hieuqtran / data_sci_guide

Repo Purpose

Welcome to your community-sourced data science repo! The overarching goal here is to provide anyone interested in learning data science with a wealth of open source, industry-best learning materials and learning tracks.

This repo is a work in progress. Please check back for updates. @momiji15, @Annu-07, and I are collaborating on the structure for this repo. If you would like to be involved in that process, please file an issue in this repo and we will add you to our Slack channel.

This repo is motivated by recent incidents. The data science community deserves better, and this repo is an attempt to provide a platform for the excellent learning resources available.

Guided Data Science Resources

• Dataquest
• Software Carpentry Lessons
• Data Carpentry Lessons
• Chromebook Data Science
• Business Science University

Course Replacements

Many instructors have admirably advocated against taking their own DataCamp courses. Often, these instructors have suggested other ways in which learners can access the same material. The suggested replacements for their courses are listed below:

R Courses

Introduction to R

Writing Functions in R

• Next, learn about iteration

Building Processing Pipelines in data.table

Developing R Packages

• Also see the usethis documentation

Text Mining with R

• Also see the associated blogs and tutorials

Building Dashboards with shinydashboard

Marketing Analytics in R: Choice Modeling

• Please see Chapter 13

Introduction to Machine Learning

Machine Learning Toolbox

Differential Expression Analysis in R with limma

Bayesian Regression Modeling with rstanarm

• Also see a walkthrough article and a practical example

Forecasting Using R

Nonlinear Modeling in R with GAMs

Working with Geospatial Data in R

Exploratory Data Analysis

Sentiment Analysis in R

Sentiment Analysis in R: The Tidy Way

[Introduction to Shell for Data Science](https://librarycarpentry.org/lc-shell/ http://swcarpentry.github.io/shell-novice/)

Python Courses

Introduction to Python

Interactive Data Visualization with Bokeh

Advanced NLP with spaCy

Intermediate Python for Data Science

Contributing

Ways to contribute forthcoming.