Welcome to Part 6 of our Data Science Primer. In this guide, we will take you step-by-step through the model training process. Since we’ve already done the hard part, actually fitting (a.k.a. training) our model will be fairly straightforward. There are a few key techniques that we’ll discuss, and these have become widely-accepted best practices in...

Welcome to Part 5 of our Data Science Primer. Choosing the right ML algorithm for your task can be overwhelming. There are dozens of options, each with their own advantages and disadvantages. However, rather than bombarding you with all options, we’re going to jump straight to best practices. Specifically, we’ll introduce two powerful mechanisms...

Welcome to Part 4 of our Data Science Primer. In this guide, we’ll see how we can perform feature engineering to help out our algorithms and improve model performance. Remember, out of all the core steps in applied machine learning, data scientists usually spend the most time on feature engineering. What is Feature Engineering? ...

Welcome to Part 1 of our Data Science Primer. This bird’s eye view of the machine learning workflow will give you an end-to-end blueprint for data science and applied ML. You’ll learn the “ELI5” intuition behind machine learning, key terminology, and the ingredients to an effective ML model. You may have already seen some of the tutorials on...

Welcome to Part 2 of our Data Science Primer. Exploratory analysis is essential for effective data science because it helps you avoid wild goose chases and dead ends. This step should not be confused with data visualization or summary statistics. Those are merely tools… means to an end. Proper exploratory analysis is about answering questions....

Welcome to Part 3 of our Data Science Primer. In this guide, we’ll teach you how to get your dataset into tip-top shape through data cleaning. Data cleaning is crucial, because garbage in gets you garbage out, no matter how fancy your ML algorithm is. The steps and techniques for data cleaning will vary from dataset to dataset. As a result, it’s...