The 5 Qualities of A Successful Data Scientist
Together with the paragraphs that I wrote for each profession, I attempted to create a single differentiation feature. The five features could be combined into a flowchart by an aspiring data professional, which might help them decide which profession is best.
Some readers gave me feedback pointing out that I placed too much emphasis on predictive analytics as to the defining feature for data scientists. I also leaned heavily on this feature in such a way as to make it appear that data scientists do more predictive analysis than any other profession. Other data professionals may not be able to do this.
I was naturally provoked to think about what is different between data scientists and other data professionals by this constructive criticism. There are many technical skills and specific technical languages, systems, and tools that are used daily by data scientists. Data scientists, as well as other professionals, can also use soft skills to succeed in their jobs. What are the characteristics of a successful data scientist? These can either be acquired after becoming a data scientist or they can be taught.
These are the five traits that I identified that help data scientists stand out from other careers and define a successful career.
We should note that every data scientist’s role is different. However, they all share some common threads. These points can help to connect some of these threads.
Also read: Top 11 Data Preparation Tools And Software
1. Predictive Analytics Mindset
This feature’s perceived importance is what I received a lot of criticism for. However, I will double down and state that the predictive analytics mindset is one of the most important defining characteristics of a data scientist. Does it have to be the only defining characteristic? No, it’s not. Could it have been used to create a flowchart that would have separated data scientists from all other occupations? In retrospect, no, probably not.
Are data scientists capable of performing predictive analytics? Absolutely. Are there any non-data scientists? Yes. But, if I could put a Data scientist On one side of the predictive analytics seesaw, there is On the other hand, I would hope that the data scientist would always get to the ground.
It’s more than just applying predictive analytics to specific situations. It’s a mindset. It’s not just a predictive mindset, but one that is constantly thinking about how we can use what we know to discover what we don’t know. This means that predictive is an integral component of the equation.
While data scientists are not limited to prediction, I believe that working with this mindset is one of the most important characteristics of data scientists. This mindset is something that many other professions, whether data-related or not, lack. This characteristic is likely to be valued more by those who share it with others.
It is important to look beyond what we know and use it to discover what we don’t know. Data scientists need to be curious about their work. Curiosity is almost the opposite of predictive analytics: While predictive analytics is focused on solving X using Y, curiosity is about determining Y.
- “How can we increase sales?”
- “Why is churn so high in certain months than others?”
- “Why is this necessary to be done like that?
- “What happens if X is changed to Y?”
- “How does X relate to what happens here?”
- “Have you tried …?”?”
- And so on…
To be a good data scientist you must have a natural curiosity. Data science is not the right choice for you if you’re the type of person who wakes up every morning and goes about your day without noticing the wonders of the universe at any level.
The cat’s long and successful career as a data scientist was due to curiosity before it was killed.
3. Systems Thinking
Here’s some hard-hitting philosophy: The world is complex. Everything is interconnected in some way. This leads to layers upon layers of real-world complexity. Complex systems can interact with complex systems to create additional complex systems, and so it goes with the universe. The game of complexity extends beyond recognizing the big picture. Where does this big picture fit in the larger picture?
This is not a philosophical idea. Data scientists recognize this real-world infinite web of complexity. Data scientists are keen to learn as much as possible about the relevant interactions as they solve their problems. To understand why any change might have unintended consequences elsewhere, they search for situation-dependent known unknowns, unknown unknowns, and unknown unknowns.
Data scientists are responsible for learning as much as they can about the systems that they work with. They also need to use their curiosity and their predictive analytical mindset to account for as many of these systems’ interactions and operations as possible. This will ensure that they run smoothly, even when tweaked. Data science isn’t for you if you don’t understand why no one can fully explain the economy.
Now we’re at the “thinking out of the box” stage. Do we not encourage everyone to think outside the box to some extent? Of course, we do. However, I don’t mean it that way.
Data scientists are not experts working in isolation. We work alongside many different roles and meet all kinds of domain experts along our journeys. These domain experts have a unique way of looking at domains. They also think outside the box. Data scientists have a unique set and mindset that allows them to approach problems outside the box where domain experts live. If you have a good understanding of the problem, you can be the new set of eyes looking at it. You can use your creativity to come up with new ideas and perspectives.
Domain experts are not being diminished by this. In fact, the reverse is true. Data scientists act as their support. By bringing a range of skills and knowledge that are trained to do what they do, we can bring a fresh perspective to our support role. This will allow domain experts to excel in what they do. The data scientist’s creative thinking will drive this new perspective. This creativity, when combined with curiosity, will allow them to ask questions and find answers.
We need to have the technical, statistical, and other skills to answer these questions. But these skills will not be of any use if we lack the creativity to find interesting and non-obvious methods to probe and provide answers. Data scientists need to be creative.
5. Storytelling Sensibilities
No matter what their position in life, everyone needs to be able to communicate with others effectively. Data scientists are no exception.
Data scientists are often required to explain their work to stakeholders, even if they don’t want to. Data scientists must be able to tell someone how they got from point A to B, even though they may not know what those points actually are. Simply put, storytelling is the ability to create a narrative using data and your analysis process. For example: How did we get from this into this.
It’s not enough to simply state facts. The data scientist must also see where each stakeholder is located and make the narrative journey meaningful — possibly with visuals or other props that help to close the deal.
This storytelling isn’t like fiction storytelling. It’s more like “fancy explaining,” which provides an intuitive explanation that the listener can understand. A five-year-old wouldn’t be able to read a Stephen King story at bedtime. The same goes for someone who works in research and development. Pay attention to your audience.
The storytelling is not persuasive by nature, it’s explicatory. We are not data politicians. Instead, we are data researchers. Scientists who misrepresent stats to manipulate others’ will never do any good. That is up to elected officials.