There's a bit of a gold rush on jobs in artificial intelligence. It's well-paid, interesting work. But is there really a scarcity of data science professionals, or a lack of professionals with KNIME expertise, to work in this field?
I asked experts what advice they have on starting a career in data science. 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.
Is There a Lack of KNIME Expertise in the Market?
Vijaykrishna Venkataram, Senior Manager Data Analytics at Relevantz, said, yes there was.
“When I read your article [KNIME Experts wanted!], I thought it looks like somebody has spoken my mind! When we started looking for people to join our group in 2019, we were specifically looking for people that have experience with KNIME. Back then, there were not many KNIME experts available."
His tip for employers is not to wait for someone who's an expert in KNIME, but rather "Look for any Business or Data Analyst who fits your business requirements and teach them KNIME. I’m sure within a few weeks they will easily pick up KNIME.”
Up to Speed with KNIME in Two Weeks
Vijay speaks from experience: “When we started working with KNIME and management asked where to find people who can work with KNIME, I brought in two business analysts and gave them two weeks to get familiar with KNIME. At the end of week two they were already able to crack some of the problems I gave them.”
Of course, you can learn KNIME software on your own, by working on some exercises and taking a course, you can even get your knowledge certified. As Vijay confirms, “The KNIME Certification Program is also very helpful here as we got our people L1- and L2-certified. It’s just a fraction of the costs you would normally spend on proprietary software. So now we don’t have to wait for talents on the market but rather develop our own talents internally.”
Facilitating Collaboration Among Professionals
Meanwhile, Andrea De Mauro, Head of Data & Analytics at Vodafone, demystifies the need for a single superhero data scientist, and explains loud and clear that collaboration is needed among different professionals.
“The traditional myth of a data scientist as a superhero who takes care of entire end-to-end processes or the full landscape of complexities around analytics is far from the reality. Today there are plenty of roles available in the amazing world of data analytics. I normally use three main role families to explain them: data analysts or business analysts, who have deep business expertise in a specific domain and ‘translate’ needs between other data practitioners and the business teams, data scientists who focus more on the algorithms and on the scaling of the analytics capabilities, and data engineers who are involved with the implementation and maintenance of the full technology stack.”
How to Get a Career in Data Science Started
To start a career in data science, I asked my interview partners what advice they had about the skillset for this profession.
Passion and Curiosity
Dennis Ganzaroli, Head of Report & Data-Management at Swisscom, Switzerland, replied that in addition to being skilled, it takes passion: A passion for data and problem solving, a passion that goes beyond the working hours.
“Whenever somebody asks me this question, I ask back: What are your hobbies? And if data science is not your hobby, you have to change hobbies! I think that learning alone is not enough. You must live it and love it to succeed.”
Tosin Adekanye, Qatar Financial Center Regulatory Authority, picked reading, sharing, and simply 'getting started' as her three pieces of advice:
“Read a lot. Medium is a good platform. You don't have to fully understand everything you read, but you’ll be familiarized with the topic, and this will help in the future. Also, don't be afraid to share your work. The first things I shared weren’t always that good, but having shared them, I got feedback which helped me improve. And finally, just get started! You don't have to be perfect, but you're going to grow and build up from there.”
Knowledge of Theoretical Concepts and Processes
Curiosity is a good thing, but more than anything else, an aspiring data scientist needs a solid grasp of the algorithms behind the work. The data experts I interviewed confirmed the importance of being skilled and knowledgeable of the theoretical concepts and processes in your field.
When Keith McCormick works with students in his UCI Continuing Education classes, he invites them to focus on processes and concepts before devoting too much energy to practical implementation:
“I would recommend that during their data science journey they devote their time and effort equally to process and understand the life cycle, concepts, and execution. For example, if they are seeking out a program, a boot camp or any certificate courses where the focus is exclusively on coding and the data science life cycle and process are ignored, they should find a way to address those topics because they will need them on the job.”
Versatility to Integrate with Relevant Tools & Environments
Curiosity and a thirst for knowledge seem to be the necessary distinctive traits of a data scientist. But what about tools? What specific tools should a data professional know? The eternal debate between coding and low code seems to find conciliation here.
Andrea De Mauro explains that a portfolio of different tools is necessary for all data professionals:
“It's really important for an aspiring data professional to have the right set of tools and options, and to know how and when to leverage them. You don’t need to learn all the tools, but have a good mix of products that complete each other as part of a versatile toolbox.”
That versatile toolkit should definitely include a low-code platform for data science, like KNIME.
“The type of toolkit I would recommend possessing would include a business intelligence product, focusing on enabling scaled dashboards and data visualization capabilities, and a versatile analytics platform. KNIME is a great example of low-code analytics platforms. I would also include more traditional code-based analytics tools, which can be integrated perfectly with a low-code platform like KNIME.”
Experience, Experience, Experience
Keith also encourages his students to get some real-life experience. The theory is one thing, but learning how to apply that to complex, often messy data is another.
He says, “They need is to get some real, practical experience — for example, with a data science apprenticeship, not unlike a medical student's residency. During this time, it’s crucial that they get the right mentorship from somebody who is more experienced and can guide and inspire them.”
Experience on use cases is highly appreciated, if not required in some hiring processes. Vijay Venkataram also insists on this:
“Most of the limelight has been on the machine learning part in data science, and many data science students jump directly into model building and prediction. But in the real world, this is only 10-15% of the work. I’d suggest anybody who’s trying to get into this industry first learn some ETL and data preparation, or something about feature engineering. For example, in my industry, we talk a lot about scorecard development or fraud analytics. Especially in fraud analytics, your prediction target, the fraud rate, is very low. So how do you come up with a good model for prediction? This is where you need to understand the data and the business and then you can jump into model development.”
Andrea also recommends experience, experience, experience:
“Do not wait for your first job to get the experience you need. You can start getting your hands dirty with data and algorithms well before your first interviews! My advice will always be to go out and look for the right opportunities around you to apply analytics first-person. Look for local charities in need of help with their data. Check out websites like Kaggle to find free online competitions and gain experience. This will give you opportunities to build your own first portfolio of models and successful data analytics applications.”
Resources to Become a KNIME Expert
In summary, the winning combination for a successful career in data science is curiosity, knowledge, experience, and a portfolio of tools, including a low-code platform like KNIME Analytics Platform. As a bonus, here are some comments by former junior data scientists (now turned senior) on where professionals can learn more about data science and KNIME software.
Martin Munch, Global Head of Plan-to-Produce at Novozymes, turned to the KNIME self-paced courses to learn more about KNIME and data science. “The self-paced courses are a great place to start. Otherwise, just get your hands dirty straight away. Google and the KNIME Hub are your best friends.