I'm the founder of Dataquest. I started building Dataquest after going through the difficult process of teaching myself data science (read more about my experience here). I know how hard it can be to go from no knowledge of programming to being able to get a data science job. When learning, I faced many roadblocks, including knowing what to learn, and staying motivated. We've designed Dataquest to help you overcome these challenges, and go from zero to job-ready in one place.
We've had students get jobs at SpaceX, Microsoft, Fitbit, 3M and more]
While I'm not super-familiar with DataCamp, I do keep an eye on what they do, and can outline some key differences in our approaches to teaching.
We help you build your knowledge up smoothly
DataCamp (and many others) focus on releasing completely separate courses written by different people. This makes it hard to build up your knowledge, as one course may not teach a key concept that the another course requires. It's also tough to adjust to multiple teaching styles, and one teacher may have much worse explanations than the previous one.
Our approach is different. We've built learning paths around three career outcomes: Data Scientist, Data Analyst and Data Engineer. Each learning path is designed to build up your knowledge smoothly until you're ready for a job in the data field. Each lesson in a path builds on the previous lesson. We never introduce concepts you haven't learned yet, and we always ensure that our explanations and teaching styles are consistent. This ensures that you're never lost, and never struggle to know what to learn next.
We focus on the skills, not the syntax
We recognize that most of our Dataquest students come to us to get data-related jobs, so we have constructed our learning paths with that in mind. Our aim is to give you a deep understanding of the concept you're learning, so you can apply it later in an interview or work setting. If you only understand what commands to type, you'll never be able to transfer your knowledge from one setting to another.
For example, when I was learning programming, I used Codecademy for a month, and thought I knew Python well. But I struggled to use the knowledge in the real world, because Codecademy was just teaching me what to type to pass the exercise, not the deeper concepts.
We focus on all of the important skills you'll need as a working data scientist, including web scraping, SQL, working with databases, machine learning, statistics, and the command line. We put a lot of emphasis on fundamental but unhyped skills like the command line and SQL because they're critical to your day to day work as a data scientist, and can often be the difference between succeeding and failing in a data science role.
We teach you how to learn
Some of our students who have previously used DataCamp tell us they feel like DataCamp makes it too easy, and as a result they don't really learn the material.
We recognize that real learning happens when you are challenged. As you progress through our learning paths, you'll notice that we start to be less explicit about how to solve problems, and start pointing you towards using the documentation more and more.
This is a lot like the steps involved in learning to ride a bicycle when you're a kid. We aim to give you the support you need throughout, but we gradually give you more and more autonomy, until you're able to do real-world data science work on your own. We're able to do this because of the thought and effort we put into designing our paths to smoothly build your skills and capabilities.
We help you build your portfolio with projects
Each of our courses ends with one or more guided projects. These projects often use Jupyter notebook and other technologies used by working data scientist.
Our guided projects serve two main purposes. The first is to help you synthesize what you have learned and get used to a real-life data science workflow. The second is to form a foundation for a portfolio, which will help you demonstrate your skills to employers so you can get get a job.
Projects were critical for me to understand the material when I was learning, and they're the only way to get used to real-world data science workflows. Working data scientists spend their time building projects that look a lot like the ones you'll build with Dataquest. If you aren't building projects, you aren't getting used to data science work, and you aren't setting yourself up for career success.
We care about helping you on your journey
We understand that learning is difficult, and it's frustrating to get stuck. Many of us have struggled to learn data science or programming on our own.
As a subscriber, you'll get access to support where you can get help with any learning questions you have, when you need it. At Dataquest, we often go the extra mile to really help you understand a concept and succeed. Many of our students cite our support as critical to helping them become job-ready.
You can learn with others in our community
As a subscriber, you'll get access to our learning community where you can get help, share your work and collaborate with others. Our community consists of others like you who want to advance their careers in data science.
Communication and collaboration are two of the most important skills for data scientists. Learning with others helps you develop these skills while staying motivated.
The Dataquest approach favors project based learning, and focuses on helping you achieve your goals. We know what it's like to struggle to learn data science skills on your own, and we're here to help you learn.
You might like to check out the reviews our students have left on some rating sites:
If you think this sounds like it might be for you, you can sign up for free. We have 23 free missions (including our entire first course) which will help you make a decision on whether our teaching style is a good fit for you.