Here are some useful websites that I have found in the past
Jekyll Now: Jekyll Now makes it easier to create your Jekyll blog, by eliminating a lot of the up front setup.
DNA CONFESSES DATA SPEAKS: computational biologist working on genomics, epigenomics and transcriptomics
Dave Tang’s Blog: computational biologist who is interested in genomics & machine learning
Tim Stuart: postdoc in the Satija lab at the New York Genome Center. develops computational and experimental methods for single cell biology, with a focus on integrative multi-modal assays and analysis methods.
Undergrad in the Lab: Tips, tricks, and strategies to get the most out of your undergraduate research experience #UndergradInTheLab
Matt Might: blog.might.net is really just a collection of short articles
Matthew Young: Things I’m trying to understand. Mostly maths, programming and science.
CMDLineTips: Python and R Tips.
fiveMinuteStats: Learn statistics in R
Deeo Learning: The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular
Another Book on Data Science: There has been considerable debate over choosing R vs. Python for Data Science. Based on my limited knowledge/experience, both R and Python are great languages and are worth learning; so why not learn them together?
R Graphics Cookbook, 2nd edition: R Graphics Cookbook, a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems.
Greenleaf Lab: (sc)ATAC-seq packages and scripts from the Greenleaf LAb
Pe’er Lab: code and scripts from the Dana Pe’er Lab
Machine Learning & Deep Learning Tutorials: This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources.