6 Qualities Of A Good Data Scientist

Posted by Siddharth on September 2nd, 2022

Introduction

The following hard and soft skills are essential to seek in a data science hire and develop in oneself to prepare for a fruitful and rewarding career in data science. This list was created to assist us in finding the best candidates for our data science course. 

Here are the 6 qualities which make the data scientists perfect,

• Statistical thinking

• Technical acumen

• Multi-modal communication skills

• Curiosity

• Creativity

• Grit

Statistical thinking

Statistics knowledge is at the forefront of our toolset since data scientists are specialists that transform data into information. The most important aspect of a data scientist's job is probably knowing your algorithms and how and when to use them. But mastering this can involve both art and science.

A skilled data scientist can model any provided data and apply a toolkit full of algorithms to generate predictions and suggestions supported by statistics. A competent data scientist can distinguish between a game-changing revelation and an expensive blind hunch by smelling something "fishy" in the information she receives, sensing the need to ask the client or stakeholder a few more questions before withdrawing to the coding cave.

Technical acumen

Data scientists collaborate with teams to develop code to create tools, pipelines, packages, modules, features, dashboards, websites, and other things. Both the front end and the back end have their own code. We perform scheduled and unstructured work. When we can't locate the answer we need, we "make our own" tools by sifting through obscure formats and antiquated code.

An excellent data scientist has the spirit of a hacker. Data scientists collaborate, support open source, and exchange information and expertise to ensure we can respond quickly to market demands. Because the industry's gold standards are changing at an alarming rate, technical adaptability is just as crucial as experience.

Multi-modal communication skills

The majority of the time, the conclusions of the analysis are not pleasing. However, they are frequently caught in readouts that are difficult to understand or in plots that make sense to the expert but are written in hieroglyphics to the rest of the team and stakeholders. The algorithmic output must be understood and conveyed to go from the hands of the data science team to the rest of the organization and be put to use in line with their utility.

A smart data scientist can use metaphor, common ground, astute listening, and storytelling to contextualize and interpret an issue and its solution to interested people from a wide range of backgrounds.

Curiosity

Your data scientist is a technical expert with solid statistical knowledge who can explain the Markov chain to a checker at a grocery store. What else makes the elite unique? Curiosity is the first of our three crucial soft talents. 

Many people drawn to data science find the chance to work on a never-ending supply of brand-new, difficult challenges, particularly enticing. They are individuals who have questioned "why" and "how" ever since their tongues were able to utter the words.

A competent data scientist will accept a request, carry it out, and confidently give the forecast or analysis. Because of anything he accomplished that piqued his curiosity, a brilliant data scientist would return and request access to further data, user interviews, or the opportunity to attempt something new in the following iteration.

Creativity

It is obvious that creativity is the fuel for effective communication. Creativity has many more uses than only the obvious ones in project design and communication. Of course, a data scientist's ability to distil facts that would require several master's degrees to comprehend into an appealing and understandable report or graphic entirely is a skill with significant payoffs.

The finest data scientists, however, go beyond aesthetics and communication to solve problems creatively and have an odd relationship with the word "no." These user-level data sets are locked away in another silo inside the organization, despite your data scientist wanting to include them in the algorithm. 

To persuade the C-suite that bridging departments is worthwhile, she devises a technique to model their impacts using population statistics or creates a simulated report using fictitious data.

Grit

We've covered the traits and abilities listed above, as well as technologies that emerge, demand attention, and disappear faster than songs on the top forty, messy data that should fit into the client's model but doesn't quite do so, dead ends, wrong turns, roadblocks, and red tape, teams with diverse goals and personalities, budgets, deadlines, clients, teams, and contractors, moreover, there is the unicorn-magician-programmer-statistician who is meant to tie everything together. People that make it through have a solid internal supply of grit.

A rational individual can take an unforeseen leave of absence due to all these difficulties and obstacles. Grit is that inner motivation that pushes us beyond hurdles, transforms setbacks into design restrictions, keeps us moving forward in the face of actual failure, aids us in restraining the need to take things personally, and wipes the dirt off our shoulders. When grit is in action, we are less competitive, allowing us to support and educate one another. We start to feel the need to take on the uncharted and unfamiliar.

Conclusion

I hope this article will be helpful. If you want to become a data scientist, you should have all these qualities, and most probably, you should have an interest before entering into any field. These qualities and a good data science course institute can make you a better data scientist. If you want to become an aspiring data scientist, you should visit the best data science course in Bangalore that is provided with an IBM certification.