How Data Experts Get Started with KNIME

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 episodes are available on my YouTube channel, each offering something to learn.

Let's see today what our experts say about their learning experiences with KNIME.

Curious about where data experts first encountered KNIME? Some users discovered KNIME Analytics Platform at universities during their study program, some during a tool search, and some followed recommendations by senior colleagues. Some users discovered the tool in the middle of a struggle with Excel, others were in the middle of a struggle with programming a script. Some users needed a tool to enable easy collaboration within the team and some needed a less expensive and more efficient alternative for data science.

University courses, senior colleagues, and tasked tool searches seem to be the starting points for many journeys. The first encounter with KNIME seemed to happen either at university, when working with colleagues, or when bosses task their juniors to perform an exhaustive search of alternative tools for data science. The curiosity sparks there.

They particularly highlight benefits such as the intuitive no-code/low-code environment, faster time-to-insight, the platform's quick learning curve, and gaining access to advanced analytics techniques.

Emiliano Amendola (Financial Data Analyst at United Nations): “In our final university project, we implemented a cross-sell/upsell campaign using several algorithms, such as the apriori algorithm or frequent itemsets. We developed this project in KNIME Analytics Platform. I was comparing the time required to deliver the results with KNIME vs. hardcore programming. The difference was huge. So for me this was the engaging point to start using KNIME ever since.”

KNIMEST (Researcher): “I learned about KNIME from a senior colleague at work seven years ago. As a non-programmer, I was attracted by the fact that it was intuitively understandable even for me.”

Raffaello Barri (Business Process Specialist at Arcese): “I discovered KNIME at work, after my boss asked me to explore KNIME as well as low-code tools in general. After realizing how powerful low-code analytics can be, I was shocked that no one during my university studies had ever mentioned it. When you finish university, you usually have a trade-off in mind: You either choose simplicity (for example, Excel, which is quite limited in its analytics applications), or you go for traditional script programming, which is powerful but can be tough. For someone like me, who is not a programmer, KNIME’s visual programming environment is the perfect solution.”

Anil Kumar Sharma (DGM - purchase (CPPD) at Dabur India Limited): “For me, the shift was gradual. We were using Excel, but I faced major problems with data wrangling tasks that were eating up a lot of time. This is why I searched for a different solution, and then I learned about KNIME. Nowadays the business user needs insights quickly, and the delivery of solutions should also keep a fast pace, despite adding pressure on the user or commercials. KNIME is the ideal fit to extract insights quickly and meet diverse demands.”

Martin Munch (Global Head of Plan-to-Produce at Novozymes): “For me, the primary benefits of visual analytics and using KNIME are that its low-code environment makes it way easier to hand it over to colleagues at other departments and explain how the data flows. With black screen coding, communicating the data flow is harder, and I would end up being the sole contact person for any of the analytics solutions.”

Tosin Adekanye (Qatar Financial Center Regulatory Authority): “This might come as a surprise, but I actually only started using KNIME in January 2021. I’d already had some exposure to software like KNIME; I used SPSS Modeler from 2017. Then I used Alteryx, but the licensing was a barrier for me. I needed something that was efficient, that could let me do so many things for data science. That's when I found KNIME. Even though I haven't been using KNIME that long, you can really climb the learning curve quickly because of the resources that are available. KNIME also has some of the most approachable, most passionate employees, and that's really helped me come along in my learning curve.”

Andrea De Mauro (Head of Data & Analytics at Vodafone): “I have used KNIME for a long time now, both at the universities and at work with P&G and Vodafone. It’s an amazing tool to teach data science for multiple reasons. KNIME makes the process of coding convenient so you can focus on the core analytical tasks.”

Evan Bristow (Senior Principal Analyst at Genesys): “I discovered KNIME many moons ago. Back then I was working for a company that was doing B2B marketing research and we were using SAS and SPSS Modeler. The problem was that we only had one copy of SPSS Modeler because it had a hefty price tag. As a result, it was installed only on one person’s computer, which was quite inconvenient for team work and to scale projects. So I started looking for an alternative tool, and this is when I learnt about KNIME. We started working with it and realized that it was a lot better at doing things than other tools. Not only was KNIME better to run analyses, but it was also easier to connect to, import, combine and manipulate different data sources. Last but not least, being a free platform, it made the business happy too.”

The answers seem to all converge on this trio: university, a senior colleague, or a data science tool search. What about you? How did you first encounter KNIME software?

Next, we asked how our experts learn more about using KNIME and how fast was the learning progress? Which learning resources are available out there? Is it better to take a course or to just start hacking?

The paths to self-improvement are different for everyone. If a user prefers a more structured approach to learning, the self-paced courses and the textbooks from the KNIME Press are the way to go. The transition booklets represent a faster alternative if the user is coming from a background in data science using another tool. Our experts all agree that the way to become a KNIME and data science expert is practice. For that we can recommend the KNIME Hub, the KNIME Forum, and of course, the material from our “Just KNIME It” challenges.

Our KNIME experts learned in many different ways. The best option usually depends on personal preference.

The KNIME self-paced courses are for those who want a structured learning path with lessons and exercises.

Raffaello Barri: “I took the L1 and L2 self-paced courses for basic and advanced KNIME proficiency. They are helpful for building a good basis to kick-start any kind of analytics work. In addition to the courses, what really made the difference was starting to use KNIME at work and having to solve problems on my own. This clearly improved my skills considerably more than what I could achieve with the courses.”

KNIME Community Hub is for those who would prefer to start hands-on as soon as possible. Perhaps they go to the Beginners space, download the workflows that look closest to what they need, and customize them. Many people prefer examples, and a practical approach. You can find example workflows in many places, including the KNIME Forum, the KNIME Community Hub, and in gamification challenges.

We strongly recommend taking a look at the “Just KNIME It” challenges.

KNIMEST: “For me, the proof of how fast the learning curve with KNIME is came when I shared a sample workflow with my colleagues without having any programming knowledge and explained it to them. The visual programming environment is so transparent that they were able to customize the workflow in a few hours. This is a great success story for me and for them.”

Martin Munch: “I was already familiar with low-code/no-code tools, so conceptually it was easy for me to get started with KNIME. Adopting a new tool always comes with a few hurdles. For example, I had to get used to the large node repository, and getting familiar with the terms, the functionalities, and how to configure nodes. Overall I think the tool is very intuitive, though.”

Emiliano Amendola: “My whole experience with learning KNIME was amazing because there are loads and loads of examples available for free: the KNIMETV YouTube channel, the official documentation, many examples on the KNIME Hub, and also the KNIME Forum is a great tool to learn from others. Additionally, KNIME offers multiple resources for learning — completely for free. The thing I like the most is the little help button on each node, because you don’t have to search for help on the Internet. It helps you keep focused!”

Some have experience with other data science tools similar to the KNIME GUI. Going through the right book from the series Transition Booklets in the KNIME Press can help one gain a quick understanding of the main KNIME concepts. For example, the textbooks KNIME Beginners Luck and KNIME Advanced Luck from the KNIME Press can help you take your first steps in learning KNIME.

Anil Kumar Sharma: “For me, it was also a mix of different resources. I started with KNIMETV, and I went step by step, learning node by node. Later on, I was eager to learn with the books, in particular KNIME Beginner’s Luck and KNIME Advanced Luck. The self-paced courses on LearnUpon are also a great resource, as they gave me a quick and interesting introduction to topics I was less familiar with, such as social media analytics or NLP. Now, those terms are no longer fancy jargon for me!”

For many of our interviewees, there was also a “wow” moment, when they realized that all they could do by programming could also be done via low-code using KNIME — be it web scraping, ETL operations, machine learning, data engineering, statistics, migration, reporting, and more.

At what point did our experts realize that KNIME software is the right way to go?

Anil Kumar Sharma: “I had a ‘wow’ moment when I managed to do web scraping in a completely codeless fashion. For a business user like me, who is not an expert in data analytics, web scraping was something I had been willing to learn for a while. I tried out online tutorials using Python and different libraries, such as Beautifulsoup, for example. Compared to all that, web scraping in KNIME is as easy as ABC!”

Alida Brizzante (Student at LUISS Guido Carli University, Rome Italy) “In the beginning, we thought it would be easier and quicker to rely on our existing knowledge of programming languages when performing more complex operations. However, we ended up conducting the entire feature engineering completely codeless!”

Raffaello Barri: “What really amazed me was the Data Explorer node. Sometimes you receive data you are not familiar with, and with this node you can visualize very high-level information and interact with it in just a few clicks!”

Vijaykrishna Venkataram: “At some point, we switched from using Oracle DB to SQL DB. We had to migrate all the data to the new server and we had to adjust the data structure. This was a huge task as we had five years of data laying around. With the help of KNIME, we were able to migrate 137.5 million records, which approximately translates to 5.8 billion data points. This was done using a complex workflow which fetches the data from the database and then brings the data in the new structure. This was a big project but the work was done by only two people and without writing one single line of code.”

In these interviews we've seen the paths KNIME experts take to move from pure curiosity to high proficiency using KNIME Analytics Platform. Every journey is different, as KNIME users are all different. By compiling information on the different learning styles, we can design learning paths that accommodate different learning styles.

Tell us about your KNIME journey by writing to blog@knime.com