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README.md missing data with naniar May 16, 2019

README.md

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

Direct 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

Intermediate R

Working with the RStudio IDE (Part 1)

Working with the RStudio IDE (Part 2)

Cleaning Data in R

Importing & Cleaning Data in R: Case Studies

  • See Guided Project: NYC Schools Perceptions

Working with Dates and Times in R

Categorical Data in the Tidyverse

Writing Functions in R

Data Manipulation in R with dplyr

Data Analysis in R, the data.table Way

  • See here for updating

  • See here for indexing

Building Processing Pipelines in data.table

Developing R Packages

  • Also see the usethis documentation

Foundations of Probability in R

  • See weeks 3 and 4

  • Also see here

Dealing With Missing Data in R

Dimensionality Reduction in R

Advanced Dimensionality Reduction in R

Foundations of Inference

Correlation and Regression

Fundamentals of Bayesian Data Analysis in R

Structural Equation Modeling with lavaan in R

Introduction to Machine Learning

Unsupervised Learning in R

Machine Learning Toolbox

Differential Expression Analysis in R with limma

Bayesian Regression Modeling with rstanarm

Forecasting Using R

Introduction to Time Series Analysis

ARIMA Modeling with R

Forecasting Product Demand in R

Nonlinear Modeling in R with GAMs

Marketing Analytics in R: Choice Modeling

  • Please see Chapter 13

Hyperparameter Tuning in R

Exploratory Data Analysis

Exploratory Data Analysis in R: Case Study

  • Please see week 4

Visualization Best Practices in R

Data Visualization with ggplot2 (Part 1)

Data Visualization with ggplot2 (Part 2)

Building Dashboards with shinydashboard

Building Dashboards with flexdashboard

Interactive Data Visualization with rbokeh

Interactive Maps with leaflet in R

Working with Geospatial Data in R

Building Web Applications in R with Shiny

Building Web Applications in R with Shiny: Case Studies

Introduction to Text Analysis in R

Text Mining with R

Sentiment Analysis in R

Sentiment Analysis in R: The Tidy Way

Analyzing Election and Polling Data in R

Analyzing US Census Data in R

Single-Cell RNA-Seq Workflows in R

Python Courses

Introduction to Python

Python for R Users

Python for MATLAB Users

Introduction to Data Science in Python

Intermediate Python for Data Science

Object-Oriented Programming in Python

Writing Efficient Python Code

Analyzing Police Activity with pandas

Interactive Data Visualization with Bokeh

Advanced NLP with spaCy

Intro to Python for Finance

SQL Courses

Intro to SQL for Data Science

  • Also see here (requires subscription)

Intermediate SQL

  • Please see the Intermediate SQL section

Intermediate SQL Server

Joining Data in SQL

  • Also see here (requires subscription)

Git Courses

Introduction to Git for Data Science

Shell Courses

Introduction to Shell for Data Science

  • Also see here

  • Also see here (requires subscription)

Spreadsheet Courses

Spreadsheet Basics

Contributing

Ways to contribute forthcoming.

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