Data Scientists

Entry-level data scientists must have one to three years of experience working with large datasets, utilizing databases, and using general-purpose programming and statistical modeling languages such as Hadoop, R, and SAS. This type of experience can be obtained by participating in internships, summer jobs, or co-operative educational experiences at data analytics firms.

Data scientists have excellent communication skills, including the ability to explain technical concepts to executives. They must have intellectual curiosity (because Big Data is always changing), creativity (since wrangling Big Data into usable datasets often takes a lot of ingenuity and imagination), strong problem-solving and analytical skills, and the ability to work well both alone and as a member of a team. Familiarity with both theoretical and applied technical details of predictive modeling, machine learning, statistical analysis, and data visualization is also important. Other important traits include a detail-oriented personality, time-management skills, a strong work ethic, and a willingness to continue to learn throughout one’s career.

Data science professionals also need familiarity with many types of software languages and tools. According to the data-focused vendor Figure Eight, the following technical skills are in strongest demand: SQL (a special-purpose programming language), Hadoop (an open-source software framework for storing data), Python and Java (general-purpose programming languages), and R (a language and environment for statistical computing and graphics). Other in-demand software languages, platforms, data warehousing structures, and tools include C+, Tableau, Spark, MATLAB, Docker, Hive, SAS, and SPSS.