Northwestern University Feinberg School of Medicine
Center for Education in Health Sciences
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Master of Science in Biostatistics

 

The Master of Science in Biostatistics is a one-year, full-time master’s program through Northwestern University Feinberg School of Medicine. It provides graduate biostatistics training for students who intend to plan, direct and execute health research and/or analyze health data. Three concentration options address a variety of student goals: Population Health Analytics, Statistical Bioinformatics and Statistical Methods and Practice. Students matriculate in June and graduate in June of the following year. Coursework can also be completed in two years for half-time students.

  • Director’s Message
    Hear from our program director, Denise Scholtens, PhD, and learn about her goals for the program.
  • Faculty
    Meet our faculty and browse their profiles to learn more about their work.
  • Degree Requirements
    Review the full degree requirements on The Graduate School’s website.

 Concentrations

Population Health Analytics Concentration

This concentration introduces statistical methods and epidemiology. Students will complete a thesis project that integrates statistical methods with cutting edge computational strategies for bioinformatics applications.

Statistical Bioinformatics Concentration

This concentration is designed for the student interested in "big data" who plans to pursue a career as an analyst or programmer or pursue additional graduate studies. Students will complete a culminating exam that integrate statistical methods with cutting edge computational strategies for bioinformatics applications. Coursework for this concentration can be completed on either a full-time or part-time basis.

Statistical Methods & Practice Concentration

This concentration emphasizes advanced statistical methods for a range of health applications, and is designed for students who plan to pursue a career as an analyst or programmer or pursue additional graduate studies. Coursework includes a thesis project and can be completed either on a full-time or part-time basis’.

 Competencies

In order to prepare students to pursue research in a broad range of topics, the degree requirements for the MS in Biostatistics were developed to teach students biostatistical reasoning and study design methods needed to carry out sound scientific research. Upon completion of the required coursework, all students earning the MS in Biostatistics degree should be able to do the following.

Graduate-Level Professional Foundational Public Health Knowledge

  1. Explain public health history, philosophy and values
  2. Identify the core functions of public health and the 10 Essential Services
  3. Explain the role of quantitative and qualitative methods and sciences in describing and assessing a population’s health
  4. List major causes and trends of morbidity and mortality in the United States or other community relevant to the school or program
  5. Discuss the science of primary, secondary and tertiary prevention in population health, including health promotion and screening
  6. Explain the critical importance of evidence in advancing public health knowledge
  7. Explain effects of environmental factors on a population’s health
  8. Explain biological and genetic factors that affect a population’s health
  9. Explain behavioral and psychological factors that affect a population’s health
  10. Explain the social, political and economic determinants of health and how they contribute to population health and health inequities
  11. Explain how globalization affects global burdens of disease
  12. Explain an ecological perspective on the connections among human health, animal health and ecosystem health (e.g., One Health)

MS in Biostatistics Competencies

  1. Apply classic methods for continuous and categorical data analysis, including regression and other appropriate statistical approaches
  2. Use computer-based statistical analysis package(s) to manage data
  3. Develop visualized data using computer-based statistical analysis package(s)
  4. Analyze data employing computer-based statistical analysis package(s)
  5. Implement sample size and power calculations for a range of experimental designs
  6. Interpret results of a health research study, including the relation to findings from other studies, potential biological or social mechanisms, study limitations and public health implications
  7. Communicate written and oral findings in a scientifically sound manner
  8. Calculate epidemiological measures of association between risk factors and disease
  9. Apply methods and strategies to evaluate and reduce bias in health research
  10. Use criteria to distinguish between association and causality
  11. Apply ethical and regulatory standards to human subjects research

Population Health Analytics Concentration Competencies

  1. Design an epidemiologic study to address a question of interest
  2. Describe practical considerations for the conduct of health research studies
  3. Access publicly available data resources for population health research
  4. Critically review the scientific literature, synthesize findings across studies and make appropriate recommendations based on current knowledge
  5. Develop a clear description of the rationale, methods, results, and overall interpretation of an epidemiologic investigation

Statistical Bioinformatics Concentration Competencies

  1. Develop computer files of high-dimensional data for analysis using high performance computing data management techniques
  2. Determine and execute appropriate statistical analyses, in particular techniques relevant to bioinformatics, to address a study question
  3. Access publicly available databases for bioinformatics research
  4. Develop statistical and bioinformatics analysis results in written, graphical and verbal format in response to an analysis request
  5. Identify theoretical underpinnings of advanced statistical models

Statistical Methods and Practice Concentration Competencies

  1. Develop computer files of raw data for analysis using data management and statistical analysis software
  2. Execute appropriate statistical analyses to address a study question
  3. Apply classic methods for the analysis of time-to-event and clinical trial data
  4. Develop statistical analysis results in written and verbal format in response to an analysis request
  5. Identify theoretical underpinnings of advanced statistical models
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