Data Science Tutorials

Learn about Object-Oriented Programming (OOP) along with R's objects, different classes like S3 and S4, along with its construction, creating its generic function with examples and many more.

Learn how to effectively use list comprehension in Python to create lists, to replace (nested) for loops and the map(), filter() and reduce() functions, ...!

Learn in detail about the ifelse() function, including syntax, along with finding whether a number is odd or even, and finally, with an example to see whether a student passed or failed their exam.

Learn about the factor function in R, along with an example, and it's structure, order levels, renaming of the levels, and finally, with the ordering of categorical values.

Learn to read and write Excel files in Python using openpyxl. This guide covers formulas, charts, cell formatting, and worksheet manipulation with hands-on exam

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.