M.S. Track in Biostatistics & 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 analytic data science techniques.

Close-up of hands typing on keyboard.

Our coursework offers students a foundation for data analytic careers in the pharmaceutical industry, healthcare, biomedical sciences, academics and general data analytics.

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 the newest applications to solve 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 challenging careers in the public and private biomedical, healthcare, insurance and pharmaceutical sectors, both in academia and the 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 Statistical Sciences at Cornell University, the Cornell Tech campus in New York City, and New York-Presbyterian Hospital. Full-time students can complete the M.S. track in Biostatistics and Data Science in 12 months, and part-time students in 18 - 24 months. Students must complete at least 34 credits to graduate.

One-Year Sample Course Sequence

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

Fall Sample Course Sequence (course load = 11 or 14 credits)
Course TitleCredit HoursCourse Type
Biostatistics I with R Lab 4Regular
Study Design1.5Regular
Categorical and Censored Data Analysis 1.5Regular
Data Science I (R and Python)3Regular
Master's Project I & Professional Development1Regular
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 Analysis3Regular
Master's Project II2Regular
Design and Analysis of Biomedical Studies3Recommended Elective
Data Management in Healthcare (SQL)3Recommended Elective
Big Data in Medicine3Recommended 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 Learning3Regular
Master's Project III3Regular
Advanced Topics in Biostatistics3Recommended Elective
Causal Inference with Machine Learning3Recommended Elective
Study Designs and Methods for Comparative Effectiveness Research3Elective

Two-Year Sample Course Sequence

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

Fall Year One Sample Course Sequence (course load = 7 credits)
Course TitleCredit HoursCourse Type
Biostatistics I with R Lab 4Regular
Study Design1.5Regular
Categorical and Censored Data Analysis1.5Regular
Statistical Programming with SAS3Recommended Elective (Any Fall Term)
Introduction to Operations Research in Health Policy3Elective (Any Fall Term)
Introduction to Health Services Research3Elective (Any Fall Term)
Fall Year Two Sample Course Sequence (course load = 4 credits)
Course TitleCredit HoursCourse Type
Data Science I (R and Python)3Regular
Master's Project I & Professional Development1Regular
Statistical Programming with SAS3Recommended Elective (Any Fall Term)
Introduction to Operations Research in Health Policy3Elective (Any Fall Term)
Introduction to Health Services Research 3Elective (Any Fall Term)
Spring Year One Sample Course Sequence (course load = 3, 6, or 9 credits)
Course TitleCredit HoursCourse Type
Biostatistics II - Regression Analysis3Regular
Design and Analysis of Biomedical Studies3Recommended Elective (Any Spring Term)
Data Management (SQL)3Recommended Elective (Any Spring Term)
Big Data in Medicine3Recommended Elective (Any Spring Term)
Artificial Intelligence in Medicine3Elective (Any Spring Term)
Health Data for Research (SAS)3Elective (Any Spring Term)
Spring Year Two Sample Course Sequence (course load = 5 or 8 credits)
Course TitleCredit HoursCourse Type
Master's Project II2Regular
Design and Analysis of Biomedical Studies3Recommended Elective (Any Spring Term)
Data Management (SQL)3Recommended Elective (Any Spring Term)
Big Data in Medicine3Recommended Elective (Any Spring Term)
Artificial Intelligence in Medicine3Elective (Any Spring Term)
Health Data for Research (SAS)3Elective (Any Spring Term)
Summer Year One Sample Course Sequence (course load = 3, 6 or 9 credits)
Course TitleCredit HoursCourse Type
Data Science II - Statistical Learning3Regular
Advanced Topics in Biostatistics3Recommended Elective (Any Summer Term)
Causal Inference with Machine Learning3Recommended Elective (Any Summer Term)
Comparative Effectiveness3Elective (Any Summer Term)
Summer Year Two Sample Course Sequence (course load = 3 or 6 credits)
Course TitleCredit HoursCourse Type
Master's Project III3Regular
Advanced Topics in Biostatistics3Recommended Elective (Any Summer Term)
Causal Inference with Machine Learning3Recommended Elective (Any Summer Term)
Comparative Effectiveness3Elective (Any Summer Term)
(number of credits in parenthesis)

Fall Term

Spring Term

Summer Term