teslaa22 Goto Github PK

advdatasci16

agplus

a rapid and flexible tool for aggregation plots

aslihandincer.github.io

awesome

Awesome resources on Bioinformatics, data science, machine learning, programming language (Python, Golang, R, Perl) and miscellaneous stuff.

awesome-deepbio

A curated list of awesome deep learning applications in the field of computational biology

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-single-cell

List of software packages for single-cell data analysis, including RNA-seq, ATAC-seq, etc.

bamkit

Tools for common BAM file manipulations

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

basic_python_django

course material for teaching python and django (2-day course)

bayes-net-r-hadoop

Bayesian Network with R and Hadoop

bcbb

Useful bioinformatics code, primarily in Python and R

biotools

A list of useful bioinformatics resources

blogs

Links to data science, bioinformatics, statistics, and machine learning resources

cheatsheets

Quick lookup documents for commonly used tools

chip-seq-pipeline

ENCODE Uniform processing pipeline for ChIP-seq

cloudbiolinux

CloudBioLinux: configure virtual (or real) machines with tools for biological analyses

coffeescript

Unfancy JavaScript

combinatorial-optimization

The accompanying code for my article: https://becominghuman.ai/probabilistic-approaches-to-combinatorial-optimization-2aa0397a795f

coursera-machinelearning-python

Skeleton code for doing the coursera machine learning course in the form of an ipython notebook.

coursera-statistics-one--r

Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation for students planning to pursue more advanced courses in statistics. Friendly means exactly that. The course assumes very little background knowledge in statistics and introduces new concepts with several fun and easy to understand examples. This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Even if you are a relatively advanced researcher or analyst, this course provides a foundation and a context that helps to put one’s work into perspective. Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! What this means is you can download R, take this course, and start programming in R after just a few lectures. That said, this course is not a comprehensive guide to R or to programming in general.

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

cruzdb

python access to UCSC genomes database

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

darkforestgo

DarkForest, the Facebook Go engine.

dataanalysis

Coursera data analysis course, done in Python

dataanalysis-1

The lecture slides for Coursera's Data Analysis class

datasciquotes

Data Science related quotes

dataviz

A book covering the fundamentals of data visualization.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"