Learning Data Science: The Dataquest Teaching Philosophy

At Dataquest, we’ve spent years refining our approach to teaching. I was originally motivated to start Dataquest because of my own experience learning data science, and the main goal of our courses is still to help you apply your data science skills in the real world. This could be completing a project at work, being able to do an analysis you weren’t able to before, or building a side project you’ve been meaning to get to.

In order to help you get there, we’ve spent years developing a set of principles that govern how we teach. For many of our students, these principles help lead to new careers in data science. However, I don’t believe in one-size-fits-all education. What may be a great approach for one person may not work at all for someone else.

In this post, I’m going to outline our teaching principles, so you can make an informed choice about whether or not Dataquest is the right fit for you. Because ultimately, what matters is whether or not learning something helps you meet your goals.

Focusing on students

First, we strongly believe that you are at the center of the learning experience. We tailor all of our courses to your goals, and help you learn in a way that lets you quickly accomplish those goals. We start by defining the outcomes that you’ll be able to accomplish after completing a series of courses — which we call a path.

We currently offer paths that help you become a data analyst, a data scientist, or a data engineer. In order to develop these paths, we determined which skills someone working in these fields needs to succeed, and then worked backward to develop data science courses that teach those skills.

Each course builds on the courses before it; our goal is to help you spend time learning, not puzzling out which course to take next, or what prerequisites you need to fill in a critical piece of knowledge that prevents you from understanding an explanation.

Our courses have a consistent style, so you never need to spend time getting acclimated to a new method of explanation. You can spend all of your mental energy on understanding concepts, not on parsing out how the explanation is being delivered.

We ensure that all of the courses we teach are relevant to your goals. We don’t want you to spend time learning concepts that, while interesting to us, aren’t going to be meaningful for you. We spend significant amounts of time beta testing our courses and getting your feedback to make sure they’re relevant before we launch them.

Helping you transition to real-world projects

Second, we focus on helping you transition from coding in the browser to working on real projects. This can be a very hard hurdle to jump, and I’ve seen a lot of people get stuck in “learning mode” where they keep cycling through courses without taking the (admittedly very scary) leap into building their own projects. We challenge you in our courses, but also give you the support you need.

To start, we focus on explaining the concepts, not just the syntax (what to type). We do this because unless you understand what a command is doing and why, you can’t successfully use it in the real world. For example, you can train a neural network in a few lines of code these days. However, unless you know how to interpret the predictions and tune the neural network, you won’t be able to apply it in the real world.

We build context for the concepts we teach by showing you how they would work on a real data set. For example, we teach the basics of machine learning by asking you to analyze data from AirBnB rentals. We don’t want you to take our word for why something is relevant. Instead, we show you why it’s relevant, so you know how and when to use the concept in the real world.

We also ask you to complete several coding exercises in each of our lessons. This helps you to build your capability, and get more comfortable with coding before making the leap into a real-world project. These exercises aren’t easy — you can’t just blindly follow the instructions and succeed — but they give you feedback on your code, so you’re able to continuously improve.

We help you build small projects at the end of every data science course to tie the concepts you learned together, and help you get comfortable with coding on your own. These projects offer guidance, so you aren’t completely on your own, but are instead able to gain confidence before diving into larger projects on your own.

Creating real motivation to learn data science

Third, we understand how important motivation is in learning. Learning data science isn’t just about memorizing facts or solving coding exercises. It’s about the wonder you feel when you finally learn a new concept. It’s about the joy you can get from completing a hard project. If you’re in a learning groove, it should feel joyous; like nothing can get in your way.

We make an effort to include a compelling storyline and data set in every lesson we teach. This means that you’re not just learning a laundry list of commands. You’re instead creating a story with the data and building an interesting analysis as you learn. This makes learning more engaging, and it makes you more likely to succeed than if you were just memorizing a lot of syntax.

We help you build projects that are exciting to you. As you go through our courses, you’ll build more and more complex projects with less and less guidance. This helps create a sense of gaining capability and advancing over time.

We constantly think about motivation as we author courses, and refine our courses over time to remove obstacles. We keep our exercises challenging, but we constantly tweak our explanations to make them more motivating and accessible to you.

Building a community

Fourth, we understand that learning isn’t just an individual exercise. Having support and community can make all of the difference in your learning journey. Peers can help you overcome struggles and gain context on the industry as a whole. Instructors can help you navigate career obstacles and quickly identify and resolve misconceptions. Without a strong support network, it’s easy for small hurdles to appear insurmountable.

We have an internal support team that answers technical questions and helps you get unstuck as you learn. This can be invaluable early on when there are so many potential ways to get stuck and give up on coding. We hate seeing people who have the ability and drive give up because they didn’t get the support they needed.

We also offer office hours where you can chat with career counselors or data scientists. In these office hours, you receive support on everything from navigating the hiring process to debugging your SQL commands. Sometimes all you need when you’re learning is a little extra motivation.

To summarize, the Dataquest teaching principles are:

  • The student is at the center of the data science learning experience.
  • Build real-world skills and capability through coding exercises and projects.
  • Teach concepts, not just what to type.
  • Have challenging exercises, but also provide help and support to get through them.
  • Build motivation through using interesting data sets.
  • Help students engage with us and with a community to increase motivation.

Together, these principles lead to a learning approach that helps students level up their careers and achieve their dreams. If you feel like our principles resonate with you, try Dataquest out! You can get a feel for our platform and style with the 60 free lessons we offer.

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