Data Science Tutorials

Build your recommendation engine with the help of Python, from basic models to content-based and collaborative filtering recommender systems.

This article covers different ways to import text files into Python using Python, NumPy, and Python’s built-in methods. It also covers how to convert these into lists.

Learn how to read and import Excel files in Python, write data to these spreadsheets, and find the best packages to do this.

Learn all about R's matrix, naming rows and columns, accessing elements also with computation like addition, subtraction, multiplication, and division.

Learn what variable scopes are all about and get familiar with the 'LEGB' rule. You will also deal with scenarios where you'll get to see the global and nonlocal keywords in action.

Learn what is Python IDLE (Integrated Development and Learning Environment) is, how you can install it, and leverage its various features for writing your Python programs.

Learn about data reshaping in R, different functions like rbind(), cbind(), along with Melt(), Dcast(), and finally about the transpose function.

Learn about type checking, different types of the type system in various languages along with duck typing, and type hinting.

Learn the basic concepts of threading and how you can use it with the help of Python.

Learn how to create data visualizations in Python using Altair and a Movies Dataset.

Learn about web scraping with python and how to scrape Amazon, with the help of the Beautiful Soup library.

Kevin Systrom published a really interesting article on estimating $R_t$, the measure known as effective reproduction number. We will use the same approach outlined by Kevin in his article to estimate COVID-19's Rt in real-time.