Curriculum - Class of 2023

Curriculum Overview
 

The Master of Biostatistics degree, a professional degree awarded by the Duke University School of Medicine, requires 36 credits of graded course work, a practicum experience, a qualifying examination, and a master’s project for which 6 units of credit are given. Completed in the second year, the master’s project serves to demonstrate the student’s mastery of biostatistics. Ten courses (BIOSTAT 701, 702, 703, 704, 705, 706, 721, 722 or 821, 801, 802) constitute 26 credits that are required for all degree candidates.The Master of Biostatistics Program curriculum is structured as follows:

Core Courses

Foundational courses required of all degree-seeking students. 

BIOSTAT 701. Introduction to Statistical Theory and Methods I
BIOSTAT 702. Applied Biostatistics Methods I
BIOSTAT 703. Introduction to the Practice of Biostatistics I
BIOSTAT 704. Introduction to Statistical Theory and Methods II
BIOSTAT 705. Applied Biostatistical Methods II
BIOSTAT 706. Introduction to the Practice of Biostatistics II
BIOSTAT 721. Introduction to Statistical Programming I 
BIOSTAT 722. Introduction to Statistical Programming II or BIOSTAT 821. Software Tools for Data Science 
BIOSTAT 801. Biostatistics Career Preparation and Development
BIOSTAT 802. Biostatistics Career Preparation and Development II

Tracks and Timeline

There are three separate tracks in the Master of Biostatistics program. These include Clinical and Translational Research track, Biomedical Data Science, and Mathematical Statistics Track. Click here to see the different track options.  

Practicum

All candidates for the Master of Biostatistics degree are required to complete a practicum. The practicum is an experiential learning opportunity. The main goal of the practicum is to allow students to develop their analytic ability, biological knowledge, and communication skills. The practicum is typically completed during the summer after the first year, but can be completed during the second year.

Qualifying Examination

All candidates for the Master of Biostatistics degree are required to pass a written Qualifying Examination demonstrating their mastery of fundamental concepts acquired through completion of the first-year core courses (BIOSTAT 701 – 706 inclusive). Students are expected to take the Qualifying Examination after completing the first year of study in the program and prior to beginning their elective coursework.

Master's Project (two semesters, totalling six credits)

All candidates for the Master of Biostatistics degree are required to complete a Master’s Project. Completed in the second year, the two-semester Master’s Project serves to demonstrate the student's mastery of core statistical concepts and the practice of biostatistics.
BIOSTAT 720. Master's Project (3 credits per semester for a total of six credits)

Elective Courses (three credits each)

Full-time Master of Biostatistics students will select five three-credit elective courses during the second year of study:
BIOSTAT 707. Statistical Methods for Learning and Discovery
BIOSTAT 708. Clinical Trial Design and Analysis 
BIOSTAT 709. Observational Studies 
BIOSTAT 710. Statistical Genetics and Genetic Epidemiology 
BIOSTAT 713. Survival Analysis 
BIOSTAT 718. Analysis of Correlated and Longitudinal Data 
BIOSTAT 719. Generalized Linear Models 
BIOSTAT 823. Statistical Programming for Big Data for students in the BDS Track
BIOSTAT 824. Case Studies in Biomedical Data Science for students in the BDS Track
BIOSTAT 905. Linear Models and Inference
BIOSTAT 906. Statistical Inference
BIOSTAT 911. Advanced Topics in Inferential techniques and Theory
MATH 531.     Real Analysis I
MATH 731.     Introduction to Real Analysis

Professional Development Courses (1 credit each)

BIOSTAT 801. Biostatistics Career Preparation and Development I
BIOSTAT 802. Biostatistics Career Preparation and Development II

Course Planning

First Year - 26 graded coursework credit hours

Fall

Spring

Summer

BIOSTAT 701 (3)

BIOSTAT 704 (3)

Qualifying Examination (covers content from  BIOSTAT 701-706)

 

Practicum (may be completed at any point after the first year)

BIOSTAT 702 (3)

BIOSTAT 705 (3)

BIOSTAT 703 (3)

BIOSTAT 706 (3)

BIOSTAT 703L (0)

BIOSTAT 722 or

BIOSTAT 821 (3)

BIOSTAT 721 (3)

BIOSTAT 802 (1)

BIOSTAT 801 (1)

 

Total: 13 credit hours

Total: 13 credit hours

Second Year  24 credit hours – master’s project (6) plus graded coursework credit hours (18)

Fall

Spring

BIOSTAT 720 (3)

BIOSTAT 720 (3)

+ 3 of the following:

 

Biostatistics:

BIOSTAT 707 (3)

BIOSTAT 710 (3)

BIOSTAT 713 (3)

BIOSTAT 719 (3)

BIOSTAT 823 (3)

BIOSTAT 906 (3)

BIOSTAT 911 (3)

BIOSTAT 914 (3)

MATH 531 (3)

MATH 731 (3)

 

+ 3 of the following:

 

Biostatistics:

BIOSTAT 708 (3)

BIOSTAT 709 (3)

BIOSTAT 718 (3)

BIOSTAT 824 (3)

BIOSTAT 905 (3)

MATH 531 (3)

MATH 731 (3)

Computational Biology (3):

Any 500 and 600 level except 510S, 511, or 591

 

Computer Science (3):

Any 500 and 600 level

 

Statistical Science (3):

Any 500 level except 501s

Any 600 level except for 693

Any 700 level except for 701S, 790, 791.

Any 800 level except for 851

Any 900 level except for 993, 994, and 995

Computational Biology (3):

Any 500 and 600 level except 510S, 511, or 591

 

Computer Science (3):

Any 500 and 600 level

 

Statistical Science (3):

Any 500 level except 501s

Any 600 level except for 693

Any 700 level except for 701S, 790, 791.

Any 800 level except for 851

Any 900 level except for 993, 994, and 995

Total: 3 required credit hours plus 9 elective credit hours

Total: 3 required credit hours plus 9 elective credit hours

Course Descriptions

BIOSTAT 701. Introduction to Statistical Theory and Methods I. This course provides a formal introduction to the basic theory and methods of probability and statistics. It covers topics in probability theory with an emphasis on those needed in statistics, including probability and sample spaces, independence, conditional probability, random variables, parametric families of distributions, and sampling distributions. Core concepts are mastered through mathematical exploration and linkage with the applied concepts studied in BIOSTAT 704.
Prerequisite(s): 2 semesters of calculus or its equivalent (multivariate calculus preferred). Familiarity with linear algebras is helpful. Corequisite(s): BIOSTAT 702, BIOSTAT 703.
Credits: 3

BIOSTAT 702. Applied Biostatistical Methods I. This course provides an introduction to study design, descriptive statistics, and analysis of statistical models with one or two predictor variables. Topics include principles of study design, basic study designs, descriptive statistics, sampling, contingency tables, one- and two-way analysis of variance, simple linear regression, and analysis of covariance. Both parametric and non-parametric techniques are explored. Core concepts are mastered through team-based case studies and analysis of authentic research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 703. Computational exercises will use the R and SAS packages.
Prerequisite(s): 2 semesters of calculus or its equivalent (multivariate calculus preferred). Familiarity with linear algebras is helpful. Corequisites(s): BIOSTAT 701, BIOSTAT 703, BIOSTAT 721.
Credits: 3

BIOSTAT 703. Introduction to the Practice of Biostatistics I. This course provides an introduction to biology at a level suitable for practicing biostatisticians and directed practice in techniques of statistical collaboration and communication. With an emphasis on the connection between biomedical content and statistical approach, this course helps unify the statistical concepts and applications learned in BIOSTAT 701 and BIOSTAT 702. In addition to didactic sessions on biomedical issues, students are introduced to different areas of biostatistical practice at Duke University Medical Center. Biomedical topics are organized around the fundamental mechanisms of disease from both evolutionary and mechanistic perspectives, illustrated using examples from infectious disease, cancer and chronic /degenerative disease. In addition, students learn how to read and interpret research and clinical trial papers. Core concepts and skills are mastered through individual reading and class discussion of selected biomedical papers, team-based case studies and practical sessions introducing the art of collaborative statistics.
Corequisite(s): BIOSTAT 701, BIOSTAT 702.
Credits: 3

BIOSTAT 703L. Introduction to the Practice of Biostatistics I Lab. The lab will beis an extension of the course. The lab will beis run like a journal club. The lab will instructs students how to dissect a research article from a statistical and scientific perspective. The lab will also giveprovides students the opportunity to present on material covered in the co-requisite course and to practice the communication skills that are a core tenant of the program.
Corequisite(s): BIOSTAT 703 or permission of the director of graduate studies.
Credits: 0

BIOSTAT 704. Introduction to Statistical Theory and Methods II. This course provides formal introduction to the basic theory and methods of probability and statistics. It covers topics in statistical inference, including classical and Bayesian methods, and statistical models for discrete, continuous and categorical outcomes. Core concepts are mastered through mathematical exploration, simulations, and linkage with the applied concepts studied in BIOSTAT 705.
Prerequisite(s): BIOSTAT 701 or its equivalent. Corequisite(s): BIOSTAT 705, BIOSTAT 706.
Credits: 3

BIOSTAT 705. Applied Biostatistical Methods II. This course provides an introduction to general linear models and the concept of experimental designs. Topics include linear regression models, analysis of variance, mixed-effects models, generalized linear models (GLM) including binary, multinomial responses and log-linear models, basic models for survival analysis and regression models for censored survival data, and model assessment, validation and prediction. Core concepts are mastered through statistical methods application and analysis of practical research problems encountered by program faculty and demonstrated in practicum experiences in concert with BIOSTAT 706. Computational examples and exercises will use the SAS and R packages.
Prerequisite(s): BIOSTAT 702 or its equivalent. Corequisite(s): BIOSTAT 704, BIOSTAT 706, BIOSTAT 722/821.
Credits: 3

BIOSTAT 706. Introduction to the Practice of Biostatistics II. This course revisits the topics covered in BIOSTAT 703 in the context of high-throughput, high-dimensional studies such as genomics and transcriptomics. The course will be based on reading of both the textbook and research papers. Students will learn the biology and technology underlying the generation of “big data,” and the computational and statistical challenges associated with the analysis of such data sets. As with BIOSTAT 703, there will be strong emphasis on the development of communication skills via written and oral presentations.
Prerequisite(s): BIOSTAT 703. Corequisite(s): BIOSTAT 704, BIOSTAT 705.
Credits: 3

BIOSTAT 707. Statistical Methods for Learning and Discovery. This course surveys a number of techniques for high dimensional data analysis useful for data mining, machine learning and genomic applications, among others. Topics include principal and independent component analysis, multidimensional scaling, tree-based classifiers, clustering techniques, support vector machines and networks, and techniques for model validation. Core concepts are mastered through the analysis and interpretation of several actual high dimensional genomics datasets.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 708. Clinical Trial Design and Analysis. Topics include: history/background and process for clinical trial, key concepts for good statistics practice (GSP)/good clinical practice (GCP), regulatory requirement for pharmaceutical/clinical development, basic considerations for clinical trials, designs for clinical trials, classification of clinical trials, power analysis for sample size calculation, statistical analysis for efficacy evaluation, statistical analysis for safety assessment, implementation of a clinical protocol, statistical analysis plan, data safety monitoring, adaptive design methods in clinical trials (general concepts, group sequential design, dose finding design, and phase I/II or phase II/III seamless design) and controversial issues in clinical trials.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 709. Observational Studies. Methods for causal inference, including confounding and selection bias in observational or quasi-experimental research designs, propensity score methodology, instrumental variables, and methods for non-compliance
in randomized clinical trials.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 710. Statistical Genetics and Genetic Epidemiology. Topics from current and classical methods for assessing familiality and heritability, linkage analysis of Mendelian and complex traits, family-based and population-based association studies, genetic heterogeneity, epistasis, and gene-environmental interactions. Computational methods and applications in current research areas. The course will include a simple overview of genetic data, terminology, and essential population genetic results. Topics will include sampling designs in human genetics, gene frequency estimation, segregation analysis, linkage analysis, tests of association, and detection of errors in genetic data.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 713. Survival Analysis. Introduction to concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, regression techniques, applications to clinical trials. Interval censoring, informative censoring, competing risks, multiple events and multiple endpoints, time dependent covariates; nonparametric and semi- parametric methods.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 718. Analysis of Correlated and Longitudinal Data. Topics include linear and nonlinear mixed models; generalized estimating equations; subject specific versus population average interpretation; and hierarchical model.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 719. Generalized Linear Models. The class introduces the concept of exponential family of distributions and link function, and their use in generalizing the standard linear regression to accommodate various outcome types. Theoretical framework will be presented but detailed practical analyses will be performed as well, including logistic regression and Poisson regression with extensions. Majority of the course will deal with the independent observations framework. However, there will be substantial discussion of longitudinal/clustered data where correlations within clusters are expected. To deal with such data the Generalized Estimating Equations and the Generalized Linear Mixed models will be introduced. An introduction to a Bayesian analysis approach will be presented, time permitting.
Prerequisite(s): BIOSTAT 701, 702, 704, 705, and 721 or 722/821 or their equivalents, or permission of the director of graduate studies.
Credits: 3

BIOSTAT 720. Master’s Project. Completed during a student’s final year of study, the master’s project is performed under the direction of a faculty mentor and is intended to demonstrate general mastery of biostatistical practice.
Prerequisite(s): BIOSTAT 701 through BIOSTAT 706.
Credits: 3 in Fall Semester and 3 in Spring Semester

BIOSTAT 721. Introduction to Statistical Programming I (R). This class is an introduction to programming in R, targeted at statistics majors with minimal programming knowledge, which will give them the skills to grasp how statistical software works, tweak it  to suit their needs, recombine existing pieces of code, and when needed create their own programs. Students will learn the core of ideas of programming (functions, objects, data structures, input and output, debugging, and logical design) through writing code to assist in numerical and graphical statistical analyses. Students will learn how to write maintainable code, and to test code for correctness. They will then learn how to set up stochastic simulations and how to work with and filter large data sets. Since code is also an important form of communication among scientists, students will learn how to comment and organize code to achieve reproducibility. Programming techniques and their application will be closely connected with the methods and examples presented in the co-requisite course.
The primary programming package used in this course will be R.
Prerequisite(s): None; familiarity with linear algebras is helpful. Corequisite(s): BIOSTAT 702.
Credits: 3

BIOSTAT 722. Introduction to Statistical Programming II (SAS). This class is an introduction to programming in SAS, targeted at statistics majors with minimal programming knowledge, which will give them the skills to grasp how statistical software works, tweak it to suit their needs, recombine existing pieces of code, and when needed create their own programs. Students will learn the core of ideas of programming (data step, procedures, macros, ODS, input and output, debugging, and logical design) through writing code to assist in numerical and graphical statistical analyses. Students will learn how to write maintainable code, and to test code for correctness. They will then learn how to set up stochastic simulations and how to work with and filter large data sets. Since code is also an important form of communication among scientists, students will learn how to comment and organize code to achieve reproducibility. Programming techniques and their application will be closely connected with the methods and examples presented in the co-requisite course. The primary programming package focus used in this course will be SAS.
Prerequisite(s): None; familiarity with linear algebras is helpful. Corequisite(s): BIOSTAT 705.
Credits: 3

BIOSTAT 732. Independent Study. Independent Study is a semester long course focused on mentored research in the practice of biostatistics. Students work with an assigned mentor. This course is only open to students by permission of the director of graduate studies.
Credits: 1, 2, or 3

BIOSTAT 740. Continuation. Continuation is a semester-based, noncredit-bearing enrollment status used when a student is continuing scholarly activities with the same mentor. This course is only open to students by permission of the director of graduate studies.
Credits: 0

BIOSTAT 801. Biostatistics Career Preparation and Development I. The purpose of this course is to give the student a holistic view of career choices and development and the tools they will need to succeed as professionals in the world of work. The fall semester will focus on resume development, creating a professional presence, networking techniques, what American employers expect in the workplace, creating and maintaining a professional digital presence and learning how to conduct and succeed at informational interviews. Practicums in this semester include an informational interviewing and networking practicum with invited guests. Students participate in a professional “etiquette dinner” and a “dress for success” module as well an employer panel.
Corequisite(s): BIOSTAT 701 through BIOSTAT 703.
Credit: 1

BIOSTAT 802. Biostatistics Career Preparation and Development II. The purpose of this course is to further develop the student’s job seeking ability and the practical aspects of job/internship search or interviewing for a PhD program. The goal is to learn these skills once and use them for a lifetime. Modules that will be covered include: Communication skills both written and oral, interviewing with videotaped practice and review, negotiating techniques, potential career choices in the Biostatistics marketplace, and working on a team. This semester includes writing and interviewing practicum, and a panel of relevant industry speakers. Students will leave this course with the knowledge to manage their careers now and in the future.
Prerequisite: BIOSTAT 801.
Credit: 1

BIOSTAT 821. Software Tools for Data Science. A data scientist needs to master several different tools to obtain, process, analyze, visualize and interpret large biomedical data sets such as electronic health records, medical images, and genomic sequences. It is also critical that the data scientist masters the best practices associated with using these tools, so that the results are robust and reproducible. The course covers foundational tools that will allow students to assemble a data science toolkit, including the Unix shell, text editors, regular expressions, relational and NoSQL databases, and the Python programming language for data munging, visualization and machine learning. Best practices that students will learn include the Findable, Accessible, Interoperable and Reusable (FAIR) practices for data stewardship, as well as reproducible analysis with literate programming, version control and containerization.
Prerequisite: BIOSTAT 721 and permission of the director of graduate studies.
Credits: 3

BIOSTAT 823. Statistical Program for Big Data. This course describes the challenges faced by analysts with the increasing importance of large data sets, and the strategies that have been developed in response to these challenges. The core topics are how to manage data and how to make computation scalable. The data management module covers guidelines for working with open data, and the concepts and practical skills for working with in-memory, relational and NoSQL databases. The scalable computing module focuses on asynchronous, concurrent, parallel and distributed computing, as well as the construction of effective workflows following DevOps practices. Applications to the analysis of structured, semi-structured and unstructured data, especially from biomedical contexts, will be interleaved into the course. The course examples are primarily in Python and fluency in Python is assumed.
Prerequisite(s): BIOSTAT 821 or permission of the director of graduate studies.
Credits: 3

BIOSTAT 824. Case Studies in Biomedical Data Science. This course will highlight how biomedical data science blends the field of biostatistics with the field of computer science through the introduction of 3 to 5 case studies. Students will be introduced to analytic programs typically encountered in biomedical data science and will implement the data science and statistical skills introduced in their previous course work.
Prerequisite(s): BIOSTAT 707, 821, 822, and 823 or permission of the director of graduate studies.
Credits: 3

BIOSTAT 905. Linear Models and Inference. Introduction to linear models and linear inference from the coordinate- free viewpoint. Topics: identifiability and estimability, key properties of and results for finite-dimensional vector spaces, linear transformations, self-adjoint transformations, spectral theorem, properties and geometry of orthogonal projectors, Cochran’s theorem, estimation and inference for normal models, distributional properties of quadratic forms, minimum variance linear unbiased estimation, Gauss-Markov theorem and estimation, calculus of differentials, analysis of variance and covariance. Prerequisite(s): Biostatistics 702, 704, 705, real analysis, and linear algebra, or consent of the instructor and director of graduate studies.
Credits: 3

BIOSTAT 906. Statistical Inference. Introduce decision theory and optimality criteria, sufficiency, methods for point estimation, confidence interval and hypothesis testing methods and theory. Prerequisite: Biostatistics 704 or equivalent. Instructor consent required.
Prerequisite: Permission of the director of graduate studies.
Credits: 3

BIOSTAT 911. Modern Inferential Techniques and Theory. The theory for M- and Z- estimators and applications. Semiparametric models, geometry of efficient score functions and efficient influence functions, construction of semiparametric efficient estimators. Introduction to the bootstrap: consistency, inconsistency and remedy, correction for bias, and double bootstrap. U statistics and rank and permutation tests.
Prerequisite: STA 711 and BIOSTAT 906 or Permission of the director of graduate studies.
Credits: 3

BIOSTAT 914. Graphical Models for Biological Data. Introduction to probabilistic graphical models and structured prediction, with applications in genetics and genomics. Hidden Markov Models, conditional random fields, stochastic grammars, Bayesian hierarchical models, neural networks, and approaches to integrative modeling. Algorithms for exact and approximate inference. Applications in DNA/RNA analysis, phylogenetics, sequence alignment, gene expression, allelic phasing and imputation, genome/epigenome annotation, and gene regulation.
Prerequisite: Permission of the director of graduate studies.
Credits: 3