Data Science Tutorials

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

Discover the power of regular expressions with this tutorial. You will work with the re library, deal with pattern matching, learn about greedy and non-greedy matching, and much more!

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 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.

In this tutorial, you'll learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with the TensorFlow framework.

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.