Data Scientist has become a catchall title for anyone who works with structured or unstructured data. For some companies it is an IT function while for others any analytical person is called a Data Scientist because it is the most desirable title and the company can attract more candidates using that title. It doesn’t mean the job you will be doing is really data science. As analytical recruiters, Smith Hanley is biased toward candidates with excellent statistical credentials either through academic training or on-the-job experience. Without programming skills though you can’t call yourself a data scientist. The other two qualities of a top data scientist are less tangible than statistics and programming skills, but essential for project and long-term success.
There are many tools, languages and technologies out there for a data scientist to have in their tool kit. Which are the most commonly used by today’s data scientist? According to a poll by KD Nuggets, Python was used by about 55% of people in the field of data science in 2017 or almost 15% more than in 2016. The second most common tool was R, which was used by about 52% of Data Scientists in 2017; 6.5% more than in 2016. SQL was the third most commonly used language at about 35% of all data scientists surveyed in 2017. Use of SQL actually decreased by just over 1% from 2016. A 2015 IBM report said there were 2.35 million data analytics jobs in the U.S. They estimated this number to increase to 2.72 million by 2020. Quartz Media reported that Python and R are the two most commonly used tools in data analytics and data science and for those interested in machine learning they are also the top technologies. What does all this mean? For those looking to get into the field of data science place a heavy emphasis on mastering Python and R.
The level of expertise you have in statistical techniques, experimental design and machine learning will define your success as a true data scientist. The variety of data, the way the data is logged and the various ways that experiments can be run gives you many, many options for testing various hypotheses. Doing it right or statistically correct will be the difference between following wrong paths and finding the right answers. Modeling, machine learning and data mining skills both theoretical and practical have tremendous value for your research and identifying opportunities for your business.
Creativity, critical thinking, cognitive flexibility, open minded problem solving are all qualities that the best data scientists have. Their curiosity helps them identify patterns, generate hypotheses and conduct tests that might take them down paths they didn’t anticipate. They can make the leap or the abstract connection to a path they didn’t predict, and they have the analytical and programming skills to assess that path effectively.
Yes, good communication skills is on every job order ever written! Because data scientists are synthesizing so much information and having an impact across so many areas of a business, they must work with a variety of people who have varied levels of understanding of the data and analytical process. Helping them to understand why and how your work can help them is critical to the success of implementing your findings. Storytelling in words and through data visualization is a key requirement for most data science roles.
Interested in pursuing a data science career? Contact Smith Hanley Associates‘ Data Science Practice Lead, Paul Chatlos, at email@example.com or 203.319-4304.