data-science-resources

Below are resources that come highly recommended from our Data Science Education team. We encourage you to review and utilize them to continue to grow your data science skills and knowledge, fine tune your technical interviewing skills, and become a no brainer hire!

Big Data

How to: Running PySpark on Google Colab (video by Flatiron instructor Rafael Carrasco)
AWS: Getting Started on Big Data (white papers, labs, guides, and more)
Google Big Query
Google Public Data

Code Challenges/Practice

Code for America (volunteer opportunities; build tools that help with local civic issues)
Data Kind (build pro bono data science projects for social organizations/non-profits)
Kaggle (mini courses and competitions)
Regex Practice (in the form of a crossword puzzle game)
Datasets, indexed by topic (millions of datasets on the web, via Google)

GitHub

How to convert a Jupyter notebook to GitHub README - 1
How to convert a Jupyter notebook to GitHub README - 2
Guidelines for a good GitHub README (plus visual elements [graphs, gifs], just as you would present at science fair)

O-Notation

Python Practice

Learn Python the Hard Way (book)
Codewars (practice code challenges for coders at all levels)
[Sentdex] (https://www.youtube.com/user/sentdex) (for Python Development)

SQL Practice

Supplemental Courses/Learning

Coursera - Improving your statistical inferences (ecourse)
Khan Academy - Probability and Statistics (ecourse)
Udemy - Data Structure and Algorithms (ecourse)
NLP and Deep Learning (free Stanford video lectures that dive into high level NLP topics)
Coursera - Machine Learning (weeks 4 and 5 courses particularly recommended)
AWS (in-person events in NYC, San Francisco, Tokyo, Berlin, Stockholm)

Social Media - Who & What to Follow to Stay Relevant in the Field

Twitter As a data science student, you should absolutely be on Twitter; it's a great place to connect with prominent data scientists and companies in the data science space, to learn about jobs, and to keep up to date on industry trends. Here are some recommended people to follow:

Chris Albon (lots of great tutorials online)
[Yann LeCun] (https://twitter.com/ylecun?lang=en) (Turing Award Winner and Facebook Chief AI Scientist. Follow him on LinkedIn as well)
[Jeremy Howard] (https://twitter.com/jeremyphoward?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) (distinguished research scientist)
Rachel Thomas (Director of USF Center for Applied Data Ethics)
Vicki Boykis (author of the Normcore Tech blog)
Cassie Kozyrkov (Google)
@NateSilver58 (no intro needed)
@FiveThirtyEight (data-driven news by Nate Silver)
@drob (a pretty big R guy with great blogposts)
@SethS_D (author of the data science book "Everybody Lies")
@BecomingDataSci - Data Science Renee

Reddit

[Machine Learning] (https://www.reddit.com/r/MachineLearning/)
[Flask (for building portfolios)] (https://www.reddit.com/r/flask/)

Slack

15 Data Science Slack communities to join

Confererences

[PyData] (https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w)
[ICML] (https://icml.cc/virtual/2020/workshops)

Online Networking, News, Jobs, etc.

[Lex Fridman Podcast] (https://lexfridman.com/podcast/) (Research scientist at MIT)
[Towards Data Science] (https://towardsdatascience.com/) (Medium publication sharing concepts, ideas, and codes)
KDnuggets (Newsletter on AI, Data Science, and Machine Learning)
Data Science Weekly (This newsletter sends out job posts weekly)
Matt Turck (Matt runs a DS VC hedge fund. His blog is informative and he sets the standard for data science market segmentation; clicking link automatically subscribes you)
OReilly (ebooks, news/trends, conference listings, in tech and business)

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