Recommended Documents for New Data Students in the Digital Collaboration Team. None of these links are mandatory, but the Useful Technical Links and Good Readings on Data Science sections will be immediately useful, whereas the Lanuage Introduction section is meant more as a reference.
- Python
- R
- JavaScript
- SQL
- Free Introduction to R (DataCamp)
- R For Data Science (Can be used as a reference)
- R Tidyverse Style Guide
Use whatever IDE you feel comfortable with. Here are some recommendations
- For Python and Javascript: Visual Studio Code
- For Exploratory Data Analysis in Python and R: Jupyter Notebook
- For Work in R: RStudio
I highly recommend checking out Hadley Wickham's Readings in Applied Data Science Course. I've listed the readings I think are particularly relevant for us, as well as some other good articles.
- Data scientists mostly just do arithmetic and that’s a good thing: Noah Lorang, 2016
- Software development skills for data scientists: Trey Causey, 2016
- Lessons from between the white lines for isolated data scientists: Benjamin S. Baumer, 2017
- Doing Data Science at Twitter: Robert Chang, 2015
- Research Debt: Chris Olah and Shan Carter, 2017
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Simple and Multiple Linear Regression in Python Adi Bronshtein, 2017
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Introduction to Statistical Learning: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, 2017
- Only read Chapters 1 and 2. Ignore Labs and Exercises.
- Other chapters will be used as references if/when we need them.
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Omitted Variable Bias and Data Science: Chris Lavoie, 2018
- A quick article I wrote trying to explain Omitted Variable Bias, and how it might impact you in your day-to-day work