My main goal at teaching is that the students seek a deep understanding of new or relevant concepts, which I pursue by encouraging active learning and bringing to the classroom real world problems. It is important that students not only learn and review statistical concepts, but also grasp their relevance and need to solve meaningful problems.

STAT450: Case Studies in Statistics

Statistics undergraduate students taking this case studies capstone course analyze real data provided by a researcher from a different discipline. More information on cases eligibility is available here. I’ve been teaching this course since 2013. The main learning objective of the course are for students:

  • to critically appraise study designs and data
  • to identify appropriate choices of statistical methodologies and apply them to solve real problems
  • to develop skills of working with non-statisticians
  • to communicate effectively, both orally and in writing, with statisticians and non-statisticians

STAT550: Techniques of Statistical Consulting

The overall objective of this course is to train students to develop the skills required to work with non-statisticians either as consultant or collaborator. Students mentor and supervise underdergraduate students from STAT450 on their consulting case study. They also work independently on an extension of the analysis provided by STAT450. The communciation among students from both courses, instructors and clients is maintained through discussions in a private Github/Gitlab repository and/or Slack. I have been teaching this course, in coordination with STAT450, since 2015. Specific learning objectives are to develop:

  • the techniques required to attain a clear understanding of a client’s needs
  • understanding of the processes involved in solving statistical problems
  • oral and written skills that facilitate communication with clients
  • working knowledge to participate in interdisciplinary collaborative models and statistical consulting

STAT540: Statistical Methods for High Dimensional Biology

This course aims to provide the students with modern and up-to-date statistical tools to analyze genomics and epigenetics data, including empirical bayes linear models estimation and inference, principal component analysis, cluster analysis, classification and regularized regression, gene set analysis, resampling and bootstrapping. I taught this course from 2013 till 2015. In 2015, I’ve built the Github organization repository that hosts the course website, students repositories, and all the course material. The learning objectives of the course are:

  • to develop a primary background in molecular biology and statistical techniques that are particularly relevant for the analysis of genomics data
  • to examine typical problems of high throughput biological data
  • to explore, visualize, and analyze large datasets emphasizing on the reproducibility of the analysis
  • to promote interdisciplinary and collaborative work