Data Science Tutorials

Learn about the various options you have to setup a data science environment with Python, R, Git, and Unix Shell on your local computer.

In this tutorial, you will learn and understand how to read jpeg format fingerprint images, reconstructing them using convolutional autoencoder.

In this tutorial, you will build four models using Latent Dirichlet Allocation (LDA) and K-Means clustering machine learning algorithms.

A comprehensive guide to building, visualizing, and interpreting decision tree models with R.

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

Learn all about the Python datetime module in this step-by-step guide, which covers string-to-datetime conversion, 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.