Biostatistics Core Student Resources Draft Page

Resources for Students

Below are some courses offered at UConn that might be useful for students interested in learning about methodologies and statistical techniques for health-related research. Please check with the department offering the course for up-to-date information about software, schedule, and prerequisites.

Department of Statistics

Measurement, Evaluation, and Assessment Program, Department of Educational Psychology

Department of Psychological Sciences

Below are some websites and online courses that might be helpful for students and researchers looking for an introduction to statistical theory and analysis. Please check with the websites offering the courses for up-to-date information about fees, topics covered, and other information.

EdX: Statistics and Data Science Courses

  • Courses are Free
  • Certificate is Available for a Fee
  • Some courses are self-paced, while others have specific start and end dates

Courses in R

Introduction to R for Data Science
Learn the R statistical programming language, the lingua franca of data science in this hands-on course.

Explore Statistics with R
Learn basic statistics in a practical, experimental way, through statistical programming with R, using examples from the health sciences.

Programming with R for Data Science
Learn the fundamentals of programming with R, from reading and writing data to customizing visualizations and performing predictive analysis.

Statistics and R
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Statistical Theory

Probability: Basic Concepts & Discrete Random Variables
Learn fundamental concepts of mathematical probability to prepare for a career in the growing field of information and data science.

Probability: Distribution Models & Continuous Random Variables
Learn about probability distribution models, including normal distribution, and continuous random variables to prepare for a career in information and data science.

Foundations of Data Analysis – Part 2: Inferential Statistics
Use R to learn the fundamental statistical topic of basic inferential statistics.

Applied Statistics & Biostatistics

Introduction to Statistics: Descriptive Statistics
An introduction to descriptive statistics, emphasizing critical thinking and clear communication.

High-Dimensional Data Analysis
A focus on several techniques that are widely used in the analysis of high-dimensional data. Factor analysis, clustering, heat maps, etc.

Introduction to Applied Biostatistics: Statistics for Medical Research
Learn data analysis for medical research with practical hands-on examples using R Commander.

Coursera: Data Science Courses

  • Enrollment requires a fee
  • Certificate is available after course completion
  • Some courses are self-paced, while others have specific start and end dates

Course Sequenences in Statistical Specializations

Statistics with R
Learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. The student will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

Methods and Statistics in Social Sciences
Covers research methods, design and statistical analysis for social science research questions. In the final Capstone Project, the student will apply the skills learned by developing their own research question, gathering data, and analyzing and reporting on the results using statistical methods.

Data Science
Covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, the student will apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

Machine Learning
Provides a case-based introduction to the exciting, high-demand field of machine learning. Learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, the student will apply their skills to solve an original, real-world problem through implementation of machine learning algorithms.

Probabilistic Graphical Models
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Khan Academy: Statistics & Probability Topics

  • Courses are Free
  • All Courses are Self-paced

Ben Lambert: Tutoring Videos

  • Video courses include the following topics:
    • A full undergraduate course in econometrics
    • A graduate course in econometrics
    • A course in Asymptotic Behavior of Estimators
    • A short course on Factor Analysis and SEM
  • YouTube Channel:

The Biostatistics and Methodology Core maintains a list of students who are interested in parterning with faculty as a research assistant on health-related projects. If you are a graduate student with statistical expertise, and would like to be considered as a potential research assistant for investigators who contact the Core, please fill out the application form:

Submit Information for Research Assistant List