TOdo.md

Learning resources to work through

General Data Science stats

• Do this before the Machine Learning below.

• Harvard CS09 course. Lots of tutorials and lectures covering everything from pandas, web scraping to bayesian stats.

  • perhaps start with the homeworks and labs which also have solutions here

Baysian Statistics

• Very important to get a decent grounding in this: [Programming and Bayesian Methods for hackers](http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/ Prologue/Prologue.ipynb)

• Recommended reading from S2DS. Think Bayes.

  • Some problems, and solutions, from the Allen Downey's Blog

• From the Harvard course:

  • Bayesian Tomatoes
  • Lab 6: [Bayesianism, with MCMC] (http://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab6/BayesLinear.ipynb)

Machine Learning

• Work through the ISLR book (downloaded) with this python repo for guidance.

• Learn and understand this introduction from the python lib scikit. I also like this reference map.

• Clone this repository and work through.

• Do the MonkeyLearn tutorials [here] (https://blog.monkeylearn.com/getting-actionable-insights-from-reviews-using-machine-learning-part1/?utm_source=Email&utm_medium= Newsletter& utm_campaign=actionable-insights-reviews-using-machine-learning-part1) and[here] (https://blog.monkeylearn.com/hacker-news-categorizer-with-monkeylearn/?utm_source=Email&utm_medium=Intercom&utm_content=FP& utm_campaign=16-hacker-news-categorizer)

• Very quick and dirty introduction to Random Forests using python and iris data.

• From harvard data science course Lab 4: Scikit-Learn, Regression, and PCA

• Statistical Natural Language Processing

• A cheatsheet of sorts for scikit-learn in Python using pandas - 'Python Machine Learning'

• Scikit.learn for large data sets article and tutorial

Social Network Analysis

• Udacity course on Algorithms

SQL

• Complete this excellent tutorial playing around with astronomy data. Done!

• SQL Zoo is a a good tutorial site. Work through these.

• Do this basic tutorial, followed by this more advanced one. Looks good with lots of questions (+ answers!).

• Dip into this tutorial which goes through common queries from MySQL.

Pandas and Python

• Start working through exorcism.io

• How to think like a computer scientist with clear explanations, videos and tests you as you go through.

• Work through these Python 3 tutorial videos. They are nice and short.

• Matplotlib visualisation tutorial which encorporates sentiment analysis with suggested futher exercises.

• Statistical Natural Language Processing

Kaggle

• Do the Titanic competition.
  • Start with this for Python and then again, but using pandas.
  • Try tutorial in Machine Learning DataBase (MLDB)

Visualisation

• Python interactive visualization library that targets modern web browsers for presentation:Bokeh
• Plotly
• Getting started with d3

git

• Work through this course.