Data Science Tutorials

Learn how Python can be used more effectively than Excel, with the Pandas package.

In the tutorial, learn everything about the Python datetime module. Find a step-by-step guide for strings to datetime conversion, along with code samples and common errors.

Visualize the training parameters, metrics, hyperparameters or any statistics of your neural network with TensorBoard!

This tutorial will demonstrate how you can install Anaconda, a powerful package manager, on your Mac.

Learn how to create custom export templates for your Jupyter Notebooks using Jinja2.

Discover Homebrew for data science: learn how you can use this package manager to install, update, and remove technologies such as Apache Spark and Graphviz.

This article covers how and when to use Random Forest classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover how to use the confusion matrix and feature importances.

Importing data is the first step in any data science project. Learn why today's data scientists prefer the pandas read_csv() function to do this.

Learn what Generative Adversarial Networks are without going into the details of the math and code a simple GAN that can create digits!

Learn why you would transform your data from a long to a wide format and vice versa and explore how to do this in R with melt() and dcast()!

Explore graph-structured multi-task regression with the GFLASSO R package with this tutorial!

In this tutorial, you'll learn about absolute and weighted word frequency in text mining and how to calculate it with defaultdict and pandas DataFrames.