Data Science Careers
Data scientists gather and interpret information to solve complex problems like how to reach and influence target audiences, how to adapt business strategies to emerging trends, and even how to fight modern slavery. These professionals use a combination of machine learning, statistics, programming, research skills, business knowledge, and mathematics to collect and analyze data.
Data scientists may advise political campaigns, businesses, governments, and other organizations that need to use information to meet their goals. As a result, graduates with a data science degree can pursue many different careers.
www.bestcolleges.com is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.
Ready to start your journey?
This page explores careers for data science majors, including salary potential, educational paths, advancement opportunities, and professional resources.
Why Pursue a Career in Data Science?
Referred to as the "sexiest job of the 21st century" by Harvard Business Review, data scientists use big data to identify trends, devise solid strategies for their clients, and assist in high-level decision-making. These careers attract individuals who want to make sense of the world by gathering and interpreting sets of information.
Bright, inquisitive individuals with a knack for solving problems often excel in data science programs. These learners often ask "how" and "why" things are the way they are. They then seek information to answer those questions.
Data science degrees feature coursework in computer science, mathematics, statistics, research methods, and business fundamentals. Although these programs are interdisciplinary, they tend to focus more on STEM topics. Learners with an interest in many different STEM areas may find data science degrees particularly intriguing.
Data Science Career Outlook
Data science careers provide high salaries and competitive job opportunities in a variety of industries. Between 2019 and 2029, the Bureau of Labor Statistics (BLS) projects strong growth rates for many data science careers, including market research analysts (18%), computer and information research scientists (15%), computer systems analysts (7%), and operations research analysts (25%).
Careers in data science often offer salaries that are much higher than the national average. As the table below indicates, data scientists can earn well over $100,000 per year, depending on their experience. According to the BLS, data scientists and professionals in mathematical science occupations earn a mean annual salary of $100,560. Exact salaries vary depending on a worker's employer, industry, location, education, and experience.
|Machine Learning Engineer||$93,580||$110,890||$136,250||$148,370|
Skills Gained With a Data Science Degree
Data science programs focus on research and analysis, teaching students to convert raw data into useful, actionable information. The list below outlines five major skills gained through a data science curriculum.
The cornerstone of data science, statistical analysis is the process of evaluating data to find underlying meaning. Students learn to calculate cohesiveness in data, summarize findings, and present information as data sets. At advanced levels, learners delve into Bayesian analysis, linear regression, and data classification.
Students learn how to ensure the accuracy and integrity of vast data sets. This skill focuses on asking the right questions and developing strategies to test the validity of research findings, thereby avoiding misleading or improper conclusions. Learners also validate data sources using digital object identifiers and other industry standards.
Data Mining and Visualization
Data mining is the process of identifying new insights and patterns in digital information. Students learn to build predictive models using data inputs to develop practical applications, such as online marketing campaigns. Due to the sheer size of many data sources, learners also develop methods for representing information and trends visually.
During data science programs, students delve into advanced computer algorithms and principal component analysis. They learn to use training data to predict potential relationships. Learners also examine overtraining in machine learning applications and cross-validation schemes.
Information Ethics and Privacy
Because data science tools are not morally neutral, students must learn to predict the impact of their work and take subsequent actions responsibly. Information ethics covers topics like intellectual copyright, data security, artificial intelligence biases, anonymization, and corporate social responsibility. Students also explore the differences between confidentiality and privacy.
Related Programs That Might Interest You
Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.
Data Science Career Paths
A popular concentration for business-oriented students, finance covers investments and asset valuation. Students take courses like managerial accounting, financial markets and institutions, and supply chain management. They also explore international business finance, learning the processes and regulations surrounding global imports and exports. This concentration prepares graduates for careers in manufacturing, forensic accounting, and financial analysis.
A computer engineering concentration trains students in electrical engineering and computer science. Students learn to design hardware, operate networks and communication systems, and develop smart technologies. They also delve into machine intelligence, embedded systems, and cybersecurity. Graduates can pursue careers as information systems managers and network architects.
Data science students who pursue a business analytics concentration develop cross-functional skills in collecting, managing, and describing data sets. They form inferences and make predictions that support business decisions at the managerial and organizational levels. Coursework includes big data analytics, optimization theory, and online business model innovation. Related data science career paths include business intelligence officer and digital/social media marketer.
A healthcare administration concentration teaches students to coordinate medical services and improve the accessibility, affordability, and quality of patient care. Individuals who pursue specialized training in healthcare administration can access some of the fastest-growing careers in the U.S. They can work for hospitals, insurance companies, government agencies, and pharmaceutical manufacturers.
By pursuing an environmental sustainability concentration, data science students learn to apply statistical analysis and research to combat climate change and help companies reduce manufacturing emissions and waste. They may also develop sustainable energy sources and advocate for environmentally responsible government actions. Coursework includes public policy analysis, clean technologies and supply chains, and corporate sustainability strategies.
How to Start Your Career in Data Science
Data science professionals typically need at least a bachelor's degree before they can pursue a career in the field. However, many jobs require more advanced degrees. While operations research analysts and market research analysts typically need a bachelor's degree, political analysts and computer and information research scientists typically need a master's degree or doctorate.
College students develop the skills and knowledge necessary for success in data science careers. Data scientists need formal education in mathematics, statistics, programming, data mining, and research methods to successfully land jobs and complete tasks.
Students and recent graduates often pursue internships, fellowships, and other professional development opportunities to start building their credentials. You can find more information about these options later in this guide.
Associate Degree in Data Science
Associate programs provide a well-rounded general education in several areas, including mathematics, science, and communication. At the associate level, learners may take a short sequence of foundational classes in their chosen topic. Alternatively, students may earn a more general associate of science or associate of arts.
Although data science programs are uncommon at the associate level, learners may benefit from pursuing an associate degree in a STEM field such as mathematics, computer science, or information technology. A few colleges offer associate degrees in analytics.
Although aspiring data scientists typically continue on to a bachelor's program, some entry-level jobs are available to individuals with an associate degree in data science, analytics, or a related field.
What Can You Do With an Associate in Data Science?
Computer Support Specialist
Also known as technical support specialists or help desk technicians, these professionals help users troubleshoot problems with their technology. Some specialists focus on computer networks and internet systems, while others specialize in electronic devices like laptops and phones. Associate degrees in data science and related fields often feature multiple courses in IT, which can prepare learners for this career.
These professionals design and build websites. Front-end developers design the overall appearance and user interface of a website, while back-end developers focus more on technical construction and maintenance. Students with an associate in data science or a related field often have relevant skills in programming and database management.
Statistical assistants perform a variety of tasks related to collecting, analyzing, and presenting data. They typically support statisticians, accountants, data scientists, and other professionals who interpret information. Duties may include writing reports, completing administrative tasks, overseeing data entry, giving presentations, and conducting surveys.
Bachelor's Degree in Data Science
Many careers in data science require a bachelor's degree. Operations research analysts, for example, typically need to hold a bachelor's degree in data science, business intelligence, or a related field.
Data science programs feature an interdisciplinary curriculum with classes in computer science, statistics, communication, and business.
Although data science bachelor's programs are becoming more popular and widespread, they are not yet common. Some schools, such as Thomas Edison State University, offer data science as a major, while others offer data science as a minor or a concentration option. Learners interested in data science careers can also consider related bachelor's programs, such as database management, business intelligence, and management information systems.
Data science programs are more widely available at the graduate level, and many students interested in the field go on to earn a master's degree after their bachelor's.
What Can You Do With a Bachelor's in Data Science?
Operations Research Analyst
Operations research analysts identify and solve problems in diverse fields, including healthcare, business, and logistics. They gather quantitative data (e.g., statistics) and qualitative data (e.g., employee feedback). Operations research analysts then use this data to help organizations operate more effectively and efficiently. They explain their findings to upper management to help with organizational decision-making.
Market Research Analyst
Market research analysts study market conditions, trends, and consumer information in order to identify the best way to sell and advertise a product or service. They also research competitors and devise strategies to maximize sales. Learners with a data science degree are prepared to interpret economic statistics, collect information through surveys and opinion polls, and forecast trends based on historical data.
Data analysts convert raw information into a format that employees, decision-makers, and organizations can understand. They identify patterns in data sets, collect information through primary and secondary research, and forecast trends. Data analysts often create charts, graphs, and other visual elements that help audiences understand information. They may make recommendations and suggestions based on their findings.
Data engineers use technology to store, retrieve, and process large data sets. They must be highly proficient in coding languages, database management techniques, and algorithms. Data engineers aggregate data, document data storage and retrieval processes, and develop tools for their clients. These professionals typically work in teams alongside other IT experts.
Sources: BLS and PayScale
Master's Degree in Data Science
Master's programs in data science help students develop specialized skill sets in machine learning, software engineering, data mining, applied statistics, risk analysis, analytics, and data management. Graduates know how to interpret large, complex data sets and apply their findings to practical applications.
The best master's degrees in data science prepare learners for roles in various industries, including politics, business, and scientific research and development. Graduates may work for one particular organization or serve multiple clients as consultants.
Learners interested in a particular industry may seek out a university with a concentration option or course sequence in that area. For instance, some programs offer classes in health informatics, financial technology, and/or security.
These master's programs often include a capstone project or practicum to help learners develop real-world experience. Some programs also include a professional experience or internship component.
Regardless of their concentration, graduates with a data science master's degree can go on to assist with decision-making at the highest levels of business and government.
What Can You Do With a Master's in Data Science?
Data scientists are experts at gathering and analyzing data and applying their findings to solve problems. They often deal with large, complex data sets like economic forecasts, research findings, and machine learning data. These professionals formulate relevant questions and identify data sources to answer those questions. They then suggest strategies and present their findings to key decision-makers.
Machine Learning Engineer
These engineers develop artificial intelligence, big data tools, and other technologies used to produce and interpret information. They are skilled in software development and focus on the technological aspects of data science. They ensure that software programs can effectively handle, process, and analyze large volumes of information.
A specialized form of political science, political analysis uses data to identify trends related to issues like domestic politics, the global economy, national security threats, and international relations. Political analysts collect and interpret economic, social, and political data. Political campaigns often hire data science professionals to identify and influence potential voters.
Business Intelligence Manager
Business intelligence managers help other managers and top executives improve business operations. They examine ways to boost profitability, improve employee performance, and develop and market in-demand products and services. These managers also use complex data sets to solve organizational problems. Business intelligence managers use a wide array of data sources to inform their recommendations, including economic forecasts, statistics, machine learning, and consumer feedback.
Sources: BLS and PayScale
Doctoral Degree in Data Science
Doctoral programs in data science typically prepare learners for careers in research and academia. These programs feature rigorous, high-level coursework in deep learning, mathematical tools for data science, computational cognitive modeling, optimization, and natural language processing.
Earning a doctoral degree is particularly useful for data science professionals. Since data scientists conduct advanced research and analytics as part of their job, they can greatly benefit from pursuing the highest levels of education and engaging in cutting-edge research.
Doctoral programs typically feature a series of courses, followed by several years of work on an original research project and dissertation. By obtaining a doctorate, learners become experts in their chosen field. They may pioneer data science techniques and technologies that other data scientists will use in their daily work.
Graduates are able to use complex data sets to create innovative solutions to cybersecurity threats, healthcare challenges, societal problems, economic troubles, and other major issues.
What Can You Do With a Doctorate in Data Science?
Computer and Information Research Scientist
These researchers develop new technologies and apply existing technologies in innovative ways. They solve complex technological issues and design experiments. Information research scientists may process large data sets in new ways; for instance, they may create artificial intelligence "bots" that can mimic and predict human behavior. These professionals may advance data science through innovations in machine learning, programming languages, software, database management, and algorithms. Professionals with a doctorate can set themselves apart from the competition when applying for these positions.
Professor, Data Science
Postsecondary teachers educate college students in their field of expertise. They create lesson plans, mentor students, grade assignments, and deliver lectures. Many professors also conduct their own research — often with the help of student assistants — and publish their findings in academic journals. Individuals with a doctorate in data science may teach diverse topics, including analytics, statistics, business intelligence, and research design.
Top executives — including chief executive officers, chief information officers, and chief operating officers — oversee the highest levels of their organization. Businesses, government agencies, and nonprofits increasingly use big data to help in decision-making and strategic planning. Since data science professionals need highly specialized and high-level skills, a doctorate can help professionals advance into leadership roles.
How to Advance Your Career in Data Science
The two most prominent ways to advance your career in data science are to complete additional education and obtain relevant professional experience. Since data science careers require advanced skills in specialized fields like machine learning, data mining, and statistics, these professionals strongly benefit from earning a graduate degree.
Internships, fellowships, and other professional development opportunities help students and recent graduates gain practical field experience. Students may also seek out jobs as research assistants to help offset the cost of their education while gaining valuable skills. Additionally, aspiring scientists may consider postdoctoral research.
Individuals can also consider obtaining certifications, pursuing nondegree educational opportunities, and joining professional organizations.
Certifications and/or Licensure
Certifications are typically optional credentials issued by professional organizations, while licenses are typically mandatory permits issued by government agencies. Most data science careers do not require professional licenses.
Certifications demonstrate concrete skills and expertise. As such, they are attractive to employers. There are several professional certifications relevant to the field of data science, including the certified analytics professional, certified professional data engineer, certified data scientist, certified advanced analytics professional, and senior data scientist credentials, which are offered by various organizations.
Many big data companies also offer their own certification programs. Examples include Google's professional data engineer certification, Dell's data science associate certification, and Microsoft's Azure data scientist associate credential.
Professionals may also seek out certifications in specialized subfields of data science. For example, the American Health Information Management Association offers the certified health data analyst credential and the International Institute of Business Analysis offers the business data analytics certificate. These specialized credentials may be especially useful for learners who complete a degree in data science that does not offer a concentration in their area of interest.
Although degrees are the most common form of education for data science professionals, many schools and organizations offer nondegree continuing education opportunities. These options typically feature a short course or sequence of courses. As such, learners can complete them in a much shorter amount of time than a traditional program.
Online learning platforms like Coursera and edX offer several free or inexpensive programs. For example, IBM's data science professional certificate, available through Coursera, imparts skills in SQL, Python, business intelligence, and statistical analysis.
Nondegree certificate programs at colleges and universities allow learners to access the institution's academic and professional resources without committing to a full degree. Many schools allow learners to apply certificate credits toward a degree later on, if they so choose. Like certifications, certificates can be particularly useful for helping individuals gain experience and credentials in specialized subfields of data science.
There are myriad options available. For example, Harvard University offers a data science graduate certificate, Massachusetts Institute of Technology offers a professional certificate in machine learning and artificial intelligence, Pennsylvania State University offers a certificate in business analytics, and Arizona State University offers an advanced analytics in higher education graduate certificate.
Outside of educational opportunities, students and professionals can advance their career in data science by networking, joining professional organizations, participating in conferences, and conducting original research.
Professional organizations host events, disseminate the latest research in the field, maintain job boards, and advocate for their members. Many professional organizations provide scholarships and fellowships to students and postdocs. They may also deliver continuing education opportunities, such as certificate programs and webinars. These opportunities are particularly important because many professionals need to complete continuing education credits on a regular basis to renew their certifications.
Additionally, since data science is a rapidly evolving field, professionals must stay abreast of the latest research and technologies. Reading industry magazines, academic journals, and other publications can benefit students and experts alike.
Where Can You Work as a Data Science Professional?
Computer Systems Design and Related Services
Data scientists in this industry design and create new innovations in technology, and they may also develop hardware or software. Individuals with a data science degree often work on database management systems, machine learning, artificial intelligence, and data visualization.
Average Salary: $106,440
Management of Companies and Enterprises
Management analysts and other data science professionals in this field use data to help managers and executives improve their organization's operations. They may collect information through surveys, questionnaires, consumer databases, sales data, and other sources. Data scientists, consultants, and analysts use this data to suggest solutions to problems, identify key trends and insights, and help managers make strategic decisions.
Average Salary: $102,030
Management, Scientific, and Technical Consulting Services
Experienced and well-educated data science professionals may provide consulting services to other organizations that need assistance with gathering data, solving complex problems, and optimizing their strategies. These consultants may also advise scientific and technical organizations on new technologies and techniques in data science.
Average Salary: $99,600
Scientific Research and Development Services
Data science is a widely applicable field that can drive innovation in several areas, including healthcare, social science, technology, and psychology. Data scientists working in research and development help identify societal needs; analyze publicly available data sets; conduct original research; interpret results from experiments; and assess satisfaction with different innovations, products, and services.
Average Salary: $106,720
Colleges, Universities, and Professional Schools
Postsecondary institutions often house research institutes and cutting-edge labs. These facilities help colleges and universities attract funding and top students. Data science professionals may work exclusively in these labs, but they may also teach courses in their field of expertise.
Average Salary: $64,650
Data scientists and professionals in other mathematical occupations enjoy the best job prospects in political, business, and technological hubs like New York City, Chicago, San Francisco, and the District of Columbia. California, Illinois, Texas, New York, and Ohio feature the highest employment levels for data scientists, while New York, North Carolina, and Maryland offer the highest wages for these professionals.
Resources for Data Science Majors
Because data science often relies on new and emerging technologies, students and professionals must stay up-to-date with the latest developments. Professional organizations disseminate news and updates in the field, and publications provide readers with in-depth analyses and research.
Students and professionals can also access open courseware to bolster their credentials and learn about new areas of data science.
American Statistical Association: Founded in 1839, the ASA includes industry, government, and academic members from over 90 countries. The association funds policy initiatives and research projects. Members connect through national and international networking events, including the Women in Statistics and Data Science Conference. Additional resources include internships, fellowships, grants, student award competitions, and a job board.
Association of Data Scientists: ADaSci serves more than 1,000 members who connect through local chapter meetings and national conventions. They can also share knowledge and receive guidance through an integrative online forum. The association delivers continuing education courses, job listings, mentoring programs, and internship opportunities. Professionals can also pursue ADaSci's chartered data scientist credential.
Computing Technology Industry Association: CompTIA helps technology professionals, educators, and students build their professional networks. The association conducts skill development and leadership programs and hosts local chapter meetings and national events. Data science professionals can also pursue certification in areas like cloud essentials, project management, IT fundamentals, and information security.
International Institute of Business Analysis: As a nonprofit professional organization, IIBA maintains global standards for the emerging field of business intelligence and analytics. Members can access career services and a knowledge center that includes business analysis competency models and best practices. They can also take advantage of certification programs in areas like core business data and agile analysis.
Research Data Alliance: Founded in 2013 as a community-driven initiative, RDA promotes social networks and technology services that enable open data sharing and reuse. Members collaborate in special-interest working groups and regional meetings. They benefit from webinars and career guidance. RDA also operates student groups and early career programs in the U.S. and Europe.
Machine Learning - Stanford University: Machine learning involves "teaching" software to act and make decisions without explicit instructions. Machine learning technology has helped develop self-driving cars, artificial intelligence, and speech recognition. This class covers logistic regression, system design, data mining, and pattern recognition.
What Is Data Science? - IBM: This introductory course examines the fundamentals of data science, including the practical applications and uses of data science. Lecturers give a basic outline of the skills that data scientists need to succeed, including algorithms, programming, and regression analysis.
Customer Analytics - University of Pennsylvania: Modern businesses collect massive amounts of data through credit card transactions, online shopping carts, user reviews, and loyalty programs. Businesses can then use this information to better drive strategic decision-making and marketing. Data analysts can also predict how potential customers will act, which can help them reach new audiences and retain their consumer base.
The Data Scientist's Toolbox - Johns Hopkins University: This course provides an overview of prevalent tools in data science, including R and GitHub. Participants also learn about experimental design and big data.
Algorithms for DNA Sequencing - Johns Hopkins University: This course examines how to use data science to analyze and sequence DNA. Topics include bioinformatics, algorithms, and Python. Participants analyze real genomes and use data analysis techniques to solve problems such as matching.
Analytics Magazine: Pustronglished strongy the Institute for Operations Research and the Management Sciences, Analytics Magazine features news, interviews, analysis, and stronglog posts on data science sustrongjects. Major topics include healthcare, artificial intelligence, strongusiness analytics, strongig data, and predictive analytics. Authors also examine ethical issues, such as strongias in algorithms.
Data Science Journal: This open-access, peer-reviewed journal focuses on the use and dissemination of research data. The journal includes articles on STEM, the arts, and humanities. The journal examines how to use data in the most efficient, effective, and ethical ways possistrongle across all disciplines.
CHANCE: This magazine explores how to use data and statistics to improve outcomes in education, science, and society. Although the magazine is dedicated to statistics, it emphasises innovative, entertaining case studies and applications rather than technical topics. By doing so, CHANCE hopes to reach a wider audience.
Data Mining and Knowledge Discovery: A technical journal for data science professionals, this pustronglication explores techniques, methods, algorithms, and applications for data mining. Prior articles have examined topics like online social networks, space exploration, and decision-making.
Journal of AHIMA: The premier pustronglication of the American Health Information Management Association, this journal features articles on data science and informatics within the healthcare sector. The journal is free for AHIMA memstrongers.
International Journal of Business Intelligence Research: This peer-reviewed journal aims to improve the role, scope, and impact of strongusiness intelligence on decision-making. Topics include data mining for strongusinesses, strongusiness intelligence in small and medium enterprises, organizational culture, and knowledge management. The journal features case studies.
IEEE Transactions on Knowledge and Data Engineering: Pustronglished strongy the Institute of Electrical and Electronics Engineers — the leading professional organization in engineering and computer science — this journal explores data science in artificial intelligence, computer science, electrical engineering, and other technological fields. The journal emphasizes datastrongase systems.
Frequently Asked Questions
Is data science a good degree?
Yes. Data science is an interdisciplinary degree that integrates STEM topics, business, and communication. Graduates can find work in lucrative fields that enjoy high job growth rates. These professionals can apply their skills in diverse industries, such as technology, social science, and politics.
Are data science majors in demand?
Yes. The BLS projects significant job growth in many different data science careers between 2019 and 2029, including market research analyst (18%), computer and information research scientist (15%), computer systems analyst (7%), and operations research analyst (25%). These projected growth rates are much higher than the national average of about 4%.
What kind of jobs can you get with a data science degree?
Many different industries — including business, scientific research and development, and healthcare — use big data to inform decisions. As a result, graduates can pursue myriad careers with a data science degree. Common job titles include analyst, data scientist, research scientist, and consultant.
What data science jobs pay the most?
Although most data science careers offer high salaries, data engineers ($92,450), machine learning engineers ($111,630), political analysts ($122,220), business intelligence managers ($101,770), and computer and information research scientists ($122,840) earn particularly lucrative wages.
Read More About Data Science on BestColleges
Data Engineering Career Track
Data Science Bootcamp Spotlight
BestColleges.com is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.
Compare your school options.
View the most relevant school for your interests and compare them by tuition, programs, acceptance rate, and other factors important to find your college home.