Data Science Tutorials

Learn about Arrays in R, including indexing with examples, along with the creation and addition of matrices and the apply() function.

Learn about R's Strings: its rules, concatenation, along with essential properties of the string, extracting and replacing a character string, and formatting a string.

Learn why there is a need for documenting code and best practices to do it. Further, learn to leverage the potential of the Pydoc module for documenting purposes.

In this tutorial, you'll learn all about R variables including how to define variables, remove variables, and much more.

This is a beginner guide that is designed to save yourself a headache and valuable time if you decide to install R yourself.

Learn about several useful functions for data structure manipulation, nested-lists, regular expressions, and working with times and dates in the R programming language.

Learn about relational operators for comparing R objects and logical operators for combining boolean TRUE and FALSE values. You'll also construct conditional statements.

Learn about data types and their importance in a programming language. More specifically, learn how to use various data types like vector, matrices, lists, and dataframes in the R programming language.

Wondering whether you should use Python or R for data analysis? You’ve come to the right place.

Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python!

In this tutorial, you'll learn about Support Vector Machines, one of the most popular and widely used supervised machine learning algorithms.

In this tutorial, you'll learn about the statistical concepts behind survival analysis and you'll implement a real-world application of these methods in R.