Teaching

I enjoy teaching data science and statistics to researchers, students, and practitioners. My goal is always the same: to make quantitative methods accessible and practical — to give people the confidence and skills to work with data independently.

Areas I teach

Python for Data Science

From fundamentals to applied analysis — pandas, NumPy, matplotlib, scikit-learn, and beyond. Focused on practical workflows for researchers and analysts.

R for Statistics & Bioinformatics

Data wrangling with tidyverse, statistical modelling, visualisation with ggplot2, and bioconductor packages for genomic analysis.

SQL & Data Management

Querying, structuring, and managing clinical and research datasets. Designed for researchers who need to work with relational databases.

Machine Learning

Supervised and unsupervised methods, model validation, and interpretation — with a focus on healthcare and life science applications.

Genomic Data Analysis

Working with sequencing data, GWAS, population structure, and bioinformatics pipelines. Aimed at life scientists making the transition to computational methods.

Biostatistics

Statistical inference, regression, survival analysis, and study design for clinical and epidemiological research.

Formats

I offer teaching in a variety of formats depending on your needs:

  • One-to-one tutoring — personalised sessions working through your own data and questions
  • Group workshops — half-day or full-day workshops for research teams or student cohorts
  • Online courses — structured self-paced learning with live Q&A support
  • Consulting — short-term analytical support for projects with a specific statistical or machine learning challenge

Get in touch

If you are interested in any of the above, please write to me at drdaviddelorenzo@gmail.com. I am happy to discuss your needs and find an approach that works for you.