We are listing some online learning resources here which we found to be particularly useful. You may find the Bioinformatics clinic sessions a useful complement to your online learning, just drop in for help with coding problems or questions about bioinformatics approaches.

Bioinformatics

Introduction to statistical methods for gene mapping: https://www.edx.org/course/introduction-to-statistical-methods-for-gene-mappi

Statistical analysis in bioinformatics: https://www.edx.org/course/statistical-analysis-in-bioinformatics-2

Introduction to genomic data science: https://www.edx.org/course/introduction-to-genomic-data-science (Archived – so this is just for viewing lectures)

Demystifying biomedical big data, a user’s guide (seems to be ongoing , but you can still join, course content will be archived after May 9 2020): https://www.edx.org/course/demystifying-biomedical-big-data-a-users-guide

Principles, statistical and computational tools for reproducible data science (Self paced): https://www.edx.org/course/principles-statistical-and-computational-tools-for

Command Line Skills

Basic introduction to the command line

Introduction to bash scripting (Dr Paul Brown)

Introduction to command line by Dr Daniel Hebenstreit (Warwick members only)

Coding in Python

Introduction to Python, absolute beginner (starts April 1) – this is a Microsoft course, and you will find there are other Microsoft Python courses that you might like to follow on from this site: https://www.edx.org/course/introduction-to-python-absolute-beginner-2

Python basics for data science (seems to be a slight step up from absolute beginner but still starts with basics) – this is an IBM course, and just as with the Microsoft course, you will find there are other IBM courses that you might like to explore (and of course you can try both the Microsoft and the IBM ones and see which you prefer): https://courses.edx.org/courses/course-v1:IBM+PY0101EN+1T2020/course/

Coding in R

Tidyverse and R Markdown https://education.rstudio.com/learn/beginner/

The Tidyverse is a collection of R packages that streamline data handling. R markdown is a way of generating documents directly from your analysis scripts that makes your code easier to share and more transparent to the reader. There are also introductory tutorials for R at this site.

Shiny R https://shiny.rstudio.com/tutorial/

Shiny is a great way to quickly create interactive web pages and reports for sharing your results. If you would like to share a Bioinformatics app over the web, the university provides a service for doing this, for which you can find some details here: https://warwick.ac.uk/services/its/servicessupport/servers/.

http://r-statistics.co/R-Tutorial.html

https://www.r-graph-gallery.com/

The R graph gallery is a great visual guide where you can see different graphs, and the code used to produce them. Watch out for some banner ads, but so long as you avoid clicking on those, this is a nice resource!

https://education.rstudio.com/learn/beginner/

Data Analysis

Statistics and R https://www.edx.org/course/statistics-and-r
A basic introduction to R and some statistics.

High Dimensional Data Analysis https://www.edx.org/course/high-dimensional-data-analysis

This course will reinforce and deepen your understanding of concepts such as classification, clustering and machine learning

Both of the above courses are from the Data Analysis for Life Sciences series from HarvardX. The whole series is available via the link below (all self-paced).
https://courses.edx.org/courses/course-v1:HarvardX+PH525.1x+1T2020/b60b30a885934cd5971b6fc620a41657/

Data analysis essentials https://www.edx.org/course/data-analysis-essentials

RNA-seq analysis introduction https://tavareshugo.github.io/data-carpentry-rnaseq/Link opens in a new window

A Note on edX Courses

https://www.edx.org/
Some courses are “archived” (where you can just watch the lectures at your own pace), others are “self paced”, where all the material is available from the start and you can go at your own pace (this may include interactive activities as well as lectures). Other courses start at specific times, and the material will be made available gradually. Courses that start at specific times have been indicated in the list below.

PLEASE NOTE that for all edX courses there is both a paid option and a free “audit” option. The “audit” option will still allow you to access the lectures. You will find that the “audit” option expires after 1 month, but that will still give you time to access the material. However, we suggest only starting the courses that you do intend to complete in a given month.