This page goes through a few other resources for learning Python and R, as well as data science, system automation, and more. This section is currently a work in progress, but I will be posting links to courses, book reviews, etc. about Python, R, and all things open source.
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Web Scraping and API Fundamentals with Python is an online course I co-created with 365 Data Science. This extensive course covers a range of topics from how to work with APIs, how to use BeautifulSoup to scrape webpages, how to download files automatically with Python, how to scrape tables from webpages, and much more! It also covers the newer Python package requests-html, which includes how to scrape data from JavaScript-rendered webpages.
Click here to see the course homepage!
365 Data Science is a great way to get introduced into the main software tools a data scientist needs to know – such as Python, R, and SQL. They also have courses from the foundations of data science – covering mathematics (e.g. linear algebra) and a breakdown of data science fields – through content on deep learning.
365 Data Science has both free and paid options. The free option gives access to 12 hours of videos. However, you can also sign up for a very reasonably priced option that gives access to their full set of courses and downloadable materials.
An additional benefit with this option is that 365 Data Science continues to put out new courses in their program. If you sign up, you’ll be able to access any of the new courses for no additional cost.
Click here to learn more about 365 Data Science
Udacity is a great learning resource. My favorite course with Udacity is their machine learning class offered by Georgia Tech professors. They offer a free version covering supervised, unsupervised, and reinforcement learning. It’s a great way to get start learning about machine learning concepts like decision trees, boosting, neural networks, clustering, and more.
Udacity offers both free individual courses (like the machine learning one above), as well as paid nanodegree programs. These are typically 9 – 12 months long and bundle together several classes in a particular focus area (hence “nano” degree. Some examples include deep learning, business analytics, and cloud computing.
For other resources on learning Python, R, and open source tools, check out these recommended books.