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MS BIOST DAT - Biostatistics and Data Science (MS)

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John D. Bower School of Population Health (School) ACADEMIC_LEVEL_GRADUATE

Program Title

Biostatistics and Data Science (MS)

Default Credentials

MS - Master of Science

Program Description

The Master of Science (MS) program in Biostatistics and Data Science will train students to extract, analyze, and translate vast amounts of data into actionable evidence and communicate results to individuals from other disciplines. This program synergizes competencies in statistics, computer science, and epidemiology, an important combination of skills for analyzing increasingly complex health-related data. Through supervised consulting sessions and an internship, students will develop the technical and collaborative skills necessary to excel in clinical, academic, government, industrial, and population health work organizations. Students must install the following statistical programs onto their personal laptops prior to orientation: Stata, R, and SAS OnDemand for Academics.

Collect biomedical, clinical, and population health data.

Clean biomedical, clinical, and population health data.

Organize biomedical, clinical, and population health data.

Analyze biomedical, clinical, and population health data.

Use standard statistical (R, SAS, and Stata) and computer (Python) programming languages to reproducibly explore and visualize data, fit models, conduct inference, and translate analysis results.

Conduct all facets of big data analysis, including extracting, storing, manipulating, and analyzing massive genetic and bioinformatics datasets.

Convert information contained in databases and data warehouses into actionable findings using machine learning and other data science techniques.

Adhere to rigorous ethical and methodological standards when analyzing real-world data.

Collaborate with researchers and communicate findings to improve health care and prevent disease.

Admission Requirements

The program accepts students for fall enrollment. To be considered for fall admission, all applications must be submitted and completed by June 1.

MS in Biostatistics and Data Science applicants will be evaluated on the following:

  • Baccalaureate degree in a relevant discipline

  • Grade Point Average (GPA) of 3.0 or better (preferred)

  • Three letters of recommendation from faculty members at accredited institutions or employment supervisors

  • A personal statement

  • Curriculum Vitae

  • GRE; A GRE score >295 on the combined verbal and quantitative scores is preferred

In addition, students must have documented training in calculus (including multiple variable integration and differentiation) and linear algebra. Additional training in statistical or computer programming languages is preferred.

Degree Requirements

Students must successfully complete the prescribed plan of study, meeting a minimum of 38 credit hours beyond a baccalaureate degree, to be eligible for the awarding of a degree.

Year 1 – Fall

BDS 721

Analytics

3

BDS 741

Statistical Inference I

3

BDS 754

Principles of Programming with Python

3

9

Year 1 – Spring

BDS 706

Ethics in Biostatistics and Data Science Research and Practice

1

BDS 722

Advanced Analytics

3

BDS 723

Statistical Programming with R

3

BDS 763 or BDS 751

Database Systems or Statistical Inference in Genetics*

3

10

Year 2 – Summer

BDS 797

Biostatistics & Data Science Internship

1

1

Year 2 – Fall

PHS 703 or MSCI 710

Epidemiology I**

3

BDS 725

Survival Analysis

3

BDS 761

Data Science and Machine Learning 1

3

9

Year 2 – Spring

BDS 724

Longitudinal and Multilevel Models

3

BDS 765

Data Science and Machine Learning 2

3

BDS 792

Statistical Consulting

3

9

*Students intending to complete the PhD in Biostatistics and Data Science are advised to register for BDS 751: Statistical Inference in Genetics.

**Students may substitute PHS 703. Epidemiology I for MSCI 710. Epidemiology I in the fall of their second year.

***Electives: In certain cases, the advisor and program director may recommend that students take additional credits.

Electives

  • BDS 711 – Statistical Methods in Research (3 hours)

  • BDS 712 – Statistical Methods in Research II (3 hours)

  • BDS 713 – Intro to Data Management and Programming (3 hours)

  • BDS 714 – Statistical Methods for Clinical Trials (3 hours)

  • BDS 715 – Intro to Sample Survey Analyses (3 hours)

  • BDS 726 – Generalized Linear Models (3 hours)

  • BDS 727 – Nonparametric Analyses (3 hours)

  • BDS 728 – Multivariate Analysis (3 hours)

  • BDS 742 – Statistical Inference II (3 hours

  • BDS 743 – Theory of Linear Models (3 hours)

  • BDS 752 – Advanced Statistical Genetics (3 hours)

  • BDS 753 – Bioinformatics (3 hours)

  • BDS 762 – Advanced Data Science (3 hours)

  • BDS 763 – Database Systems (3 hours)

  • BDS 764 – Data Visualization (3 hours)

  • BDS 766 – Advanced Computational Methods (3 hours)

  • BDS 767 – Deep Learning Applications (3 hours)

  • BDS 791 – Special Topics (1-9 hours)

  • BDS 793 – Seminar Series: Microtopics (1 hour)

  • BDS 796 – Directed Research (3 hours)

For more information about this program, contact: