15 Ways Machine Learning Will Impact Your Everyday Life

Artificial intelligence (AI) and machine learning is now considered to be one of the biggest innovations since the microchip. AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. Neural networks (imitating the process of real neurons in the brain) are paving the way toward breakthroughs in machine learning, called...

Aug 2018

Python for Data Science (Ultimate Quickstart Guide)

This quickstart tutorial will get you set up and coding in Python for data science. If you want to learn one of the most in-demand programming languages in the world… you’re in the right place. By the end of this guide, you’ll have a strong foundation and be able to follow along other tutorials on this site, even if you’ve never programmed before....

May 2018

Python Data Wrangling Tutorial: Cryptocurrency Edition

Bitcoin and cryptocurrency have been all the rage… but as data scientists, we’re empiricists, right? We don’t want to just take others’ word for it… we want to look at the data firsthand! In this tutorial, we’ll introduce common and powerful techniques for data wrangling in Python. Broadly speaking, data wrangling is the process of reshaping, aggregating,...

Jan 2018

How to Become a Data Scientist, The Self-Starter Way

So you want to become a data scientist… that’s fantastic! But as you may already know (or may soon find out), it’s not quite that simple. In fact, you’ll most likely face some challenges that are unique to data science… Challenge #1: WTF is a “data scientist?” You could ask 10 data scientists and get 15 descriptions of what they do. The term “data...

Jan 2018

Python Cheat Sheet for Data Science

Pandas, Numpy, and Scikit-Learn are among the most popular libraries for data science and analysis with Python. Numpy is used for lower level scientific computation. Pandas is built on top of Numpy and designed for practical data analysis in Python. Scikit-Learn comes with many machine learning models that you can use out of the box. In this cheat...

Nov 2017

How Data Science Can Nearly Double Your Income, with Zachary Washam

In data science, knowledge is not power… applied knowledge is power. Businesses want to know if you can deliver results that impact the bottom line. But how exactly can you do so? Are hard skills enough? What practical steps can you take to help your organization thrive? We explore these questions and much more in this interview with Zachary Washam,...

Nov 2017

Open Source vs Commercial Machine Learning Software

At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R, SAS, MATLAB… the list goes on. But first, you’ll actually need to make another choice: Should I go with open source or commercial software? Open source code is “freely available...

Nov 2017

How to Write the Perfect Data Scientist Resume

As data scientists, we often obsess over numbers and conversion rates… and that’s a good thing! A job search is just a numbers game with plenty of conversion rates. In fact, you can optimize the conversion rate between each step of the application process: Applications ⇒ Interviews ⇒ Job Offers Today, we’ll look at how you can improve your rate of Applications...

Oct 2017

Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit? And that this mistake can single-handedly ruin your machine learning model? No, that’s not an exaggeration. We’re talking about one of the trickiest obstacles in applied machine learning: overfitting. But don’t worry: In this guide, we’ll walk you...

Sep 2017

Datasets for Data Science and Machine Learning

These days, we have the opposite problem we had 5-10 years ago… Back then, it was actually difficult to find datasets for data science and machine learning projects. Since then, we’ve been flooded with lists and lists of datasets. Today, the problem is not finding datasets, but rather sifting through them to keep the relevant ones. Well, we’ve done...

Aug 2017