Dataquest’s Philosophy: Building the Perfect Data Science Learning Tool
At the time I started Dataquest, if you wanted to become a data scientist, you were confronted with dozens of courses on sites like EdX or Coursera, with no easy path to getting a job. I saw many promising students give up on learning data science because they got stuck in a loop of taking the same courses over and over.
There were two main barriers to learning data science that I was trying to solve with Dataquest: the challenge of getting from theory to application, and the challenge of knowing what to learn next.
I strongly believe that everyone deserves a chance to do work that they find interesting, and Dataquest was a way to put that belief into action and help others get a toehold in a difficult field.
Over the years, we’ve worked hard to make it simple to learn all of the skills you need for a data science role in one place. From basic Python and R to SQL to Machine Learning, Dataquest teaches you the right skills, and helps you build a portfolio of projects along the way.
As we’ve built the site, we’ve learned quite a few lessons on how to most effectively help our students. We’ve been gradually increasing the scope of our initial vision. In this post, I want to outline what we’re focused on now, and where we’re headed. Along the way, I hope to make the case for why Dataquest is the place you should be learning data science.
Dataquest’s Philsophy, Based on Years of Observations
It’s a common refrain that learning is its own reward. Massively Open Online Course (MOOC) sites like the aforementioned edX and Coursera were created with this wisdom in mind.
What we’ve found instead is that our students are learning data science because they enjoy it and because they want better jobs. This observation has pushed us to become more career-focused. The most common thing students want is a better path to data science careers, and we feel that it’s the highest leverage thing we can work on.
As we help people get ready for new careers, we’ve made four key observations:
- Focus is critical to retaining knowledge, especially when you have limited time.
- Motivation is the most important determinant of whether you’ll get a job.
- It’s easy to get “stuck” and frustrated — timely help is key.
- There isn’t a lot of good career advice and interview preparation help.
Let’s dive into each of these observations in more depth, and see how they’ve affected our thinking.
When you’re learning data science, it’s tempting to get lost in a sea of tools. You’re told that you have to learn R, Python, Spark, and Tensorflow. If you don’t, you’re not a “real” data scientist. This is off-putting, but more to the point, it isn’t actually true.
What we’ve found instead is that the students who end up getting jobs focus on concepts over tools. If you learn how to implement a random forest from scratch, and know the tradeoffs involved in training it, it doesn’t matter if you use Python, Scala, or R to make predictions.
Concepts generalize between tools; if you learn a concept well, you can use any tool to implement it. If you can fit a decision tree model in R, you’ll have some job prospects, but if you deeply understand the model and how it works, you’ll have an order of magnitude more.
Focusing on a few concepts at a time and mastering them before moving on is key to retaining knowledge. We’ve kept Dataquest extremely focused, so knowledge sinks in. Our interactive platform constantly challenges students to apply what they’re learning by writing their own code before moving on to the next step, so that they can take things one step at a time and get immediate feedback from our answer-checking system when they haven’t understood something.
We have a linear curriculum that takes you from no programming knowledge all the way to advanced machine learning. Because we develop the entire curriculum, we’re able to teach things in logical order, and make sure you’re never lost. Our consistent style and focus on concepts mean that you can stay focused on learning one concept at a time.
For example, here’s the first few steps of our Data Analyst in Python path. Each course builds on the skills learned in the previous one.
It’s often taught in school that it’s a teacher’s job to teach, and your job to be motivated. But if you’re unmotivated, even a teacher who knows the material well won’t be effective.
We’ve found that motivation is the single biggest difference between students who get jobs and those who don’t. It’s not enough to just “check the boxes” and get certificates. You have to build projects to demonstrate your skills and build a portfolio. Building a good one requires real interest and motivation.
In order to be motivated build effective projects, you have to genuinely enjoy working with data. As I wrote in a blog post on how to learn data science, a prerequisite for learning data science is finding problems that interest and motivate you.
At Dataquest, our philosophy is that it’s our job to be motivating, and we’ve oriented the site around it.
We’ve designed our curriculum to interleave dozens of interesting data sets, including data on CIA interventions and NBA player stats. When you’re ready for them, we include dozens of interesting projects exploring topics like how to win Jeopardy and stock price forecasting. By focusing on engaging and motivating you, we help you get further in your journey to get a data science job.
For example, here’s a screen from a guided project where you use SQL to analyze some discrepancies in scores on movie review sites:
When it comes to working on more open-ended projects — which is something all aspiring data analysts and data scientists need to do — we’ve found that students need help getting “unstuck”.
Being stuck can range from not knowing how to install a package to having trouble conceptualizing the structure of the data. Students often don’t need major help — just a small nudge in the right direction or a confidence boost can be invaluable.
We’ve realized that as these small moments of frustration when you’re stuck pile up, they decrease your motivation, and make it more likely that you won’t reach your goals. We’ve designed systems that ensure you can get help either from a mentor or peers to avoid this frustration, including our data science student community.
We’ve noticed that many of our students have career questions, which range from wondering what skills they should learn to be most marketable to employers, to what questions might be asked in an interview, to what their portfolio should look like.
To help answer these questions, we spoke to dozens of data scientists, recruiters, and hiring managers and produced a data science career guide. For Premium subscribers, we also offer career counseling to help with career questions on a more direct, personal level. And of course, students can also solicit career advice from peers and mentors in the student community.
The Perfect Data Science Learning Tool
Based on the above observations, Dataquest’s philosophy is that the ideal data science learning tool would include the following elements:
- Gives you a roadmap for learning data science.
- Allows you to practice skills by coding in the browser.
- Teaches advanced concepts in an applied fashion.
- Helps you build your portfolio with projects.
- Gives you support along the way with mentor and community help.
- Guides you on career choices and helps you find potential employers.
We think we’re addressing all of these six elements, although there are definitely plenty of improvements that we can make. We’re working to improve Dataquest all the time, so let’s take a look at each of these six areas, what we offer, and what we’re working to improve.
1. Data science roadmap
A roadmap for data science lets you stay focused and on track, without having to figure out which course to take next. We currently offer four learning paths that aim to take students from zero experience to job ready as data analysts (in Python or R), as data scientists (in Python), and as data engineers (in Python).
But there’s lots more we can do. In the immediate future, we’re planning a re-launch that with make our Data Engineering courses more beginner-friendly, and we’re also constantly working to expand our R offerings with the ultimate goal of building out a Data Scientist in R path, too.
2. In-browser coding
It’s amazing how long installing packages like pandas or tools like Spark can take when you’re a beginner. Dataquest lets you get your feet wet in the browser, writing real code without having to set anything up locally. And we have automated answer-checking that makes working on our site better for learning than working on your local machine.
Eventually, though, everybody needs to get comfortable working locally, so we also help you get everything setup on your own computer to work on your own.
In the past year, we’ve rolled out some exciting changes, like overhauled Command Line courses with a better in-browser interface, and code-running improvements that make our browser-based system more reliable and faster. We’re always working to improve and optimize our platform, so work to improve our interactive, in-browser coding will continue for the foreseeable future.
3. Applied concepts
Our missions teach you data science concepts like decision trees by having you work through real problems using interesting data sets. For example, you might work through data on airline accidents, or educational achievement worldwide. Using our in-browser coding system, you’ll learn piece-by-piece, applying each concept in code that manipulates real data sets as you go.
Once you’ve learned the skills, you’ll be able to apply them In larger guided projects that still provide some structure while challenging you to synthesize the new skills you’ve learned.
This loop of learning followed by immediate application helps you quickly develop and solidify your skills. We think that our quick loop of concept learning and application interspersed with larger guided projects is the most effective way to teach data science, and our students agree.
(Check out our student stories and see how many of them mention that they’ve tried multiple platforms, but that Dataquest was the one that ‘forced them to think’ and apply what they were learning).
There’s always room for improvement, though, and we’re constantly iterating and tweaking our courses based on student data. If we see that students are getting stuck on a particular screen, for example, we can and do make adjustments. That’s something we’ll continue to do, and do more of, in the years ahead.
We help you build a portfolio of projects. Not only does this help you practice and learn concepts, it also helps you get job interviews!
Hiring managers are increasingly looking at portfolios when making decisions on who to interview. Even interviews have moved more towards projects as a means of assessment — you might get a take-home or in person project as part of your interview.
We know that Dataquest’s guided projects can help people get jobs — here’s one example of that. But we want to offer more projects with a broader variety of data sets to give students more choices as they work through our lessons.
5. Support along the way
Right now, you can get help from other students learning on Dataquest in our student community, and our data scientists often post there as well. This help is critical in keeping you focused and motivated. This year, we’ll be working hard to expand that community and introduce tools to make it even more effective for finding the help you need fast whenever you get stuck.
6. Career help
In the past year, we’ve released a data science career guide that’s more than 30,000 words long, based on dozens of interviews with experts. It covers every stage of the job application process.
We also offer career counseling for Dataquest Premium subscribers so that they can get help with more personal career questions.
In the year ahead, we plan to introduce more career help by doing things like holding community webinars with career experts who can give advice and answer questions.
Dataquest’s Philosophy: Looking Ahead
As you’ve read, you can expect a lot of improvements for Dataquest. But progress doesn’t happen all at once, it happens regularly, as we constantly tweak the Dataquest experience.
We’re constantly iterating and improving on the platform experience, and we’re also constantly writing and releasing new courses to expand our course offerings. If you choose to subscribe, you’ll see our course directory expand as you’re learning, so there’s always something new to add to your skill set.