Select Page

Data scientists are in high demand as companies are constantly seeking to get the most value from their resources. Greg Boyd, director at consulting firm Protiviti, says, “as organizations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources, the role of the data scientist will continue to expand in relevance.” The rising stars of many businesses are the savvy data scientists who can not only manipulate large amounts of data using complex statistical and visualization techniques but also have a strong acumen that they can derive forward-looking insights. Now, to be an elite data scientists, one must have a particular set of skills. Here are some of the essential traits that data scientists have.

 

Critical Thinking

In order to be able to apply objective analysis of facts on a given topic or problem, they need to be solid critical thinkers. According to Anand Rao who is a global artificial intelligence and innovation lead for data and analytics at PwC, “they need to understand the business problem or decision being made and be able to model or abstract what is critical to solving the problem, versus what is extraneous and can be ignored.” They must have the experience, but also the ability to suspend belief. Data scientists should be able to take a step back and assess a problem or situation from multiple points of view.

 

Coding

Coding is one of the biggest jobs of a data scientists. The best data scientists know how to write code and are capable of handling many different programming tasks. According to Rao, “to be really successful as a data scientist, the programming skills need to comprise both computational aspects—dealing with large volumes of data, working with real-time data, cloud computing, unstructured data, as well as statistical aspects—and working with statistical models like regression, optimization, clustering, decision trees, random forests, etc.” Due to the big data boom that started in the beginning of the late 90s, there has been a bigger demand for data scientists to understand and be able to code in languages like Python, C++ or Java.

 

Math

If you do not like math or are not as proficient in the subject, data science may not be the right career choice for you. Data scientists need to leverage their expertise in mathematics to develop statistical models which they can then use to develop or shift key business strategies. Top-notch data scientists “excel at mathematics and statistics and can collaborate closely with line-of-business executives to communicate what is actually happening in the “black box” of complex equations in the manner that provides reassurance that the business can trust the outcomes and recommendations,” according to Greg Boyd.