*All courses and sequences are subject to change. 

Fall Sample Course Sequence (course load = 11 or 14 credits)

Course TitleCredit HoursCourse Type
Biostatistics I with R Lab 4Required
Study Design1.5Required
Categorical and Censored Data Analysis 1.5Required
Data Science I (R and Python)3Required
Master's Project I & Professional Development1Required
Statistical Programming with SAS3Recommended Elective
Introduction to Operations Research in Health Policy3Elective
Introduction to Health Services Research3Elective

Spring Sample Course Sequence (course load = 8, 11 or 14 credits)

Course TitleCredit HoursCourse Type
Biostatistics II - Regression Analysis3Required
Master's Project II2Required
Design and Analysis of Biomedical Studies3Recommended Elective
Data Management (SQL)3Recommended Elective
Big Data in Medicine3Recommended Elective
Pharmaceutical Statistics3Recommended Elective
Artificial Intelligence in Medicine3Elective
Health Data for Research (SAS)3Elective

Summer Sample Course Sequence (course load = 9 or 12 credits)

Course TitleCredit HoursCourse Type
Data Science II - Statistical Learning3Required
Master's Project III3Required
Hierarchical Modeling and Longitudinal Data Analysis3Recommended Elective
Causal Inference with Machine Learning3Recommended Elective
Study Design and Comparative Effectiveness3Elective