Data Science Tutorials

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

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.

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.