Supplementing the remainder of my Master's coursework with self-learning, moocs, reading books, and projects

DIY Data Science.md

Things I want to learn

⌚ Time Series Analysis:

Purpose: Time series analysis is a frequent method used to understand the effect of a parameter over time and I'm curious to learn about how those models are built in R and the theory beneath them.

1. Using R for Time Series Analysis
2. CRAN Task View: Time Series Analysis
3. google/CausalImpact Package
4. Introduction to Statistical Analysis of Time Series

♠️ ♥️ ♣️ ♦️ Bayesian Statistics:

Purpose: Everyone seems to be talkin about Bayes theorem and it will help me get a better grounding in conditional probability theory.

1. An Intuitive (and Short) Explanation of Bayes’ Theorem
2. Conditional probability with Bayes Theorem (video)
3. Naive Bayes in Python

📊 Data Visualization:

Purpose: I want to learn how to effectively communicate my analysis through visualization. I already know how to create plots, dashboards and maps in R and uncomfortably in python and I'm now looking to broaden my knowledge and learn about new software packages and visualization theory. I'm open to courses and readings on: D3.js, tableau, bokeh, and others.

1. Data Visualization and Communication with Tableau @ Coursera
2. Communicating Data Science Results @ Coursera
3. Oral Communication for Engineering Leaders @ Coursera
4. The Visual Display of Quantitative Information by Edward Tufte @ Amazon.com

📒 Text Mining:

Purpose: I want to learn about this!

1. H2O University - Lesson 3: Classify unstructured text data with Sparkling Water
2. Tidy Text Mining with R by Julia Slige & David Robinson
3. Basic Text Mining in R

🎲 Monte Carlo Algorithms & Markov Chain Monte Carlo Algorithms (MCMC):

Purpose: I'm interested in this topic as the study of random numbers can contribute to my overall knowledgebase of machine learning and understanding of how these stochastic problems work.

1. Advanced Scientific Computing: Stochastic Optimization Methods. Monte Carlo Methods for Inference and Data Analysis

#️⃣ Social Network Analysis:

Purpose: Super interesting in learning about this topic beyond just scraping Twitter data!

1. Network Analysis and Visualization with R and igraph: Also has a PDF tutorial version
2. Social Network Analysis Labs in R and SoNIA

Neural Networks & Deep Learning:

Purpose: I want to learn about these algorithms!

1. Hacker's guide to Neural Networks
2. H2O University - Lesson 7: Try Deep Learning with H2O

📱 Software Development in R:

Purpose: I want to enhance my current skills as a R programmer and learn advanced topics that have a strong CS base and wider appeal, such as functional programing (purrr), integrating C++ with R (rcpp) and other general programming languages.

1. Mastering Software Development in R Specialization @ Coursera
2. Introduction to Rcpp

🀄 Programming in other Languages & Packages:

Purpose: I want to become more familiar with other programming langauges. It would be great to have a general understanding about hwo they work and be able to write some small programs/scripts.

1. H2O, Sparkling Water, and Steam Documentation, Data Science Algorithms, H2O University Lessons
2. Functional Programming in Scala @ Coursera
3. Meta Julia Tutorial
4. Introduction to Julia