Department of Biostatistics

2018 - 2019 Course Descriptions

Core Principles of Biostatistics and Epidemiology for Public Health Practice
ID 201
Gauvreau and Mostofsky
HSPH
Biostatistics / Epidemiology
2018 Fall
Full Term
This course will provide an introduction to the methods of biostatistics and epidemiology in the context of public health and clinical research. The focus will be on applications, providing students with the skills necessary to critically interpret issues related to study design and data analysis in the public health literature. The computer is used throughout the course. Lectures are complemented by seminars and weekly lab sessions. Topics include measures of frequency and association, study designs, bias, confounding, screening tests, probability distributions, estimation and statistical inference, sample size estimation, and regression methods.

Introduction to Statistical Methods
BST 201
Catalano
HSPH
Biostatistics
2018 Fall
Full Term
Covers basic statistical techniques that are important for analyzing data arising from epidemiology, environmental health and biomedical and other public health-related research. Major topics include descriptive statistics, elements of probability, introduction to estimation and hypothesis testing, nonparametric methods, techniques for categorical data, regression analysis, analysis of variance, and elements of study design. Applications are stressed. Designed for students desiring more emphasis on theoretical developments. Background in algebra and calculus strongly recommended.

Applied Regression Analysis
BST 210
Lutz
HSPH
Biostatistics
2018 Fall
Full Term
Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.

Prerequisites: BST 201 or ID 201 or (BST 202 and BST 203) or [BST 206 and (BST 207 or BST 208)].

Applied Regression Analysis
BST 210
Glynn
HSPH
Biostatistics
2019 Spring
Full Term
Topics include model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course will provide students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature.

Prerequisites: BST 201 or ID 201 or (BST 202 and BST 203) or [BST 206 and (BST 207 or BST 208)].

Survey Research Methods in Community Health
BST 212
Mangione
HSPH
Biostatistics
2019 Spring
Full Term
Covers research design, sample selection, questionnaire construction, interviewing techniques, the reduction and interpretation of data, and related facets of population survey investigations. Focuses primarily on the application of survey methods to problems of health program planning and evaluation. Treatment of methodology is sufficiently broad to be suitable for students who are concerned with epidemiological, nutritional, or other types of survey research.

Applied Regression for Clinical Research
BST 213
Orav
HSPH
Biostatistics
2018 Fall
Full Term
This course will introduce students involved with clinical research to the practical application of multiple regression analysis. Linear regression, logistic regression and proportional hazards survival models will be covered, as well as general concepts in model selection, goodness-of-fit, and testing procedures. Each lecture will be accompanied by a data analysis using SAS and a classroom discussion of the results. The course will introduce, but will not attempt to develop the underlying likelihood theory. Background in SAS programming ability required.

Course Notes:
1) Lab or section times to be announced at first meeting.
2) Section 2 of this course is online and only available to Summer Only EPI-SM1 students and Summer-Only MPH students.

Course Prerequisites: BST 201 or ID 201 or (BST 202 and BST 203) or [BST 206 and (BST 207 or BST 208)]. Concurrent enrollment allowed.

Principles of Clinical Trials
BST 214
Wypij
HSPH
Biostatistics
2019 Spring
Spring 1
Designed for individuals interested in the scientific, policy, and management aspects of clinical trials. Topics include types of clinical research, study design, treatment allocation, randomization and stratification, quality control, sample size requirements, patient consent, and interpretation of results. Students design a clinical investigation in their own field of interest, write a proposal for it, and critique recently published medical literature.

Course Prerequisites: ID 201 or BST 201 or (BST 202 and BST 203) or [BST 206 & (BST 207 or BST 208)] or PHS 2000A.

Introduction to Quantitative Methods for Monitoring and Evaluation
BST 216
Pagano and Hedt-Gauthier
HSPH
Biostatistics
2019 Spring
Spring 1
Monitoring and evaluation is concerned with assessing the quality of a program as measured against action plans, and evaluating its overall impact. This course addresses the quantitative or statistical aspects of monitoring and evaluation: what to measure, how to measure, how to analyze and how to make inference for the next steps of program implementation. The course covers quantitative components of M&E, both current and innovative methods, and complements GHP 251 which describes the conceptual framework for M&E.

Course Prerequisites: ID 201 or BST 201 or (BST 202 and BST 203) or [BST 206 & (BST 207 or BST 208)] or PHS 2000A.

Statistical and Quantitative Methods for Pharmaceutical Regulatory
Science
BST 217
Testa
HSPH
Biostatistics
2019 Spring
Spring 2
The goal of this course is to enable scientists and public health professionals who already have an introductory background in biostatistics and clinical trials to acquire the competencies in quantitative skills and systems thinking required to understand and participate in drug development and regulatory review processes. The course illustrates how statistical and quantitative methods are used to transform information into evidence demonstrating the safety, efficacy and effectiveness of drugs and devices over the course the product's life cycle from a regulatory perspective. Content is delivered using a blended-learning approach involving lectures, web-based media and selected case study examples derived from actual FDA decision-making and regulatory assessments to highlight and describe each phase of the regulatory drug approval process. Case studies will illustrate regulatory science in action and practice and will include content publically available from the FDA's website that can be used in conjunction with FDA science-based guidances and decision precedents.

Course Prerequisites: Course Prerequisites: ID 538 or PHS 2000A or {ID 201 or BST 201 or (BST 202 & BST 203) or [BST 206 & (BST 207 or BST 208)] and (EPI 201 or EPI 208)}.

Basics of Statistical Inference
BST 222
Wypij
HSPH
Biostatistics
2018 Fall
Full Term
This course will provide a basic, yet thorough introduction to the probability theory and mathematical statistics that underlie many of the commonly used techniques in public health research. Topics to be covered include probability distributions (normal, binomial, Poisson), means, variances and expected values, finite sampling distributions, parameter estimation (method of moments, maximum likelihood), confidence intervals, hypothesis testing (likelihood ratio, Wald and score tests). All theoretical material will be motivated with problems from epidemiology, biostatistics, environmental health and other public health areas. This course is aimed towards second year doctoral students in fields other than Biostatistics. Background in algebra and calculus required.

Course Prerequisites: BST 210 or BST 213 or PHS 2000A&B.

Applied Survival Analysis
BST 223
Bellavia
HSPH
Biostatistics
2019 Spring
Full Term
Topics will include types of censoring, hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and the accelerated failure time model, adjustment for time-varying covariates, and the use of parametric distributions (exponential, Weibull) in survival analysis. Methods for recurrent survival outcomes and competing risks will also be discussed, as well as design of studies with survival outcomes. Class material will include presentation of statistical methods for estimation and testing along with current software (SAS, Stata) for implementing analyses of survival data. Applications to real data will be emphasized.

Course Prerequisite(s): BST 210 or BST 213 or BST 232/BIOSTAT 232 or BST 260 or PHS 2000A.

Applied Longitudinal Analysis
BST 226
Fitzmaurice
HSPH
Biostatistics
2019 Spring
Full Term
This course covers modern methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including the unbalanced and incomplete data sets characteristic of biomedical research. Topics include an introduction to the analysis of correlated data, analysis of response profiles, fitting parametric curves, covariance pattern models, random effects and growth curve models, and generalized linear models for longitudinal data, including generalized estimating equations (GEE) and generalized linear mixed effects models (GLMMs).

Course Activities: Homework assignments will focus on data analysis in SAS using PROC GLM, PROC MIXED, PROC GENMOD, and PROC GLIMMIX.

Course Prerequisite(s): BST 210 or BST 213 or BST 232/BIOSTAT 232 or BST 260 or PHS 2000A.

Introduction to Statistical Genetics
BST 227
Aryee
HSPH
Biostatistics
2018 Fall
Fall 2
This course introduces students to the diverse statistical methods used throughout the process of statistical genetics, from familial aggregation and segregation studies to linkage scans and association studies. Topics covered include basic principles from population genetics, multipoint and model-free linkage analysis, family-based and population-based association testing, and Genome Wide Association analysis. Instructors use ongoing research into the genetics of respiratory disease, psychiatric disorders and cancer to illustrate basic principles. Weekly homeworks supplement reading, course lectures, discussion and section. Relevant concepts in genetics and molecular genetics will be reviewed in lectures and labs. The emphasis of the course is fundamental principles and concepts.

Course Prerequisites: BST 210 (concurrent enrollment allowed) or PHS 2000A (concurrent enrollment allowed).

Applied Bayesian Analysis
BST 228
Trippa
HSPH
Biostatistics
2018 Fall
Full Term
This course is a practical introduction to the Bayesian analysis of biomedical data. It is an intermediate Master's level course in the philosophy, analytic strategies, implementation, and interpretation of Bayesian data analysis. Specific topics that will be covered include: the Bayesian paradigm; Bayesian analysis of basic models; Bayesian computing: Markov Chain Monte Carlo; STAN R software package for Bayesian data analysis; linear regression; hierarchical regression models; generalized linear models; meta-analysis; models for missing data.

Programming and case studies will be used throughout the course to provide hands-on training in these concepts.

Prerequisites: (BST 210 or PHS 2000A&B) and BST 222.

Probability I
BST 230 / BIOSTAT 230
Pagano
HSPH / GSAS
Biostatistics
2018 Fall
Full Term
Axiomatic foundations of probability, independence, conditional probability, joint distributions, transformations, moment generating functions, characteristic functions, moment inequalities, sampling distributions, modes of convergence and their interrelationships, laws of large numbers, central limit theorem, and stochastic processes.

Course Prerequisite (HSPH): You must be a Biostatistics student or have taken BST 222 to register for this course. If you have taken BST 222 and are not a Biostatistics student, please ask the instructor for an instructor override.
Course Prerequisite (GSAS): You must be in the Biostatistics PhD Program.

Statistical Inference I
BST 231 / BIOSTAT 231
Gray
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.

Course Prerequisite: BST 230/BIOSTAT 230 (concurrent enrollment allowed).

Methods I
BST 232 / BIOSTAT 232
Coull
HSPH / GSAS
Biostatistics
2018 Fall
Full Term
Introductory course in the analysis of Gaussian and categorical data. The general linear regression model, ANOVA, robust alternatives based on permutations, model building, resampling methods (bootstrap and jackknife), contingency tables, exact methods, logistic regression.

Course Restricted: Students in the Biostatistics department.

Introduction to Data Structures and Algorithms
BST 234 / BIOSTAT 234
Lange and Huttenhower
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Introduction to the data structures and computer algorithms that are relevant to statistical computing. The implementation of data structures and algorithms for data management and numerical computations are discussed.

Advanced Regression and Statistical Learning
BST 235 / BIOSTAT 235
Mukherjee
HSPH / GSAS
Biostatistics
2018 Fall
Full Term
An advanced course in linear models, including both classical theory and methods for high dimensional data. Topics include theory of estimation and hypothesis testing, multiple testing problems and false discovery rates, cross validation and model selection, regularization and the LASSO, principal components and dimensional reduction, and classification methods. Background in matrix algebra and linear regression required.

Prerequisite (HSPH): BST 231/BIOSTAT 231 and (BST 232/BIOSTAT 232 or BST 233/BIOSTAT 233).
Prerequisite (GSAS): BIOSTAT 231 AND BIOSTAT 233.

Advanced Topics in Clinical Trials
BST 238 / BIOSTAT 238
Wypij
HSPH / GSAS
Biostatistics
2019 Spring
Spring 2
This course will focus on selected advanced topics in the design, analysis, and interpretation of clinical trials, including study design; choice of endpoints (including surrogate endpoints); interim analyses and group sequential methods; subgroup analyses; and meta-analyses.

Course Prerequisite (HSPH): (BST 214 or BST 214S) and BST 222. BST 214, BST 222 may be taken concurrently. BST 214S may not be taken concurrently.
Course Prerequisite (GSAS): BST 230/BIOSTAT 230 and BST 231/BIOSTAT 231 (may be taken concurrently)

Probability II
BST 240 / BIOSTAT 240
Not Offered in 2018-19
HSPH / GSAS
Biostatistics
Fall
Full Term
A foundational course in measure theoretic probability. Topics include measure theory, Lebesgue integration, product measure and Fubini's Theorem, Radon-Nikodym derivatives, conditional probability, conditional expectation, limit theorems on sequences of random stochastic processes, and weak convergence.

Course Prerequisites: BST 231/BIOSTAT 231.

Statistical Inference II
BST 241 / BIOSTAT 241
R. Wang
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Sequel to BST 231. Considers several advanced topics in statistical inference. Topics include limit theorems, multivariate delta method, properties of maximum likelihood estimators, saddlepoint approximations, asymptotic relative efficiency, robust and rank-based procedures, resampling methods, and nonparametric curve estimation.

Course Prerequisite (HSPH): BST 231/BIOSTAT 231 and BST 240/BIOSTAT 240.
Course Prerequisite (GSAS): BIOSTAT 240.

Analysis of Failure Time Data
BST 244 / BIOSTAT 244
Wei
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Discusses the theoretical basis of concepts and methodologies associated with survival data and censoring, nonparametric tests, and competing risk models. Much of the theory is developed using counting processes and martingale methods. Material is drawn from recent literature.

Course Prerequisite (HSPH): BST 231/BIOSTAT 231 and (BST 232/BIOSTAT 232 or BST 233/BIOSTAT 233) and BST 240/BIOSTAT 240.
Course Prerequisite (GSAS): BIOSTAT 233 and BIOSTAT 240.

Analysis of Multivariate and Longitudinal Data
BST 245 / BIOSTAT 245
Haneuse
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Presents classical and modern approaches to the analysis of multivariate observations, repeated measures, and longitudinal data. Topics include the multivariate normal distribution, Hotelling's T2, MANOVA, the multivariate linear model, random effects and growth curve models, generalized estimating equations, statistical analysis of multivariate categorical outcomes, and estimation with missing data. Discusses computational issues for both traditional and new methodologies.

Course Prerequisite (HSPH & GSAS): BST 231/BIOSTAT 231 and BST 235/BIOSTAT 235.

Advanced Statistical Genetics
BST 247
Not Offered in 2018-19
HSPH
Biostatistics
Spring
Spring 2
BST 247 is a seminar style course with readings selected from the literature in areas of expertise of the participating faculty. Content may vary from year to year. The specific objectives are (1) To train students to critically read foundational papers and current journal articles in Statistical Genetics, (2) To train students to present sophisticated ideas to an audience of peers, and (3) To prepare students to engage in doctoral level research in the area. After the course, students are expected to have an in-depth and broad understanding on important topics of statistical genetics research.

Course Prerequisite(s): BST 227 and (BST 231/BIOSTAT 231 or EPI 511). BST 231/BIOSTAT 231 may be taken concurrently.

Bayesian Methodology in Biostatistics
BST 249 / BIOSTAT 249
Not Offered in 2018-19
HSPH / GSAS
Biostatistics
Fall
Full Term
General principles of the Bayesian approach, prior distributions, hierarchial models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trails, survival analysis.

Course Prerequisites:BST 231/BIOSTAT 231 and BST 232/BIOSTAT 232.

Special Topics in Biostatistics
BST 254 4
Not Offered in 2018-19
HSPH
Biostatistics
Fall
Fall 1
Section 4: Effective Grant and Research Proposal Writing for Biostatistical Research
This course will be organized partly as a seminar and partly as a practicum, in order to allow students to conceptualize and design a biostatistical research project, to craft effective research and grant proposals, and to develop skills for providing constructive critiques of the research proposals of other students and colleagues. During each week, the first session will be given seminar style but may also include case discussions or guest lectures. The second session of each week will be devoted to meeting in smaller subgroups of 2-3 students to share, discuss, and provide feedbac on grant or research proposals. At the end of the course, each student should submit a completed research proposal in the format of an appropriate research grant.

Registration Note: Restricted to 3rd year and higher doctoral students in Biostatistics.

Theory and Methods for Causality I
BST 256
Rotnikzky
HSPH
Biostatistics
2018 Fall
Fall 1
Health policy and clinical decisions rely on the findings of clinical and epidemiological studies of the causal effects of interventions, treatments or exposures. This course will be the first of a sequence of two 2.5 credit courses in which students will learn the mathematical foundations of, state of the art, causal analytic methods that help squeeze as much evidence as these imperfect studies carry about the causal effects of interest. A central theme of the two-course sequence will be that, in order to properly conduct a causal analysis one must start with a formal model that encodes the temporal ordering of variables and possible a-priori known causal relationships. One must subsequently give a clear mathematical formulation of the effect measure that is of interest to answer the causal question at stake. The causal analysis is then geared towards inference about the specific effect measure. Students will understand why a formal theory of causation is needed and why intuition alone often leads to logical mistakes.

This two-course sequence will focus on the theoretical underpinnings of the modern methods for causal inference. The course sequence will cover causal inference methods with enough mathematical rigor to address the needs of students in the Doctoral program in Biostatistics who not only wish to learn how to apply the methods but also to master the theoretical underpinnings which will enable them to develop new methods in causality.

The central theme of Theory and Methods for Causality I will be the formulation, interpretation and identification of models for causal inference.

Course Prerequisite(s): BST 230/BIOSTAT 230 and BST 231/BIOSTAT 231.

Introduction to Data Science
BST 260
Mattie
HSPH
Biostatistics
2018 Fall
Fall
This class focuses on methods for learning from data, in order to gain useful predictions and insights. Separating signal from noise presents many computational and inferential challenges, which we approach from a perspective at the interface of computer science and statistics. Through real-world examples of wide interest, we introduce methods for five key facets of an investigation:
1) data munging/scraping/sampling/cleaning in order to construct an informative, manageable data set;
2) software engineering skills for accessing data as well as organizing data analyses and making these analyses sharable and reproducible and
3) exploratory data analysis to generate hypotheses and intuition about the data;
4) inference and prediction based on statistical tools such as modeling,regression, and classification;
5) communication of results through visualization, stories, and interpretable summaries.

Data Science II
BST 261
Mattie
HSPH
Biostatistics
2019 Spring
Spring 2
This course is the second course in the foundational sequence of the School's newly approved Master's Degree in Health Data Science. The course will build upon our intro course, BST 260 "Introduction to Data Science", in presenting a set of tools for modeling and understanding complex datasets. Specifically, the course will provide practical regression and tree-based techniques for big data. Specific topics that will be covered include: linear model selection and regularization: LASSO and regularization; principal component regression and partial least squares; tree-based methods: decision trees; bagging, random forests, and boosting; unsupervised learning: principal components analysis, cluster analysis.

Programming (Python and R) and case studies will be used throughout the course to provide hands-on training in these concepts.

Prerequisites: BST 260.

Computing for Big Data
BST 262
Choirat
HSPH
Biostatistics
2018 Fall
Fall 2
Big data is everywhere, from Omics and Health Policy to Environmental Health. Every single aspect of the Health Sciences is being transformed. However, it is hard to navigate and critically assess tools and techniques in such a fast-moving big data panorama. In this course, we are going to give a critical presentation of theoretical approaches and software implementations of tools to collect, store and process data at scale. The goal is not just to learn recipes to manipulate big data but learn how to reason in terms of big data, from software design and tool selection to implementation, optimization and maintenance.

Statistical Learning
BST 263
Miller
HSPH
Biostatistics
2019 Spring
Spring
Statistical learning is a collection of flexible tools and techniques for using data to construct prediction algorithms and perform exploratory analysis. This course will introduce students to the theory and application of methods for supervised learning (classification and regression) and unsupervised learning (dimension reduction and clustering). Students will learn the mathematical foundations underlying the methods, as well as how and when to apply different methods. Topics will include the bias-variance tradeoff, cross-validation, linear regression, logistic regression, KNN, LDA/QDA, variable selection, penalized regression, generalized additive models, CART, random forests, gradient boosting, kernels, SVMs, PCA, and K-means. Homework will involve mathematical and programming exercises, and exams will contain conceptual and mathematical problems. Programming in R will be used throughout the course to provide hands-on training and practical examples.

Course Prerequisites: BST 260 or BST 210 or BST 232/BIOSTAT 232.

Introduction to Social and Biological Networks
BST 267
Onnela
HSPH
Biostatistics
2018 Fall
Fall 2
Many systems of scientific and societal interest consist of a large number of interacting components. The structure of these systems can be represented as networks where network nodes represent the components and network edges the interactions between the components. Network analysis can be used to study how pathogens, behaviors and information spread in social networks, having important implications for our understanding of epidemics and the planning of effective interventions. In a biological context, at a molecular level, network analysis can be applied to gene regulation networks, signal transduction networks, protein interaction networks, and more. This introductory course covers some basic network measures, models, and processes that unfold on networks. The covered material applies to a wide range of networks, but we will focus on social and biological networks. To analyze and model networks, we will learn the basics of the Python programming language and its NetworkX module.

The course contains a number of hands-on computer lab sessions. There are five homework assignments and four reading assignments that will be discussed in class. In addition, each student will complete a final project that applies network analysis techniques to study a public health problem.

Course Prerequisites: BST 201 or ID 201 or (BST 202 & 203) or [BST 206 & (BST 207 or 208)]. Concurrent enrollment allowed.

Reproducible Data Science
BST 270
Mattie
HSPH
Biostatistics
2019 Spring
Spring
The central theme of the course will be to meet these scientific needs of reproducible science through training in reproducible research. The topics covered in this course include the fundamentals of reproducible science, case studies in reproducible research, data provenance, statistical methods for reproducible science, and computational tools for reproducible science. This is a blended course where students are introduced to course content online through videos and reading assignments, and then discuss the content in lecture. Each student will submit a completely reproducible research project and give a short presentation at the end of the course.

Course Requirements: Enrollment limited to students in the Biostatistics department, including CBQG SM students.

Introductory Genomics & Bioinformatics for Health Research
BST 280
Quackenbush
HSPH
Biostatistics
2018 Fall
Fall 2
This survey course is intended for a wide audience and will provide an introduction to genomics-inspired techniques and bioinformatics tools, including genome sequencing, DNA microarrays, proteomics, and publicly available databases and software tools.

Course Prerequisites: ID 201 or [BST 201 or (BST 202 & 203) or (BST 206 & (BST 207 or 208)) and (EPI 201 or EPI 500)]. Courses may be taken concurrently.

Genomic Data Manipulation
BST 281 / BIOSTAT 281
Huttenhower and Franzosa
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Introduction to genomic data, computational methods for interpreting these data, and a survey of current functional genomics research. Covers biological data processing, programming for large datasets, high-throughput data (sequencing, proteomics, expression, etc.), and related publications. This course is targeted at students in experimental biology programs with an interest in understanding how available genomic techniques and resources can be applied in their research.

Introduction to Computational Biology and Bioinformatics
BST 282 / BIOSTAT 282
Liu
HSPH / GSAS
Biostatistics
2019 Spring
Full Term
Basic biological problems, genomics technology platforms, algorithms and data analysis approaches in computational biology. There will be three major components of the course: microarray and RNA-seq analysis, transcription and epigenetic gene regulation, cancer genomics.

This course is targeted at students with some statistics and computer programming background who have an interest in exploring genomic data analysis and algorithm development as a potential future direction.

Course Prerequisite (HSPH): STAT 110 and CS 5O, or students in BIO, CBQG, or HDS degree programs.
Course Prerequisite (GSAS): STAT 110 OR CS 50 OR BIOSTAT PhD Students.

Cancer Genome Analysis
BST 283
Carter
HSPH
Biostatistics
2019 Spring
Full Term
This course is an introduction to modern statistical computing techniques used to characterize and interpret cancer genome sequencing datasets. This Master's level course will begin with a basic introduction to DNA, genes, and genomes for students with no biology background. It will then introduce cancer as an evolutionary process and review landmarks in the history of cancer genetics, and discuss the basics of sequencing technology and modern Next Generation Sequencing. The course will cover the main steps involved in turning billions of short sequencing reads into a representation of the somatic genetic alterations characterizing an individual patient's cancer, and will build on this foundation to study topics related to identifying mutations under positive selection from multiple tumors sampled in a population.

By the end of the course, students will be able to apply state-of-the art analysis to cancer genome datasets and to critically evaluate papers employing cancer genome data.

Advanced Computational Biology and Bioinformatics
BST 290 / BIOSTAT 290
Not Offered in 2018-19
HSPH / GSAS
Biostatistics
Fall
Full Term
Students will explore current topics in computational biology in a seminar format with a focus on interpretation of `omics data. They will develop skills necessary for independent research using computational biology.
Course Prerequisites: BST 282/BIOSTAT 282 required.