Advanced Jupyter Notebooks: A Tutorial
Lying at the heart of modern data science and analysis, Jupyte project lifecycle. Whether you’re rapidly prototyping ideas, demonstrating your work, or producing fully fledged reports, notebooks can provide an efficient edge over IDEs or traditional desktop applications. Following on from “Jupyter Notebook for Beginners: A Tutorial“, this guide will take you on a journey […]
Jupyter Notebook for Beginners: A Tutorial
The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. A notebook integrates code and its output into a single document that combines visualisations, narrative text, mathematical equations, and other rich media. The intuitive workflow promotes iterative and rapid development, making notebooks an increasingly popular choice at the heart […]
How to Set Up a Free Data Science Environment on Google Cloud
Whether you’re running out of memory on your local machine or simply want your code to run faster on a more powerful machine, there are many benefits to doing data science on a cloud server. A cloud server is really just a computer, like the one you’re using now, that’s located elsewhere. In this post, […]
Getting Started with Kaggle: House Prices Competition
Founded in 2010, Kaggle is a Data Science platform where users can share, collaborate, and compete. One key feature of Kaggle is “Competitions”, which offers users the ability to practice on real-world data and to test their skills with, and against, an international community. This guide will teach you how to approach and enter a […]
28 Jupyter Notebook tips, tricks, and shortcuts
This post is based on a post that originally appeared on Alex Rogozhnikov’s blog, ‘Brilliantly Wrong’. We have expanded the post and will continue to do so over time — if you have a suggestion please let us know. Thanks to Alex for graciously letting us republish his work here. Jupyter Notebook Jupyter notebook, formerly […]
DigitalOcean and Docker for Data Science
Creating a cloud-based data science environment for faster analysis There are times when working on data science problems with your local machine just doesn’t cut it anymore. Maybe your computer is old, and can’t work with larger datasets. Or maybe you want to be able to access your work from anywhere, and collaborate with others. […]
Tutorial: Running a Dockerized Jupyter Server for Data Science
Editor’s note: This post was updated in May 2018. At Dataquest, we provide an easy to use environment to start learning data science. This environment comes preconfigured with the latest version of Python, well known data science libraries, and a runnable code editor. It allows brand new data scientists, and experienced ones, to start running […]
Tutorial: Installing and Integrating PySpark with Jupyter Notebook
At Dataquest, we’ve released an interactive course on Spark, with a focus on PySpark. We explore the fundamentals of Map-Reduce and how to utilize PySpark to clean, transform, and munge data. In this post, we’ll dive into how to install PySpark locally on your own computer and how to integrate it into the Jupyter Notebbok […]
