DataCamp: The Plan
In a previous post, I gave some context on why we started DataCamp at a macro level. This post describes the key steps in building out DataCamp, i.e. the plan.
First of all, let’s fast forward to what we’re trying to achieve here in the long run: “DataCamp’s mission is to help individuals and companies at every step of their journey to become data fluent by building the smartest data science education platform out there.”.
To be more specific, the end goal is to:
- Enable individuals to learn data science in a fun and engaging way, stay up to speed with the latest and greatest in data science land, get mentorship and help in an efficient way during their learning process, have their skill level certified, and help them advance in their career.
- Empower companies to retrain their workforce effectively, understand the skills of their workforce, find and screen the right applicants for jobs.
At a high level, there’s 5 steps to reach that end goal:
- Step 1: Build the best learning platform for data science
- Step 2: Attract and retain students
- Step 3: Attract and retain the best instructors
- Step 4: Help companies to retrain their workforce
- Step 5: Expand our offering
In what follows, we take a deeper dive into the steps of the plan. All of these steps are required to reach the end goal. They are sequential in the sense that we want to get to a decent level for every step, before we move to the next step. Focus is crucial as explained in step 5. That said, it’s extremely important to understand that we need to keep executing on the first steps as we move to new ones.
Step 1: Build an engaging and effective platform to learn data science
We’ve built 3 different (sub)products that help students learn data science in an engaging and effective way:
- Interactive courses
- Practice mode
Interactive courses: Our focus on data science enables us to build interactive exercise interfaces that work really well for learning data science. Students solve challenges with R/Python/.. in the comfort of their browser. Furthermore, we’ve created custom technology to give personalized and instant feedback to students when they are solving challenges. We want our students to spend at least 70% of the time with their hands on the keyboard, actively solving challenges and receiving instant and personalized feedback. Not only does this lead to higher engagement (many of our courses have completion rates of over 50%), we believe it is a more effective way of learning for most students.
Practice mode: Our interactive courses are great to teach students new skills. The courses are not very helpful to build fluency and retention of knowledge. In contrast, for practice mode, we encourage students to practice regularly as spaced repetition leads to better knowledge retention. Furthermore, the somewhat simplified bite-sized challenges help students to build fluency. Practice mode is a fairly new part of our product: the goal is to become really good at understanding someone’s skill level and offer the right amount of practice on the right topics at the right time.
Furthermore, we’re investing heavily in a mobile app that allows our students to learn new topics and practice data fluency on the go.
Projects: The previous 2 elements of our learning product help to: (i) gain understanding of a topic, (ii) become fluent in applying it and (iii) have a high probability of retaining that knowledge. There’s still something missing though. Our students want to be able to make better decisions based on data and gain insight from data. They want to tackle real data science projects. In the next few weeks, we’ll launch a new part of the product: projects. Projects help students transition into the real world of data science. The goal here is to bring the learning experience as close as possible to what they will be doing in actual data science projects.
By the end of 2017, we expect these 3 core components of the learning experience to reach a decent level of maturity from a product perspective. In addition to further improving them, we want to invest in:
- Gamification: Learning should be fun. DataCamp’s current gamification system with experience points will get a complete revamp.
- Mobile: Many of our students are busy. A mobile learning experience allows them to learn on the go. We’d like to push the boundaries and we’re currently rethinking how learning for data science can happen on a mobile phone.
- Resource features: As our content library grows, we need better ways for students to navigate through the content library, and enable them to use the platform as a resource as well.
Step 2: Attract and retain students
When we built the first version of the platform, we faced the chicken or egg problem: If you don’t have students, no one is interested in building courses, if you don’t have courses you certainly won’t attract any students.
We did two things to overcome this challenge. First, we created a course for absolute beginners and made it available for free. As people started linking to that course, it started ranking in top position for various “learn R” related search terms. We intend to keep releasing free courses on topics that matter to beginners. Second, we built complements to existing courses on Coursera to improve their lab/exercise components, and set up a similar collaboration with Microsoft for their edX courses (on R, Python and SQL). These collaborations helped to get the word out on DataCamp initially.
DataCamp now has over 1.7 million students. We’re ready to welcome millions more in 2018. A few of our current initiatives to welcome new students:
- Teachers who want to use DataCamp in the classroom can do so entirely for free. Over 1,000 teachers are currently using DataCamp in the classroom (and tens of thousand of students). In addition to access to the entire course library, they get access to numerous tools that help them use DataCamp in the classroom.
- Complete beginners can enjoy our introductory courses on Python, R, SQL, Shell and Git for free.
- Everyone can enjoy the tutorials and data science blog posts.
- Every first chapter of every course is available for free, so student can try before they buy a subscription.
- We allow everyone to create DataCamp courses. Have a look at the list of these open courses. Some of them are translations of existing DataCamp courses in French, Spanish, Chinese, Italian, Vietnamese, German & Portugese.
- A mobile app with free introductory courses and practice sessions (download for Android or iPhone).
We love offering free services to our students. That said, we also want to build a large and sustainable business. This is the best way to ensure that we can keep innovating and providing value to our students. Our business model is simple: to get access to the entire content library students pay an affordable fee of $29/month or $300/year. This is either on par or much lower than alternative options. We are committed to maintaining a low price point as we believe in the importance of data fluency for a large group in society. There’s room for improvement here though: ideally, we would have a price point adjusted to the GDP per capita in a country for example (we have subscribers from most countries in the world).
As we’re getting better at attracting students, our focus should be shifting more towards student retention. Supporting students in their journey certainly helps to improve retention. Unfortunately, due to our rapid growth, we’ve not always been able to provide excellent support. By the end of 2017, we’ll have a dedicated customer support and a customer success team. Not surprisingly, our data shows that product improvement and the addition of more content also has a positive effect on student retention.
Step 3: Attract and retain the best instructors
As mentioned above, we believe creating a network of expert instructors to teach our data science courses. Instructors build DataCamp courses for various reasons. The three main reasons being:
- Impact. DataCamp has become the most effective way to reach over 1.7 million students eager to learn data science. DataCamp’s reach helps instructors build their personal brand as well.
- Belief in the educational vision. Many educators care. They are looking for novel and innovative ways to teach data science topics. Our focus on data science enables us to offer the best learning tools.
- Financial. We share revenue with instructors in a unique way: instructors are paid for every student that finishes a course. This ensures that incentives are aligned around creating engaging courses that our students enjoy.
Compared to writing a book, our instructors can reach more students, teach more effectively and make more money.
Making sure our instructors get value from creating content on DataCamp is essential for the long term success of the platform. Therefore, we have a content team focused on (i) finding the best people in the world to join that network, (ii) supporting instructors to teach according to best practices and (iii) providing practical support and insightful feedback to help them create and maintain the best data science courses out there.
As there’s a learning curve to creating DataCamp content, we want instructors to create multiple courses. The following external instructors already created multiple courses on the platform: Garrett Grolemund, Dhavide Aruliah, Charlotte Wickham, Rick Scavetta, Ben Baumer, Justin Bois, David Robinson, Jason Myers, Daniel Kaplan.
In addition to forging a network of instructors, we’re designing a scalable process for the creation of high-quality content. Technology is key in this context. We have invested heavily in technology that makes it easy to create interactive courses on DataCamp. That said, there’s still a lot of room for improvement on DataCamp’s authoring environment. It’s a core part of our strategy to make it very easy and scalable to create content for DataCamp’s platform (see this post as well). With this network of instructors and efficient authoring technology, we believe it’s realistic to have about 1,000 content modules (=courses/projects/practice modes) by 2019.
It doesn’t stop at authoring great content though. Students have completed over 70 million exercises on DataCamp. We can learn a lot from that data, and our content team and instructors can and do use that data to improve the content. We have an unfair advantage here as a company, since a large percentage of DataCamp employees and instructors actually know how to work with data well.
Step 4: Help companies to retrain their workforce
Most people learn data related skills in a professional context. Therefore, the end game is to offer scalable training solutions to companies. It helps increase DataCamp’s impact and provides a more stable revenue stream.
With already over 1.7 million registered students, we can rely on engaged professional students to help bring DataCamp into their organization. Often, we start by training smaller groups of people (20–50). Their enthusiasm then spreads within the organization and other groups sign up for a DataCamp for business license. Ultimately, we want to help the entire organization become more data literate through enterprise licenses. If you’re curious how DataCamp is helping companies, have a look at the case studies on our business page.
As we move towards helping organizations retrain their workforce, we’re also expanding our product offering to cater to the needs of larger organizations. In the next months (years), we’ll continue to launch product features focused at helping organizations such as:
- Ability to create custom learning paths for groups and individuals
- Detailed reporting on what and how employees learn, their skill level, what they struggle with, etc.
Step 5: Expand our offering
Many students, employees & investors have asked me some version of “Why is DataCamp not doing XYZ”? With XYZ being, anything from mentorship, to recruiting, to certification, etc. The real question is often not why not but when is it appropriate.
In the first years of building DataCamp, we deliberately want to remain hyper focused on building a learning resource with the best instructors. We believe that creating a great learning resource for data science is not only what’s most missing in the market right now. It’s also the most defensible asset. The defensibility follows from: (i) the fact that it takes a lot of time, effort and the right team to create such a resource (content+product), and (ii) that it’s an asset that captures people and companies at the start of their journey towards data fluency.
When we feel we master the first four steps, we want to expand our offering (at the time of this writing, DataCamp is gradually shifting focus to step 4). The importance of certification for skill level was discussed in a previous blog post, and seems the most likely candidate for initial expansion of our offering. We know our individual subscribers and corporate clients want mentorship, help in their job search, etc., so we won’t stop there. DataCamp wants to help as they progress the journey towards more data fluency.
So.. In a nutshell
- 1. Build the best learning platform for data science
- 2. Attract and retain students
- 3. Attract and retain the best instructors
- 4. Help companies to retrain their workforce
- 5. Expand our offering