Learn about machine learning in Python and build your very first ML model from scratch to predict Airbnb prices using k-nearest neighbors.
Learn data cleaning for a machine learning project by cleaning and preparing loan data from LendingClub for a predictive analytics project.
Pandas plotting methods provide an easy way to plot pandas objects. Often though, you’d like to add axis labels, which involves understanding the intricacies of Matplotlib syntax. Thankfully, there’s a way to do this entirely using pandas. Let’s start by importing the required libraries: import pandas as pd import numpy as np import matplotlib.pyplot as […]
Kaggle is a site where people create algorithms and compete against machine learning practitioners around the world. Your algorithm wins the competition if it’s the most accurate on a particular data set. Kaggle is a fun way to practice your machine learning skills. This tutorial is based on part of our free, four-part course: Kaggle […]
Our version 1.29 release is here and includes lots of new features to help enhance your learning experience. Over the past few months we’ve been tirelessly talking to students like you to learn how we can improve the mission interface. With this release, we are unveiling the results of this hard work. Other big changes […]
The printable version of this cheat sheet The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out! This cheat sheet is the companion […]
Many aspiring data scientists focus on doing Kaggle competitions as a way to build their portfolios. Kaggle is an excellent way to practice, but it should only be one of many avenues you use to work on data science projects. This is because Kaggle competitions only focus on a narrow part of data science work. […]
Our new mission design has arrived! Over the past few months we’ve been tirelessly talking to students like you to learn how we can improve the mission interface. Today we are unveiling the results of this hard work. Since a lot has changed, we wanted to take a moment to describe the big changes and […]
Python and pandas work together to handle huge data sets with ease. Learn how to harness their power in this in-depth tutorial.
Jennifer Thomas works as a Chief of Staff for one of the USA’s ‘big four’ banks. She supports the team that develops all the software used by the bankers. “I do anything that needs doing so our coders can sit and code as much as possible. I do financials, budgeting, reporting, visa work – a little […]
Prerit Anwekar had decided he wanted to be a data scientist. He was attending Indiana University doing his masters. “I realized that to do machine learning, I needed to focus on Python, but I was only being taught R.” After trying to learn Python from a book, he was discouraged. “Learning using a book is really taxing. It […]
Wes Brooks had always understood that data was key to success in business. “Early in my career, I built other businesses and my own with a data-driven mindset.” This led Wes to a role at Cornett, an advertising agency where he worked on marketing campaigns for Fortune 500 companies. “They hired me to apply that […]
It’s common when first learning Python for Data Science to have trouble remembering all the syntax that you need. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out! This cheat sheet is the […]
One of the biggest sources of confusion and misinformation for people wanting to learn Python is which version they should learn. Should I learn Python 2.x or Python 3.x? Indeed, this is one of the questions we are asked most often at Dataquest, where we teach Python as part of our Data Science curriculum. This […]
While working as a geophysicist for an oil services company, Harry Robinson found himself interested in data. “My job involved lots of data, but it was always at arms length. We were applying algorithms, but I never got to see them. “I wanted to know what was happening and why, so I could interpret the results.” He decided […]
Our version 1.19 release includes new features designed to improve your learning experience. The first thing you may notice is a new look. We’ve made some design tweaks, including a new mission-text font which we think you’ll agree makes everything easier to read. Other big changes in v1.19 include: Multiscreen, so you can work more […]
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 […]
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 […]
When working with data, a key part of your workflow is finding and importing data sets. Being able to quickly locate data, understand it and combine it with other sources can be difficult. One tool to help with this is data.world, where you can search for, copy, analyze, and download data sets. In addition, you […]