We recently released a new RapidMiner Academy course in partnership with Old World Computing called Establishing Data Science in Organizations. The course helps you understand that the adoption of data science in an organization depends not only on identifying suitable use cases and availability of data, but also figuring out how to involve and get buy-in from different stakeholders.
We wanted to use the launch of this course to talk a bit more about RapidMiner Academy—what it is, why we made it, and how you can use it to help you get the most out of your machine learning journey, whether you’re a data science professional or a domain expert just starting to learn about how data science can help you.
What is RapidMiner Academy?
RapidMiner Academy is an online learning platform that teaches topics related to data science and machine learning, from introductory material to more advanced topics. The Academy developed directly out of in-person, on-site trainings that we used to do to support our customers. There were a few problems with the in-person approach, not the least of which was scalability—we wanted anyone to be able to access this content and learn about data science and machine learning, whether they’re a paying RapidMiner user or not.
The overall purpose of the Academy has always been to focus on data science enablement. Many RapidMiner users come to the product without any background in data science and machine learning. The need to provide support that focuses specifically on data science know-how has been a critical aspect of RapidMiner’s success.
Although we certainly talk about RapidMiner products in the Academy—and hope that people who find the Academy without knowing about RapidMiner will become interested in trying it out—direct product training was never our main goal. Data science enablement was primary, with tips for using the product and driving customer loyalty and retention being secondary goals.
The online model was also more realistic than in-person training as we’ve moved to a subscription-based business and have new users starting with the product all the time. We want those users to do ever more sophisticated data science tasks as they get more familiar with what data science and machine learning—and RapidMiner—can do for them. A one-off, on-the-ground training just can’t support users’ continued growth and upskilling in the same way that an always-available online resource like Academy can.
How RapidMiner Academy Works
Academy used to present information in a college-lecture style format, but we found that didn’t work very well—it’s easy to lose engagement from busy people who are trying to learn a bit here and there and don’t have time to sit through hour-long videos.
That’s why we’ve moved to what is called a micro-modular approach, with information chunked into five- to ten-minute lessons, focusing on a single aspect of a topic at a time. Not only does the micro-modular approach work well for our users’ busy schedules, it also lets them focus on learning how to solve just a single problem, or address a small gap in their knowledge, without needing to do a full course—although a single module often becomes the jumping-off point for people to tackle a whole course.
Micro-modules make it easier for us to keep up with changes in the world of data science and with RapidMiner as well. We can easily add and remove content as needed without having to re-record long lectures or completely overhaul courses, ensuring that we’re as up to date as possible.
We use these micro-modular pieces as the building blocks of larger groups of content like our Courses, which string together multiple modules and are designed with our users’ role-based learning requirements in mind. The courses are also grouped into Learning Paths that combine multiple courses to guide you from the basics to advanced data science skills. Want to celebrate your learning and show off what you’ve accomplished? We also offer Certification Exams that provide an assessment and proof of your skills in the form of a digital badge.
Another advantage of micro-modular content is that these pieces can be reused in different learning paths and courses to reinforce what’s being taught. If you come across a module you already know from a previous course or learning path, you can skip it. But if you’d like to review, it’s there for you to review again to cement your understanding and provide the appropriate background for what’s coming next in the course or learning path you’re pursuing.
Finding Your Way Around RapidMiner Academy
Our courses, learning paths, and certifications provide the primary way that the content in the Academy is organized. In addition, the content we have at the Academy is grouped into general topic areas like Applications & Use Cases, Machine Learning, Data Engineering, and Administration and Deployment. These domains also represent the certifications that are available, with two levels of proficiency offered at each: “Professional” and “Master”.
- Applications & Use Cases: Mapping real-world problems to relevant machine learning techniques; deploying models to directly impact enterprise ROI.
- Data Engineering: Creating replicable processes to automate data blending and cleansing—from disk to lake to cloud to stream.
- Machine Learning: Understanding and applying state- of-the-art AI concepts and techniques; providing insight to opportunities and risks.
- Platform Administration: Providing infrastructure to scale and integrate across the entire enterprise; ensuring data governance and compliance.
You might be surprised when you first visit RapidMiner to see that personas aren’t the primary guiding principle of how courses and learning paths are organized (although you can still sort by persona if you’d like). We chose not to have personas be primary because, globally and across industries, persona definitions can vary immensely. For example, “analyst” or “data scientist” can mean a lot of different things, so people might end up not recognizing themselves in the persona names if we used those.
Using the four content domains outline above makes it easier for learners to focus on the skills they’d like to develop, regardless of their title. And, depending on your role in the organization, you might want to focus on one or more of these domains. By grouping content this way, we’ve provided a way for you to focus on what you need to do for your specific role, with the ability for you to go from very little knowledge to advanced topics in data science and machine learning.
We hope the above gives you a good overview of what RapidMiner Academy is, why we built it, how it’s structured, and, most importantly, how you can use it to improve your data science and machine learning know-how.
We’re always looking to expand and improve on what the Academy has to offer. For example, we’re currently building out our Solutions Goldmine that collects end-to-end applied data science solutions that have been created in RapidMiner. More than just use cases or templates, these fully documented examples contain all the complexities and challenges that are faced in the real world, giving you a great starting place to explore exactly how data science is used for business applications.
Head over to the Goldmine and give the solutions a try today! And, if you’re a RapidMiner user who wants to contribute to this collection, we have a practitioner certificate waiting for you.