The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

  • SPH BS 700: Essentials of Biostatistics
    Graduate Prerequisites: MPH students cannot receive degree credit for BS700.
    This intensive one-week course provides a comprehensive introduction to the use of biostatistics in the field of public health. Students learn to compute and interpret descriptive and inferential statistics. Topics include descriptive statistics and graphical displays of data, probability, confidence intervals, hypothesis testing for means and proportions, linear and logistic regression and survival analysis.
  • SPH BS 704: Introduction to Biostatistics
    This course provides an overview of biostatistical methods, and gives students the skills to perform, present, and interpret basic statistical analyses. Topics include the collection, classification, and presentation of descriptive data; the rationale of estimation and hypothesis testing; analysis of variance; analysis of contingency tables; correlation and regression analysis; multiple regression, logistic regression, and the statistical control of confounding; sample size and power considerations; survival analysis. Special attention is directed to the ability to recognize and interpret statistical procedures in articles from the current literature. Students will use the R statistical package to analyze public health related data.
  • SPH BS 722: Design and Conduct of Clinical Trials
    Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of instructor.
    This course covers the development, conduct, and interpretation of clinical trials. It is suitable for concentrators in any department. Topics include principles and practical features such as choice of experimental design, choice of controls, sample size determination, methods of randomization, adverse event monitoring, research ethics, informed consent, data management, and statistical analysis issues. Students write a clinical trial protocol during the semester.
  • SPH BS 723: Introduction to Statistical Computing
    Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of instructor.
    This course introduces students to statistical computing with focus on the SAS package. Emphasis is on manipulating data sets and basic statistical procedures such as t-tests, chi-square tests, correlation and regression. Conditions underlying the appropriate use of these statistical procedures are reviewed. Upon completion of this course, the student will be able to use SAS to: read raw data files and SAS data sets, subset data, create SAS variables, recode data values, analyze data and summarize the results using the statistical methods enumerated above. This course includes hands-on exercises and projects designed to facilitate understanding of all the topics covered in the course. Students use equipment and software available through the Boston University Medical Center. This course is a prerequisite for these SPH courses: BS805, BS820, BS821, BS851, BS852, BS853 and BS858.
  • SPH BS 728: Public Health Surveillance,a Methods Based Approach
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or permission of instructor are required.
    Thacker wrote, "Surveillance is the cornerstone of public health practice." This course will provide an introduction to surveillance and explore its connections to biostatistics and public health practice. Topics will include complex survey design, weighted sampling, capture-recapture methods, time series analyses and basic spatial analyses. Students will learn about available surveillance data, how to analyze these data, and how to write about their findings. This class carries Epidemiology concentration credit.
  • SPH BS 730: Introduction to R: software for statistical computing
    Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of instructor.
    Students will learn how to conduct statistical analysis using the public domain and free statistical software, R. Many public, private, and international organizations use R to conduct analysis, thus experience with R is a great skill to add to one's credentials. R offers flexibility, ranging from ease of writing code for simple tasks (e.g. using R as a calculator) to implementing complex analyses using cutting-edge statistical methods and models. Additionally, the R language provides a rich environment for working with data, especially for statistical modeling, graphics, and data visualization. This course will emphasize data manipulation and basic statistical analysis including exploratory data analysis, classical statistical tests, categorical data analysis, and regression. Students will be able to identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This hands-on and project-based course will enable students to develop skills to solve statistical problems using R. R can be used as an alternative or in addition to SAS (BS723). R is compatible with Apple OS, Windows, and Unix environments.
  • SPH BS 750: Essentials of Quantitative Data Management
    Graduate Prerequisites: SPH BS723 Introduction to Statistical Computing
    Any data analysis is only is good as the data on which it is based. This course will focus on the importance of high quality data and the skills required for effective data management, including collection, cleaning, auditing, and merging. Students will have hands-on experience with data sets. Examples of what can go wrong and how research can be complicated by or produce erroneous results due to poor quality data will be provided.
  • SPH BS 775: Applications of Statistical Methods in Clinical Research
    Graduate Prerequisites: The biostatistics MPH core requirement and SPH BS723 or consent.
    This course provides a non-technical (no computer programming) overview of concepts in statistical methods used for clinical research and their applications. Each week, students read a methodologic article and a clinical research article. The first portion of the class is a didactic presentation; the second portion is a discussion of the clinical research article, incorporating the concepts discussed in the didactic presentation. Students explore statistical test selection, alternative tests or approaches. Students examine interpretations of scientific articles in the lay press.
  • SPH BS 800: Accelerated Statistical Training
    Graduate Prerequisites: Calculus I and II, including multivariable calculus, and linear algebra to cover matrix operations, matrix functions, and singular value decomposition.
    This course is designed for the newly developed MS in Applied Biostatistics program and will cover concepts of descriptive statistics and exploratory data analysis, measures of association in epidemiological studies, probability, statistical inference and computing in R and SAS. It is intended to equip students enrolling in the MS in Applied Biostatistics program with sufficient probability, statistics and computing background to enter 800 levels courses and finish the MS program within a year. The course will be offered during the 3 weeks preceding the Fall semester, and will involve 15 day-long modules. Modules will generally run from 10am to 5pm, combining a traditional lecture (10am to 12pm), a practice session in which students will practice the notions learned in class through exercises (1pm to 2:30pm), and a computer lab (3pm to 5pm) in which the students will learn basic computing in R and SAS and also apply the notions learned in class to real data. Please note one year of calculus to include multivariable calculus and linear algebra are prerequisites for this course. Allowing a student to waive this course is at the discretion of program directors, Paola Sebastiani and Yorghos Tripodis. [2 cr.]
  • SPH BS 803: Statistical Programming for Biostatisticians
    Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of the instructor.
    This course will focus on skills required for advanced computing applications in biostatistics. Students will learn statistical programming and methods such as loops, functions, macros as well as data visualization techniques in SAS and R. Furthermore, the course will provide and introduction to Linux and basic statistical programming in Python. Lab sessions S will also provide students with basic computing skills to enroll to more advanced statistical classes such as BS830 and BS857.
  • SPH BS 805: Intermediate Statistical Computing and Applied Regression Analysis
    Graduate Prerequisites: Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of the instructor. BS805 and BS806 cannot both be taken for credit. It is not recommended that BS805 and BS852 be taken concurrently. BS805
    This course is a sequel to BS723. Emphasis is placed on the use of intermediate-level programming with the SAS statistical computer package to perform analyses using statistical models with emphasis on linear models. Computing topics include advanced data file manipulation, concatenating and merging data sets, working with date variables, array and do-loop programming, and macro construction. Statistical topics include analysis of variance and covariance, multiple linear regression, principal component and factor analysis, linear models for correlated data, and statistical power. Includes a required lab section.
  • SPH BS 806: Multivariable Analysis for Biostatisticians
    Graduate Prerequisites: Cannot be taken concurrently with BS805. BS805 and BS806 cannot both be taken for credit. Calculus I and II, including multivariable calculus, and linear algebra to cover matrix operations, matrix fun
    This course will focus on skills required for effective conduct of data analysis. This course will focus on the multiple regression modeling and multivariate analysis to cover multi-way anova, multiple linear regression, classification and regression trees, automated model search, model fit and diagnostic, experimental design and multivariate analysis (PCA and cluster analysis) with particular emphasis on applications in medicine and public health.
  • SPH BS 810: Meta-Analysis for Public Health & Medical Research
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of instructor.
    Meta-analysis is the statistical analysis of research findings and is widely used in public health and medical research. Typically meta-analysis is employed to provide summary results of the research in an area, but other uses include exploratory analyses to find types of subjects who best respond to a treatment or find study-level factors that affect outcomes. The course will cover the theory and use of the most common meta-analytic methods, the interpretation and limitations of results from these methods, diagnostic procedures, and some advanced topics with a focus on public health application. Grading will be based on homework, an exam and a project.
  • SPH BS 820: Logistic Regression and Survival Analysis
    Graduate Prerequisites: The biostatistics and epidemiology MPH core course requirements and BS723 or BS852.
    This course provides basic knowledge of logistic regression and analysis of survival data. Regression modeling of categorical or time-to-event outcomes with continuous and categorical predictors is covered. Checking of model assumptions, goodness of fit, use of maximum likelihood to determine estimates and test hypotheses, use of descriptive and diagnostic plots are emphasized. The SAS statistical package is used to perform analyses. Grading will be based on homework and exams.
  • SPH BS 821: Categorical Data Analysis
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of instructor.
    This course focuses on the statistical analysis of categorical outcome data. Topics include the binomial and Poisson distributions, logistic and Poisson regression, nonparametric methods for ordinal data, smoothed regression modeling, the analysis of correlated categorical outcome data, cluster analysis, missing data and sample size calculations. The course emphasizes practical application and makes extensive use of the SAS and R programming languages.
  • SPH BS 822: Advanced Methods in Statistical Computing
    Graduate Prerequisites: SPH BS805 & linear algebra (CAS 142 or equivalent) or permission
    This course introduces advanced statistical methods and programming techniques that allow students to examine advanced statistical models that go beyond that available with standard SAS procedures taught in BS805. Topics include simulation studies, bootstrapping and Bayesian analysis. Students will apply these methods in homework assignments.
  • SPH BS 825: Advanced Methods in Infectious Disease Epidemiology
    Graduate Prerequisites: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800; or consent of the instructor.
    This course aims to introduce students to statistical and mathematical methods used in infectious disease epidemiology. Students will be able to evaluate and appraise the literature in this field, be able to select which methods to use in different circumstances, implement some methods in simple situations and we will provide sufficient background reading that students can further examine methods that are of particular interest. This will be a hands-on course involving class discussions, computer lab sessions and a class debate on a controversial topic in infectious disease epidemiology.
  • SPH BS 831: Genomics Data Mining and Statistics
    Graduate Prerequisites: Graduate Prerequisites: Knowledge of basic statistics techniques (BS704 or equivalent) and basic statistical computing skills using R (BS730 or equivalent) are required, or permission from the instr
    The goal of this course is for the students to develop a good understanding and hands-on skills in the design and analysis of data from microarray and high-throughput sequencing experiments, including data collection and management, statistical techniques for the identification of genes that have differential expression in different biological conditions, development of prognostic and diagnostic models for molecular classification, and the identification of new disease taxonomies based on their molecular profile. These topics will be taught using real examples, extensively documented hands-on's, class discussion and critical reading. Students will be asked to analyze real gene expression data sets in their homeworks and final project. Principles of reproducible research will be emphasized, and students will become proficient in the use of the statistical language R (an advanced beginners knowledge of the language is expected of the students entering the class) and associated packages (including Bioconductor), and in the use of R markdown (and/or electronic notebooks) for the redaction of analysis reports.
  • SPH BS 835: Applied Intermediate Biostatistics
    This course covers intermediate-level statistical methods commonly used in epidemiologic and public health research. The course has an applied focus, with emphasis on understanding research questions addressed by these methods, key assumptions these analyses rely on, and the presentation and interpretation of results. Students will use either the SAS or R statistical package to carry out analyses. Topics include multivariable regression models for continuous, binary, survival, and longitudinal outcome data, stratified and matched analyses of epidemiologic data, and analysis of survival data. This course will provide the student with training in intermediate level biostatistical analyses and the use of biostatistical software. Prerequisites: BS723 or BS730. Students with a strong interest in statistical program and a strong mathematical background are encouraged to take BS805 and BS852. Students cannot take both this course and BS852.
  • SPH BS 845: Applied Statistical Modeling and Programming in R
    Graduate Prerequisites: SPH BS 723 or SPH BS 730; or consent of instructor.
    This course covers applications of modern statistical methods using R, a free and open source statistical computing package with powerful yet intuitive graphic tools. R is under more active development for new methods than other packages. We will first review data manipulation and programming in R, then cover theory and applications in R for topics such as linear and smooth regressions, survival analysis, mixed effects model, tree based methods, multivariate analysis, boot strapping and permutation.