We've collected the best resources from across the web to start your journey into data science!
The best way to get started is to come to a meeting! But for those interested in starting right now, we've created a list of resources to get you started. We'll update this page throughout the semester, so check back in!
This is our staples for those who don't have the time to go through all the resources listed.
- Datacamp.com. Incredible resource to learn data science interactively on your own time.
- Data Science Weekly. Check out their articles and interviews, and join their mailing list.
- R-bloggers. If you use R, you should already be following them.
- DataSciGuide has a ton of resources ready for you to digest.
- AnalyticsVidhya is made for people just starting out in the field. Scroll to the bottom to check out their 12-month plan!
- data.world - a new website dedicated to hosting and curating the best datasets to work with.
- Springboard's data science interview question page is a great resource to use when brushing up for an interview.
- Our Slack channel hosts a community of student data scientists.
All the podcasts below are available on your smartphone's podcast application! They're great to listen to in the morning or while you're walking to class.
- FiveThirtyEight Podcasts are a great intro for beginners to connect data problems to problems in the real world.
- Data Skeptic provide high-level descriptions of key concepts relating to data science and skepticism! Their mini episodes are a great introduction.
- Partially Derivative an irreverent, slightly lower-level podcast compared to Data Skeptic.
- Talking Machines discusses machine learning theory with some complexity.
- Learning Machines 101 regularly discusses machine learning in the real world with varying degrees of complexity.
- O'Reilly Data Show discusses important concepts in all their complexity. Ideal for knowledgeable listeners.
- Data Stories focuses on storytelling with data with the occasional nod to implementation.
Every aspiring data scientist ought to have an RSS feed to keep up with new developments in the field. Experts are constantly blogging about all topics, new and old, so non-subscribers are really missing out! If you don't have an RSS feed, find an add-on for your favorite browser, or turn your Evernote account into an RSS feed with IFTTT.
- R-bloggers is a staple. If nothing else, subscribe to them. Fair warning: they post 4-5 times per day.
- Baseball with R is exactly what it sounds like.
- KDNuggets, like R-bloggers, funnels articles from across the web and is much larger in scope.
- DataScience+ blogs about statistics, data manipulation, and visualization.
- Andrew Gelman, Ph. D., the famed Bayesian, runs a great blog with daily posts.
- Rob Hyndman, Ph. D. regularly posts about predictive modeling.
- The Unofficial Google Data Science Blog posts about complex methods. Recommended for experienced readers.
- Win-Vector is a terrific blog; however, some posts are very low-level.
- Datacamp provides interactive tutorials for learning R and Python, and the first chapter of every course is free! Our president, Adhi, can personally vouch for it!
- Downey, Think Python is a staple for learning Python.
- Wickham, R for Data Science is a free e-book written by Rstudio's . It's a great resource to get started with R and some of its core packages like dplyr and ggplot2. Advanced R users should check out Advanced R and R Packages.