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Training

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]