Post-baccalaureate Certificate Program in Analytics

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Course Descriptions

  • BDS 721. Analytics. Provides an introduction to basic statistical and data analytic methods. This course covers topics such as data archetypes; exploratory data analysis; basic statistical paradigms including frequentist, likelihood and Bayesian approaches; contingency tables; sampling distributions; the Central Limit Theorem; point and interval estimation; sufficiency; tests of statistical
    significance including large sample, likelihood ratio and resampling approaches; basic random variable linear combinations; ANOVA; correlation; and linear, logistic, and Poisson regression. Course content will be delivered through lectures, hands-on lab instruction and team-based learning using multiple statistical packages (R, SAS and Stata). Traditional Lecture (3 hours)
  • BDS 722. Advanced Analytics. Continues introductions to intermediate and advanced statistical analysis methods for biomedical research. This course covers advanced regression topics, generalized linear models (GLM), generalized additive models (GAM), splines and smoothing techniques, decision trees, basic survival models, and introduces machine learning techniques (clustering, classification, regularization/penalized regression, feature selection, Bayesian methods, and unbiased estimators). Course content will be delivered through lectures and hands-on lab instruction. Traditional Lecture (3 hours)
  • BDS 723. Statistical Programming with R. This course will provide students with an introduction to statistical computing. Students will learn the core ideas of programming — functions, objects, data structures, flow control, input and output, debugging, logical design and abstraction — through writing code to assist in numerical and graphical statistical analyses. This course will emphasize the learning of statistical methods and concepts through hands-on experience with real data. Since code is also an important form of communication among scientists, students will learn how to comment and organize code. Traditional Lecture (3 hours)


  • BDS 741. Statistical Inference I. Introduces probability and distribution theory, including axioms of probability; random variables; probability mass and density functions; common discrete and continuous distributions; transformations and sums of random variables; expectations, variances, and moments; hierarchical models and mixture distributions; and properties of random samples. Traditional Lecture (3 hours)
  • BDS 750. Study Design. This course will equip doctoral-level biostatisticians and data scientists with the skills necessary to participate in the planning and analysis of biomedical, clinical, and population-based health studies. This course will cover a wide array of study designs, one and two-way classifications, nesting, blocking, factorial designs, multiple comparisons, confounding, power, sample size, and selected issues (randomization, blindness, adherence, dropout, phases) from clinical trials. Traditional Lecture (3 hours)
  • BDS 754. Principles of Programming with Python. This course will introduce fundamental programming concepts such as data structures and algorithms, object oriented programming, and the basics of building interactive applications in the python programming language. Traditional Lecture (3 hours)