How to become a data scientist

Data science is one of the most buzzed about fields right now, and data scientists are in extreme demand. And with good reason — data scientists are doing everything from creating self-driving cars to automatically captioning images. Given all the interesting applications, it makes sense that data science is a very sought-after career. Data science […]

NumPy Cheat Sheet — Python for Data Science

NumPy is the library that gives Python its ability to work with data at speed. Originally, launched in 1995 as ‘Numeric,’ NumPy is the foundation on which many important Python data science libraries are built, including Pandas, SciPy and scikit-learn. It’s common when first learning NumPy to have trouble remembering all the functions and methods […]

Kyle: “Dataquest helped me get into the tech industry”

For the first four years of his career, Kyle Stewart worked as a product manager in industrial automation. “I was working for a fortune 500 company. I managed products that helped industrial processes, like at an oil refinery.” He wanted to move into the more dynamic tech industry. “In industrial product management it’s difficult to make […]

Building An Analytics Data Pipeline In Python

If you’ve ever wanted to learn python online with streaming data, or data that changes quickly, you may be familiar with the concept of a data pipeline. Data pipelines allow you transform data from one representation to another through a series of steps. Data pipelines are a key part of data engineering, which we teach […]

What’s New in v1.14: Data Engineering Path & Performance Improvements!

Our latest Dataquest release has over 20 new features, including many major performance improvements and the launch of our much-anticipated data engineering path. New Path: Data Engineering The first course in our Data Engineering Path is here! Data Engineering is a broad field which includes: Working with Big Data Architecting distributed systems Creating reliable pipelines […]

Pandas Cheat Sheet — Python for Data Science

Pandas is arguably the most important Python package for data science. Not only does it give you lots of methods and functions that make working with data easier, but it has been optimized for speed which gives you a significant advantage compared with working with numeric data using Python’s built-in functions. It’s common when first […]

Dong: “Dataquest Helped Me Get a Job I Love”

After 4 years of working in postdoc positions, Dong Zhou was starting to re-evaluate academia. “It’s not a real job in terms of compensation and stability. I decided to quit postdoc and try working in industry.” Dong started to explore learning software development and data science. “I started off trying to learn with books, but I […]

How to get a data science job

You’ve done it. You just spent months learning how to analyze data and make predictions. You’re now able to go from raw data to well structured insights in a matter of hours. After all that effort, you feel like it’s time to take the next step, and get your first data science job. Unfortunately for […]

Whats New in v1.10: Answer diffs, Improved Q&A!

Along with our two new data visualization courses (Exploratory Data Visualization and Storytelling Through Data Visualization) our latest release includes two major features designed to make your life easier — enhanced Q&A and answer diffing. Introducing: Output & Variable Diffing When you’re learning to code, it can be frustrating to be stuck on an exercise […]

What’s New in Dataquest v1.9: Console, hotkeys, and more!

Whenever you send us feedback or an ideas for a feature, we read and catalogue your suggestions. We then use this to help planning features and improvements for Dataquest. Today we’re excited to launch two of our most-requested features: Hotkeys and a Python Console. Introducing the Python console Many of you have told us that […]

NumPy Tutorial: Data analysis with Python

Don’t miss our FREE NumPy cheat sheet at the bottom of this post NumPy is a commonly used Python data analysis package. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. NumPy was originally developed in the mid […]