Data Science Tutorials

In this tutorial, you'll learn all about Python Strings: slicing and striding, manipulating and formatting them with the Formatter class, f-strings, templates and more!

Get Google Trends data of keywords such as 'diet' and 'gym' and see how they vary over time while learning about trends and seasonality in time series data.

Learn how feature engineering can help you to up your game when building machine learning models in Kaggle: create new columns, transform variables and more!

Which shell commands do data scientists use nearly every day? Discover and learn how to use them in this tutorial!

Learn how to build your first machine learning model, a decision tree classifier, with the Python scikit-learn package, submit it to Kaggle and see how it performs!

In this Kaggle tutorial, you'll learn how to approach and build supervised learning models with the help of exploratory data analysis (EDA) on the Titanic data.

Five useful tips that you can use to effectively improve your R code, from using seq() to create sequences to ditching which() and much more!

Learn all about Python dictionary comprehension. Learn how you can use it to create dictionaries, to replace (nested) for loops or lambda functions with map(), filter(), reduce(), and more!

Get introduced to Python data structures: learn more about data types and primitive as well as non-primitive data structures, such as strings, lists, stacks, etc.

Discover why you should use Amazon Web Services Elastic Compute Cloud (EC2) and how you can set up a basic data science environment on a Windows instance.

In this tutorial, you’ll learn how to implement Convolutional Neural Networks (CNNs) in Python with Keras, and how to overcome overfitting with dropout.

Discover the R formula and how you can use it in modeling- and graphical functions of well-known packages such as stats, and ggplot2.