I host a monthly podcast on LinkedIn Live called My Data Guest. Each episode features an interview with an expert — on data, education, management, and more. All of them are also technical experts in KNIME. All 12 episodes are available on my YouTube channel, each offering something to learn. Today let's look at what they called out as the KNIME Analytics Platform’s top features for higher education.
Low Code to Practice Theoretical Concepts
According to Keith McCormick, KNIME is a go-to teaching tool. With the KNIME graphical interface, it’s easy to explain how a certain algorithm is applied in real-life data science.
“In my courses and in many courses of other instructors in our department, KNIME is the go-to tool for teaching. For example, in a course about introduction to modeling, where I cover every week a new algorithm, such as scale-dependent algorithms, classification algorithms, etc, what I really need is a tool that helps students see how the theoretical concepts that they read in the books can be implemented practically. From an educational perspective, KNIME offers a great opportunity to really practice theoretical concepts.”
Low Code and Complex Algorithms to Reach Results Faster
Academic professor Malik Yousef (Professor and Head of the Galilee Digital Health Research Center, Israel) also advocates the integration of KNIME with Python and R. “I love KNIME. I mainly use KNIME in combination with Python and R. All my teaching and all my collaborations involve KNIME to some extent.”
He adds a concrete example of how and why he started using KNIME to overcome the hurdle of programming, reach results faster, implement complex new algorithms, and easily communicate his work with non-programmers:
“Let me tell you a brief story: A few years ago, I went to a scientific conference in Germany. I discovered KNIME when talking to a colleague about an issue I had in my Java code. Nothing major, but to fix it it would have probably taken me a week. My colleague suggested I could just use KNIME to solve the problem. I was impressed, and that was when I started learning KNIME by myself. After this encounter, I started my work on maTE, which was one of my first complex algorithms implemented in KNIME. But even at this level of complexity, the workflow structure helps me communicate the work to non-programmers.”
Low Code as a More Efficient Tool for Students and Teachers
Sometimes the border between enterprise and education is not well-defined. We’ve encountered a number of experts who are using KNIME in both worlds. Andrea De Mauro (Head of Data & Analytics at Vodafone) is one of them: “I have used KNIME for a long time now, both at the universities and at work with Vodafone and P&G.”
Andrea underlines the advantage of having a graphical user interface, as it removes the coding barrier for non-programmer students and allows him to skip teaching coding and focus on data science concepts and practice instead. The result is a more efficient learning process for both teachers and students.
For teachers: “KNIME makes the process of coding convenient so you can focus on the core analytical tasks. Those who would like to start using analytics feel often discouraged by their lack of coding skills. KNIME offers a solution to this. Visual tools like KNIME let you ‘see’ and track what’s going on at each step. You can easily identify where the problem is if you are stuck at a certain point. This really supports the educational experiences while teaching data science courses. In short, this increases the efficiency of the learning process for the students.”
For students: “KNIME makes the learning more accessible, and sometimes also more fun. The experience of building a workflow step by step is somewhat enjoyable for them. The usage of KNIME nodes makes the learning modular and progressive. A node makes you ‘see’ what is going on with your data in the flow very easily. The Joiner node, for example, combines the two input tables into one single output table. Or the Loop nodes apply iteration to a sequence of steps between Start and End. By making it ‘visual,’ you understand it better and reduce the chance of making mistakes.”
Transcriptions of the original interviews have been collected and published in the booklet “Best of KNIME” available for free download from KNIME Press.
A Visual Interface for Clear Communication of Concepts and Practices
The top feature of KNIME Analytics Platform for education seems to be its low-code visual interface. Per our education experts, the visual interface makes the data science concepts and practices very clear to learn and teach. This does not mean you cannot code within the platform; you can add Python and R code via the KNIME-Python and KNIME-R integration.
The other very much appreciated feature is the large coverage of machine learning and statistics-based algorithms. Especially if combined with the KNIME-Python and KNIME-R integration, this expands the algorithmic boundaries and represents a big plus in the adoption of the KNIME software for teaching.
KNIME Analytics Platform is easily accessible, since it is open-source and free to download.