What Makes a Great Data Scientist?
Data Science is about more than crunching vast numbers. This fast growing industry is short of people with the right combination of know how, interpretative skills and communication abilities. Exactly what makes the perfect data scientists is subject to healthy debate. Here are some of the opinions from thought leaders and analysts that we have come across.
It goes without saying that data scientists need top-notch mathematical and statistical skills. However, they also need to be up to date with the latest tools required to process and interpret the asymmetric information that data mining relies on. They also need to be able to take results and use them to make meaningful predictions about the future.
A quality data scientist is always willing to take their analysis one stage further and to design new tests to verify predictions and make predictions. After all, the whole point of analyzing information is to generate useful insights that can be used to maximize future efficiency and profits.
Focus, Curiosity and Persistence
With the reams of data available to data scientists it takes a dedicated professional to work out which data sets to prioritize. Data science is only as valuable as the insights it generates. Too much statistical analysis currently yields conclusions that are not relevant to the problem at hand.
Data scientists must be able to focus on their task but also have the flexibility and persistence to push their data until it yields results. As Jeff Hooper of Bell Labs once said,,”data do not give up their secrets easily. They must be tortured to confess.”
Curiosity is perhaps the data scientists’ greatest asset. Instead of explaining away anomalous results the best return to the data and explore it until they find the cause. This extra level of commitment to the art of data science is what separates the great from the good. In a post on the O’Reilly Radar blog DJ Patil, the former chief scientist at LinkedIn and now resident data scientist at Greylock Partners, describes this as “a passion for really getting to an answer.”
Confidence and Communication Ability
Data analysis often produces insights that disprove pet theories and even make current practices obsolete. A great data scientist must have the confidence to stick by their conclusions and the ability to explain them to people with less technical skill. They must be prepared to go back to the date and devise new tests to prove that their insights are correct.
Data scientists must be able to turn mathematical conclusions into stories that can be communicated effectively to everyone in an organization.
Even the best data miners cannot be expected to know everything about their science. The best are willing and able to consult with other experts in their field and beyond to add value to their insights. Networking is thus an important ability for anyone seeking a data science job. People with a broad network of contacts are more likely to be able to tap outside knowledge and skills I order to improve their own performance.
DJ Patil comments that this ability to get “many people to look at data” means that “any problems that may be present will become obvious more quickly.”
Translating data into useful insights requires a rare combination of technical skill, the ability to recognize and develop significant discoveries, and the communication skills required to explain them effectively. If you think you have this combination of skills then please contact Alan Furley at email@example.com today to discuss your future in the exciting data science industry.