A data scientist could...
|Analyze video game players' online behavior to make a game more engaging and profitable.||Help a coach make the right game decision by providing them with data about which play has the most chance of succeeding.|
|Inspire a new social media tool by looking at millions of interactions between mobile phone users.||Prevent a tragedy by looking for behavioral changes of a terrorist group to predict the time and place of a terrorist attack.|
Key Facts & Information
|Overview||Many aspects of peoples' daily lives can be summarized using data, from what is the most popular new video game to where people like to go for a summer vacation. Data scientists (sometimes called data analysts) are experts at organizing and analyzing large sets of data (often called "big data"). By doing this, data scientists make conclusions that help other people or companies. For example, data scientists could help a video game company make a more profitable video game based on players' online behaviors, or help a travel agency figure out what destinations they should make vacation packages for.|
|Key Requirements||Analytical skills, mathematical problem-solving abilities, good communication skills, ability to explain mathematical data in everyday language, attention to detail|
|Minimum Degree||Bachelor's degree|
|Subjects to Study in High School||Biology, physics, geometry, algebra II, pre-calculus, calculus, English; if available: computer science, statistics|
|Projected Job Growth (2014-2024)||Average (7% to 13%)|
Training, Other Qualifications
To become a data scientist, a person usually starts out with a bachelor's degree in one of many possible fields, and then specializes as a data scientist through experience and/or additional degrees. While a bachelor's degree is the minimum requirement, many companies prefer or require data scientists who have more advanced degrees, specifically a master's degree or a PhD. Skilled workers who keep up-to-date with the latest technology usually have the best opportunities.
Education and Training
Data scientists must have at least a bachelor's degree with a major that is typically in one of the following areas or a related area: computer science, mathematics (e.g., applied math, data analytics, or statistics), physics, or biology (e.g., genetics). Some data scientists major in economics or behavioral sciences (e.g., psychology or sociology) instead, depending on the type of data they deal with.
In addition to having a relevant bachelor's degree, many employers also want a data scientist who has a master's degree or a PhD in a relevant field, or an equivalent number of years of experience. For example, an employer may look for a data scientist with a master's degree or a PhD in a field related to statistics or data-based research, or a data scientist with a bachelor's degree and 3-5 years of professional work experience in a related area. (Professional work experience can also be gained by doing an internship with a company.)
The type of educational background a data scientist has depends on what type of data they want to analyze in their career. For example, if a data scientist wants to work with a company that develops video games to try to improve the gaming experience, it may help if they have a degree in psychology, sociology, and/or computer science. Alternatively, if a data scientist wants to work with a healthcare provider to analyze what type of healthcare coverage should be made available to their clients, a background in a biology- and/or economics-related field may be more appropriate.
No matter what specific degree(s) a data scientist has, it is also important to have an educational background that includes statistics (and preferably other mathematics courses) and computer experience. Specifically, to organize and analyze large sets of data and entire databases, experience with using relevant computer programs and having some basic computer programming skills is highly desirable.
A data scientist needs to think logically, pay close attention to detail, be a problem-solver, and enjoy working with numbers and data. Being a data scientist calls for patience, persistence, and the ability to perform exacting analytical work. At the same time, a data scientist must be able to see the "bigger picture" and draw large-scale conclusions from looking at lots of small pieces of data.
Because data scientists often must show their data-based results to other people in a company or to clients, data scientists must be able to communicate well (both verbally and in writing) with non-technical personnel. Business skills are also important, especially for those wishing to be involved on the commerce side of operations.
Nature of the Work
Data scientists, who are also sometimes called data analysts, are experts at organizing, analyzing, and drawing conclusions from large sets of data. They do this by using computer programs and applying their knowledge of mathematics and algorithms (which are step-by-step procedures used for making a calculation).
The conclusions that data scientists come to from analyzing the data help other people and companies improve their product (such as a vacation package) or solve a problem (such as fixing gameplay in a video game). Depending on the exact field that the data scientist is working in, the data scientist may also need to apply their knowledge of computer science, physics, biology, economics, or a behavioral science to do their job.
When an employer turns to a data scientist for help, they typically have a specific goal in mind or a question they want answered. For example, a healthcare provider may want to know what type of healthcare coverage should be offered to their clients, and what costs are reasonable for covering different medical procedures and medications. To figure this out, a data scientist will use computer programs to go through databases of relevant information. During this process, the data scientist might develop an algorithm (or even their own computer program) to do some of the work for them automatically so they do not have to go through all of the data manually.
Once the data scientist has drawn some large-scale conclusions based on the data they sorted and analyzed, they need a way to easily present their results to other people. Because of this, some data scientists need to be able to use computer programs that make visualizations (i.e., detailed graphs) based on data. Data scientists may also need to prepare written reports and presentations to groups of people (such as co-workers, collaborators, and potential clients), many of whom are non-technical personnel, so it is important for data scientists to be able to communicate well.
Overall, the conclusions that the data scientists come to based on analyzing all of the data goes into helping a company make a better product or offer better services.
Data scientists spend the majority of their time in front of a computer, and work in clean, comfortable offices. Most data scientists work about 40 hours per week, but sometimes they may work more hours to meet deadlines.
Some data scientists travel to provide advice on projects, supervise and set up surveys, or gather statistical data. While email and teleconferencing are making it easier for data scientists to work with clients in different locations, there still are situations that require the data scientist to be present, such as during certain meetings or while gathering data.
Like other workers who spend long periods of time in front of a computer typing at a keyboard, data scientists are susceptible to eye strain, back discomfort, and hand and wrist problems, such as carpal tunnel syndrome.
On the Job
- Make mathematical models to predict outcomes.
- Report results of statistical analyses, including information in the form of graphs, charts, and tables.
- Identify relationships and trends in data, as well as any factors that could affect the results of the data.
- Make computer-based tools for others to use so that they can better use data in their jobs.
- Debug sets of data.
- Present findings in front of groups of people.
- Recommend what actions to take based on the analyses of data sets.
- Create, maintain, and add to a database.
- Work with clients and others who want to use data, such as journalists and stockholders.
- Prepare written reports based on data analyses.
- Make computer programs, algorithms, and mathematical equations to sort and analyze large data sets.
- Explain complex data sets to people with non-technical backgrounds.
- Use relevant computer programs to analyze data sets and perform statistical analyses (such as R, MATLAB, SAS, and Python).
- Use database software programs, programming languages, and platforms to manage large datasets (such as SQL, HaDoop, Hive, and MapReduce).
- Use computer programs to make visualizations of data (such as Tableau, R, D3, and Microsoft Excel).
- Know how to use computers that run different operating systems (e.g., Unix and Windows).
- Teach others how to use computer programs to analyze data sets.
Companies That Hire Data Scientists
Explore what you might do on the job with one of these projects...
Do you have a specific question about a career as a Data Scientist that isn't answered on this page? Post your question on the Science Buddies Ask an Expert Forum.
- O*Net Online. (2016). National Center for O*Net Development. Retrieved July 1, 2017.
- Gualtieri, M. (2013, June 4). What is a Data Scientist? Forrester. Retrieved July 7, 2014.
- Lo, F. (n.d.). Big Data Salaries: An Inside Look DataJobs. Retrieved July 7, 2014.
- Chevron, YouTube. (2018, February 12). Day in the Life: Data Scientist. Retrieved April 12, 2019.
- The Wall Street Journal. (2013, December 11). Interview with Data Analyst at Game Operator DeNA. Retrieved April 12, 2019.
- Patel, M. (2012, April 23). Big Data Transforms Business: A Data Scientist Speaks EMC. Retrieved April 12, 2019.
- Central Intelligence Agency (CIA). (2014, July 10). A Day in the Life of a CIA Data Scientist. Retrieved July 16, 2014.
Explore Our Science Videos
BlueBot 4-in-1 Robotics Kit
Slippery Slopes - STEM activity
Gel Electrophoresis and Forensic Science: Biotechnology Science Fair Project