Course Descriptions

• Basic and Translational Cancer Biology
  Course Detail

  GS04 1235 (5 credits)
  Spring

  This Cancer Biology Core course aims to consolidate essential knowledge of human cancer biology, providing insights into disease development, multifaceted molecular signatures, diagnostics, and therapeutics. It will utilize seminal articles in the field of cancer biology, primary research publications, and incorporate the expertise of GSBS faculty to convey foundational information and the latest advancements in basic, translational, and clinical cancer research.

  Auditing this is permitted with Course Directors' approval.     

  >> Curriculum Committee commended course for Academic Year 2020-2021 <<

• Biostatistics for Life Scientists
  Course Detail

  GS14 1612 (2 credits)
  Spring

  This is an entry-to-intermediate level course of biostatistics aimed at scientists in the life sciences. During the first half of the semester, the course will introduce students to the basic concepts and statistical tests that are routinely encountered in analyzing scientific data in designed experiments, as opposed to the analysis of clinical or epidemiological type data. Following an introduction to probability, students will learn what statistical tests are appropriate and how to run them. Emphasis is on intelligent usage rather than mathematical formality. Standard tests such as t, z, chi squared, ANOVA and regression analyses will be learned, as well as how power analyses and calculating sample size is performed. During the second half of the semester, advanced topics in life sciences, including Poisson distributions, clustering methods and multidimensional analyses will be covered. Another goal of this course will be to build familiarity with the basic R toolkit for statistical analysis and graphics.

• Foundations of Biomedical Research for Quantitative Students
  Course Detail

  GS21 1018 (7 credits)
  Fall

  Enrollment in this course is limited to GSBS first-year and second-year students who will pursue the quantitative degree track. 

  This course will provide incoming graduate students with a broad overview of modern biomedical sciences, spanning historical perspectives to cutting edge approaches.  The course combines traditional didactic lectures and interactive critical thinking and problem solving exercises to provide students with a strong background in fundamental graduate-level topics including genetics, molecular and cellular biology, biochemistry, physiology, developmental biology and biostatistics. This is the GSBS Core Course which will be graded pass/fail and together with Introduction to Biostatistics and Bioinformatics (GS01 1033) fulfills the GSBS breadth requirement for quantitative-track students.

  >> Curriculum Committee Commended Course for Academic Year 2021-2022 <<

  Course Web Page

• Foundations of Statistical Inference II
  Course Detail

  GS01 1283 (3 credits)
  Spring

  This is the second semester course in a two-semester sequence in mathematical statistics.  The course topics include random variables, distributions, small and large sample theorems of decision theory and Bayesian methods, hypothesis testing, point estimation, and confidence intervals; also topics such as exponential families, univariate and multivariate linear models, and nonparametric inference will also be discussed.  This course is cross-listed at Rice STAT 533.  The venue of the course will be at Rice University. Audit is permitted with Course Director's consent. 

• Genetics and Human Disease
  Course Detail

  GS11 1013 (3 credits)
  Fall

  This course introduces principles and methods of human genetic analysis with special reference to the contribution of genes to our burden of disease. Although molecular, biochemical and morphogenic processes controlled by genes will be briefly surveyed, the aim is to describe the analytical processes whereby genetic mechanisms are inferred and genes located on chromosomes. 

• Introduction to Bioinformatics
  Course Detail

  GS01 1143 (3 credits)
  Spring

  This course is intended to be an introduction to concepts and methods in bioinformatics with a focus on analyzing data merging from high throughput experimental pipelines such as next-gen sequencing. Students will be exposed to algorithms and software tools involved in various aspects of data processing and biological interpretation. Though some prior programming experience is highly recommended, it is not a requirement.

• Introduction to Biostatistics and Clinical Trials
  Course Detail

  GS01 1033 (3 credits)
  Spring

  This course is a one-semester overview of statistical concepts most often used in the design and analysis of biomedical studies. It provides an introduction to the analysis of biomedical and epidemiological data. The focus is on non-model-based solutions to one sample and two sample problems. The course also includes an overview of statistical genetics and bioinformatics concepts. Because this course is primarily for statistics majors, the applied methods will be related to theory whenever practical. Students will gain experience in the general approach to data analysis and in the application of appropriate statistical methods. Emphasis will be on the similarity between various forms of analysis and reporting results in terms of measures of effect or association. Emphasis will also be given to identifying statistical assumptions and performing analyses to verify these assumptions. Because effective communication is essential to effective collaboration, students will gain experience in presenting results for statistically naive readers.

• Introduction to Statistical Genetics and Bioinformatics
  Course Detail

  GS11 1113 (3 credits)
  Fall

  This course is designed as an introduction to statistical genetics/computational biology, and serves as the entry point to several courses in this area. It reviews the key statistical concepts and methods relevant to statistical genetics, discusses various topics that have significant statistical component in genetics, particularly in population and quantitative genetics. Topics include estimation of gene frequencies, segregation analysis, test of genetic linkage, genetics of quantitative characters, inheritance of complex characters, forensic science and paternity testing, phylogeny and data mining. This course is cross-listed at School of Public Health (PH1986L). The venue will be at School of Public Health.

• Modern Nonparametrics
  Course Detail

  GS01 1273 (3 credits)
  Spring

  This course seeks to introduce students to the many developments in modern nonparametrics, including resampling methods, nonparametric and semiparametric regression models that have occurred over the last several decades. Topics include the bootstrap, jackknife, cross-validation, permutation tests, classification tree, random forests, nonparametric smoothing and regression, spline regression, and functional data analysis. While the course will focus on applications, time will be devoted to derivations and theoretical justifications of methods. The statistical software R will be used for the homework exercises. 

• Quantitative Sciences Student Seminar Series
  Course Detail

  GS01 1031 (1 credits)
  Fall

  This series is held bi-weekly for students to present their research project in front of their peers and program faculty.  The focus of the session is for the students to practice presenting their project to a varied audience of peers and mentors.  Attendees should be prepared to ask questions of the speaker and to provide constructive criticism.  This is a required course for all QS Program students and participation is mandatory.  All QS students must register for this course every semester unless the student has a direct course conflict.  QS-affiliated students are expected to give a minimum of two talks; one pre-candidacy and one post-candidacy, and secondary ARC students are expected to give a minimum of one talk.

• Statistical Communication, Consulting, and Collaborative Data Science
  Course Detail

  GS01 1022 (2 credits)
  Spring

  This course is designed to help students build essential statistical communication skills that are often underemphasized in traditional training. It focuses on preparing students to collaborate effectively with researchers from diverse backgrounds by teaching them how to:

  • Effectively interview collaborators to understand their research questions and objectives,
  • Articulate mutual goals and expectations specific to statistical consulting and interdisciplinary collaboration,
  • Define statistical objectives and deliverables that can guide the research process, and
  • Provide regular progress updates in a clear and actionable manner.

  Through two core components—a consulting clinic and a project-based learning curriculum—students gain hands-on experience in applying statistical and data science knowledge while refining their communication and collaboration skills.

  In the consulting clinic, students offer pro bono statistical consulting services to UTHealth/MDA community researchers, working under instructor supervision to apply these skills in real-world settings. The project-based learning component focuses on project scoping, collaborative practices, and reproducible workflows, equipping students with tools to translate research questions into actionable solutions and foster productive interdisciplinary collaborations.

  This course will prepare students for professional collaborations in academic and industry settings.

  Prerequires: Students are expected to have knowledge in basic Statistics Inference, Probability, and Linear Regression. Prior programming experience in R or Python is required.

  Note: Students who are interested in the course but not sure whether they meet the prerequisites can contact the course directors. If the registration number goes above 12, the course directors will make decisions on who to admit to the course.

  Priorities will be given to QS program 1st and 2nd year PhD students and those who meet the prerequisites. The directors will provide guidance on preparing the prerequisites, through taking other basic statistical courses in class or through Coursera courses, for those who are interested in taking it in future.

• Survival Analysis
  Course Detail

  GS01 1023 (3 credits)
  Spring

  Survival data are commonly encountered in scientific investigations, especially in clinical trials and epidemiologic studies.  In this course, commonly used statistical methods for the analysis of failure-time data will be discussed.  One of the primary topics is the estimation of survival function based on censored data, which include parametric failure-time models, and nonparametric Kaplan-Meier estimates of the survival distribution.  Estimation of the cumulative hazard function and the context of hypothesis testing for survival data will be covered.  These tests include the log rank test, generalized log-rank tests, and some non-ranked based test statistics.  Regression analysis for censored survival data is the most applicable to clinical trials and applied work.  The Cox proportional hazard mode, additive risk model, other alternative modeling techniques, and new theoretical and methodological advances in survival analysis will be discussed.

• Topics in Clinical Trials
  Course Detail

  GS01 1813 (3 credits)
  Fall

  This course will provide an overview of methods for the design and analysis of clinical trials. Topics will include fundamental principles and commonly used designs for phases I, II and III trials. Advanced topics will include flaws with many conventional methods, hybrid designs, dealing with multiple outcomes, bias correction, precision medicine, and Bayesian methods. This course is cross-listed at Rice University as STAT 630.