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Data Scientist

Overview and Key Facts

Woman presenting data
Education
Education
Bachelor's degree
Median Pay
Median Pay
$112,590
Job Growth
Job Growth
33.50%
(Above US Average)
Jobs in 2034
Jobs in 2034
328,300

What Do They Do?

A data scientist could...

 
Video game players
Analyze video game players' online behavior to make a game more engaging and profitable.
Football players
Help a coach make the right game decision by providing them with data about which play has the most chance of succeeding.
petroleum distillation
Analyze a large set of data from petroleum distillation process to reduce energy consumption.
Man investigating on computer
Prevent a tragedy by looking for behavioral changes of a terrorist group to predict the time and place of a terrorist attack.
 
Video game players Football players petroleum distillation Man investigating on computer

Overview Listen to this section

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.
In this video, Chevron Data Scientist Alena Crivello describes her use of analytics to find solutions to complex problems.
https://www.youtube.com/watch?v=_Wk9T_G-u4o

Do You Have the Skills and Characteristics of a Data Scientist?


  1. Complex Problem Solving: ?
  2. Critical Thinking: ?
  3. Mathematics: ?
  4. Speaking: ?
  5. Active Listening: ?
More skills...

Core Tasks

Think about if you'd like the typical tasks a Data Scientist might do:
  • Analyze, manipulate, or process large sets of data using statistical software.
  • Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
  • Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
  • Clean and manipulate raw data using statistical software.
  • Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
  • Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
  • Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
  • Design surveys, opinion polls, or other instruments to collect data.
  • Identify business problems or management objectives that can be addressed through data analysis.
  • Identify relationships and trends or any factors that could affect the results of research.
  • Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
  • Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
  • Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
  • Recommend data-driven solutions to key stakeholders.
  • Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
  • Write new functions or applications in programming languages to conduct analyses.

Salary & Job Openings

Median Salary (Yearly Pay)

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Future Jobs

Blast-Off:

How Will Data Scientist Jobs Grow by 2034?
Projected % Growth
Rapid growth
(10% or higher)
Much faster than average
(7% - 10%)
Faster than average
(5% - 7%)
About as fast as average
(3% - 5%)
Slower than average
(0% - 3%)
Decreasing
Average of all U.S. jobs (5.3%)
Data Scientist jobs (33.50%)
Source: https://www.bls.gov/emp/tables/occupational-projections-and-characteristics.htm

Steps to Get There: Becoming a Data Scientist

High School Subjects to Study

Aim to study the yellow-shaded subjects.
Math
Algebra 1
Geometry
Algebra 2
Pre-Calculus
Calculus
Statistics
Science
Biology
Chemistry
Physics
Environmental Science
Physiology
Bio-Medical / Biotech
IT &
Engineering
Computer Science
Applied Technology
Drafting & CAD Training
Core
English
Foreign Language
Business
Psychology / Sociology

Career Pathway Listen to this section

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- Typical Degree Needed After High School Listen to this section

Degree
Post
high school credential
AA
Bachelor's
Master's
Doctoral or Professional Degree

Typical degree for an entry-level position: Bachelor's degree ? Types of Degrees and Credentials

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.

Companies That Hire Data Scientists

Try it Out with an Activity or Project

STEM Activities

STEM activities are fun hands-on explorations that usually take from 10 minutes to one hour and use readily available household or classroom materials while introducing you to STEM concepts common in this career.

Science Project Ideas

Project Ideas are in-depth STEM explorations that have a strong focus on controlling variables, taking accurate measurements, and analyzing data suitable for investigating the scientific method or the engineering design process.

On the Job

Role Models

Read this article to find out how Noelle Sio decided to be a data scientist and what she does as a lead member of the EMC Greenplum Data Science Dream Team.
I once had a Mathematics professor who likened solving difficult math problems to hitting your head against a wall until something useful came out. The same goes with the Big Data analytics process. 
Noelle Sio
Read this interview to find out how Julia Slige connected an interest in stars with a love of data.
I love learning the stories of real people... who...solve amazing technical problems. [One] is Annie Jump Cannon, an astronomer at Harvard (who suffered hearing loss) responsible for a huge part of the earliest work done classifying the stars in our Galaxy. 
Julia Slige, Ph.D.

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.

Work Environment

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.

More Information

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