Skip to Content## R, Genomics, and Statistics

- Experimental Design [University of Cambridge]
- Introduction to Machine Learning [UoC]
- Introduction to Statistical Analysis [UoC]
- Introduction to Genomic Technologies [Johns Hopkins University]
- Linear Modelling with R [UoC]
- R for Biologists Bootcamp [UoC]
- Statistical Analysis using R [UoC]
- Solving Biological Problems with R [UoC]
- Data Analysis and Visualisation in R [UoC]
- R object-oriented programming and package development [UoC]
- R Programming [JHU]
- Introduction to Probability and Data [Duke University]

## NGS Data Analysis

- Analysis of single cell RNA-seq data. [UoC]
- Analysis of bulk RNA-seq data. [UoC]
- Transcriptome Analysis for Non-Model Organisms. [UoC]
- Extracting biological information from gene lists. [UoC]
- Next Generation Sequencing Platforms and Bioinformatics Analysis. [UoC]
- Analysis of gene regulatory sequencing data: ChIP-seq, ATAC-seq and Hi-C. [UoC]
- Analysis of mapped NGS data with SeqMonk. [UoC]

## Data Science and Python

- Getting and Cleaning Data [JHU]
- Exploratory Data Analysis [JHU]
- Data Science in Python. [UoC]
- Solving Biological Problems with Python. [UoC]
- Working with Python: functions and modules. [UoC]
- Introduction to Python [DataCamp]
- Intermediate Python for Data Science [DataCamp]

## Reproducible Research

- Snakemake Workshop [UoC]
- The Data Scientist’s Toolbox [JHU]
- Command Line Tools for Genomic Data Science [JHU]
- Reproducible Research [JHU]