5-qualities-of-a-successful-data-scientist

5 Qualities of a Successful Data Scientists

What qualities do you need to be a successful data scientist? When the Metis data science Bootcamp is being developed, a lot of things were put into consideration. Some of these you will surely recognize without looking, while others may catch you off guard. These six hard and soft skills are not only important to look for in a successful data scientist hiring, but they are also important to develop in yourself in order to have a successful and rewarding career in data science. This list was created to assist us in identifying potential students for our data science Bootcamp, folks who will make excellent students and hires.

Analytical reasoning

Because data scientists convert data into information, statistical knowledge is at the forefront of our toolkit. Knowing your algorithms, as well as when and how to use them, is likely the most important aspect of a data scientist’s job. To do this properly, though, maybe both an art and a science.

A smart data scientist can use a toolkit full of algorithms to model any data and generate statistically-informed predictions and suggestions. A great data scientist can detect something ‘fishy’ in the findings she receives, recognizes that he needs to ask the client or stakeholder a few more questions before withdrawing to the code cave, and can distinguish between a game-changing revelation and an expensive blind hunch.

Developing statistical thinking: When statistical thinking is relevant to you, it sinks in the fastest. We keep our statistical thinking sharp at Datascope by challenging one other’s bold assertions, occasionally making friendly bets, and working out how to resolve it with statistics. Here’s an example you can test on your friends: How many articles on Wikipedia contain the word “the”? Is it all of them? Almost all of them? We set the over-under at 90%, developed a script to calculate the frequency of the word “the” in Wikipedia documents, and were astonished to discover that the word “the” only appears in 85% of Wikipedia articles.

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Technical know-how

Data scientists are developing tools, pipelines, packages, modules, features, dashboards, websites, and more by writing code and working with teams. On both the back end and the front end, we write code. We work with both structured and unstructured data. When we can’t locate the solution we need, we search through unfamiliar formats and legacy code and “build our own” tools.

The spirit of a brilliant successful data scientist is that of a hacker. Because the gold standards in this industry change at an alarming rate, technical adaptability is just as crucial as experience. To ensure that we can move at the speed of demand, data scientists collaborate, support open-source, and share our knowledge and experience. If your data scientist is a quick study, you’ve made a wise investment that will pay you in the long run.

Developing technical knowledge: If at all possible, write code every day. Learn about the tools you’d like to utilize, but don’t just read about them; put them to the test. Make use of a tutorial. See what happens if you change it. It must be broken. Examine someone’s efforts that are written in a language you are unfamiliar with. If you come across a new tool or service that you’re interested in, start by saying “hi, world.” Work in little increments.

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Multi-modal communication skills

Most of the time, after the analysis is concluded, the outcomes aren’t pretty. That’s not to suggest they’re useless, but they’re frequently ensnared in obfuscated readouts or plots that appear intuitive to the expert but are cryptic to the rest of the team and stakeholders. Algorithmic output must be understood and disseminated in order for it to leave the data science team and enter the hands of the rest of the firm, where it may be put to use in accordance with its utility.

A smart and successful data scientist can use common ground, metaphor, skilled listening, and storytelling to contextualize and interpret an issue and its solution to people from all walks of life. Written communication for a statement of work or a report, visual communication for clear and intuitive plots and visualization, and oral communication for presentations, project specifications, check-in meetings, and iterative design are all examples of this. When it’s evident that no one is on the same page, your data scientist can call a meeting to a halt, create a diagram on the whiteboard, and compel consensus from a varied group, you’ve got yourself a highly useful member of your team.

Developing multi-modal communication: Practice writing and chatting to “regular” people about your technological projects. Trim yourself down to the essentials. It’s crucial to learn how to edit yourself. Use “crayon wireframes” to practice visual communication—we use them all the time, and they help us think visually and iterate quickly. Sketches are also useful for ensuring that everyone is on the same page. If the words you’re saying appear to be in sync but the visuals don’t, you may have avoided some future problems.

Curiosity

So your data scientist is a statistical and technical wizard who can explain a Markov chain to a grocery cashier. What else sets the elite apart? Curiosity is the first of our three essential soft skills. The possibility to work on a steady stream of fresh and tough challenges is appealing to many people who are drawn to data science. They are folks who have been questioning “why” and “how” since they were able to form the words in their tongues.

A smart data scientist will take a request, put it into action, and confidently produce the prediction or analysis. Because whatever he performed ignited that curious itch, a skilled data scientist will ask for more data, or to interview people, or attempt something new in the next version. Machine learning competitions may irritate curious data scientists because they don’t have access to all of the levers and options for asking questions and digging further. Curiosity masters are ready to challenge their own assumptions.

Is there such a thing as a foolish question? Actually, most likely… sure. Who cares, though? They’re nearly indistinguishable from truly outstanding questions that no one has thought to ask yet, in our experience. So we don’t make any distinctions and ask a lot of questions that may or may not be foolish. If you ask enough foolish questions, you’ll come up with something clever. Seek out people that are different from you to discuss your opinions with. Allow your thoughts to roam. Raise your hand if you have a question. And never assume that specialists see everything you do; don’t dismiss your intuition when it warns you that something isn’t adding up. You may ask a dumb question (and learn), or you might ask a clever question (and learn) (and discover).

Creativity

Creativity can be applied in a variety of ways, including but not limited to project design and communication. Of course, a data scientist who can turn results that would take a number of master’s degrees to fully comprehend into an appealing and easy-to-grasp report or visual is a skill with significant payoffs. Creativity is the fuel for effective communication, and that is a no-brainer.

Aside from aesthetics and communication, the best data scientists are problem solvers who have an odd connection with the word “no.” Your data scientist really wants to incorporate those user-level data sets into the algorithm, but they’re locked away in another department? She devises a method to predict their effects using population statistics, or she creates a simulated report with fictitious data to persuade the C-suite that constructing a bridge across departments is well worth the risk or work. The client wants to know how much foot traffic their possible new outlets will get next Friday, but the information doesn’t exist or isn’t available? He bases his estimates on publicly available transportation data and recommends a small, low-cost sample-gathering operation to develop a turnstile-count-to-total-pedestrians conversion heuristic.

Successful data scientists are irritated by “no,” to the point where they find a way around, over, or through it, or they back up and pursue a different path entirely. Constraints in design are both annoying and enticing. A data scientist who responds with “no” and then “wait, hold on… let me think” could be a wonderful creative thinker.

Cultivating creativity requires letting go of judgment when you get an idea. Encourage others to ask questions, think outside the box, and engage in “yes, but” conversations. Give those around you the freedom to “fail” without fear of judgment or condemnation, and they’ll most likely do the same for you. When you’re around by others who are thinking and working creatively, your own creativity will blossom as well.

Conclusion

So far so good, we have highlighted and discussed the top qualities of a successful data scientist. These are qualities one needs to develop in order to be the best in this field as a data scientist.

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