This page curates great resources for learning Data Science. I will update this page as I learn more and more about Data Science.
Udacity's Intro to Machine Learning. A very good place to start and pretty fun.
Andrew Ng's Machine Learning course. This lecture series is a classic!
Stanford's Computer Vision course (CS 231n). Highly recommend this course to learn about Neural Networks, CNNs, RNNs, GANs, modern advances in computer vision. Incredibly up-to-date material; check out the course webpage for more.
Daniel Soper's Introduction to Databases. This may be very basic for you or a much needed source of information! Either way, it's an excellent series to check out. Links... Episode 1: Introduction to Databases, Episode 2: The Relational Model, Episode 3: SQL, Episode 4: Data Modeling and the ER Model, Episode 5: Database Design, Episode 6: Database Administration, Episode 7: Database Indexes, Episode 8: Big Data, Data Warehouses, and Business Intelligence Systems
Neural Networks and Deep Learning by Michael Nielsen. A great introduction to Neural Networks with nice visualizations
Command Line for Data Science. Perhaps a niche skill among Data Scientist, impress your co-workers with your Terminal talent! A very simple, efficient, automatable way to retrieve and scrub data.
Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville. I don't think this book is very good, but it's written by some big shots so I marked it down here.
Genetic Programming by John Koza. A very unique approach to machine learning. Koza introduces an evolutionary framework for an algorithm to learn and mutate.
Driven Data. Hosts competitions that are directed towards good. Examples include predicting disease spread, efficient education spending, and predicting extreme poverty.
SQL Zoo. This is the best resource I know for learning SQL.
HackerRank. Challenges that span from basic to advanced on topics such as Statistics, Python, SQL, Java, and much more!
Top Coder. Again similar to LeetCode and HackRank, but this is more project focused. Also there are opportunities to make money.
Holoviews tutorial. Highly advanced visualization tutorial that introduces interactivee graphs, 3D plots, and more. Learn about Python's visualization libraries Holoviews and Bokeh.
Tableau. For some reason this can get a bad reputation for not being as serious as Python. I admit, when I first used Tableau, it reminded me of Scratch. However Tableau is able to produce stunning visualizations in a fraction of the time as other languages. Here are some resources Tableau-specific:
Colah's blog. Incredibily insightful, well illustrated essays on Neural Networks from one of Google Brain's employees.
I'm really sorry for doing this, blogs listing important papers is nothing new.
Stay Determined! Although academic papers may appear dense and intimidating at first, and stay that way. However you get used to that aspect over time. You got this!
Convolutional Neural Networks (CNNs).
Recursive Neural Networks (RNNs).
Generative Adversarial Networks (GANs)
Deep Learning (fun)
I apoligize if any links break over time; if you notice one that is, please email me at 'email@example.com' and I will fix it promptly.
If you start to become overflooded with papers, check out Mendeley to keep you organized.
A / B test Sample Size calculater (link) - Performs most of the mathematical work you'll need for A / B tests.
Kaggle Kernels (link) - Python and R notebooks running off of the cloud. It isn't industrial-grade, but if you're just starting out, it's a good supplement to your laptop. Kaggle kernels move the computation away from your local device for FREE. And the kernels specs are: 4 CPUs, 16GB RAM, 1GB disk space, 60min execution time.
If you have made it this far, you are extremely powerful and should consider using your newfound skills for good. I wish you the best on your journey as Data Science is as difficult as it is rewarding.