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Are you interested in pursuing an online data science degree? This field of study combines mathematics, computer science, and business strategy into a single curriculum. The major is ideal for individuals who want to work in business but have a strong interest in and aptitude for information technology.

Overall, the outlook for data science professionals is very promising. According to the Bureau of Labor and Statistics, employment for computer and information technology occupations is projected to grow by 13% from 2020 to 2030. This is faster than the national average for all professions and will result in about 667,600 new job openings each year.

The data science field, in particular, is likely to see increased demand for qualified workers. As more and more businesses seek to make decisions based on reliable information analysis and projections, expertise in statistics, computer science, engineering, and management will become highly valuable. As a result, data science professionals should have little difficulty finding employment in most industries.

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Why Get a Data Science Degree?

Data science generally consists of the collection, analysis, shaping, storing, and management of relevant information that can be utilized by companies and organizations when making important business decisions. Professionals in this field allow stakeholders to make data-driven judgements, which increase the probability of success in the future. Almost every interaction with technology results in usable data. When used appropriately, this information can help retailers influence purchasing habits, as well as improve individual health through wearable fitness trackers and medical diagnostic capabilities worldwide.

Earning a degree in data science can lead to a wide variety of coveted employment opportunities.

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Data Science Degree Levels

While one of the most common careers for graduates is data scientist, other jobs options include:

  • Machine Learning Engineer
  • Applications Architect
  • Statistician
  • Data Analyst
  • Business Intelligence Developer
  • Data Engineer
  • Infrastructure Architect
  • Web Development and Analytics

The career field is exceptionally flexible, with positions available in almost every industry, as well as with many government organizations. Additionally, because the knowledge and skills utilized is widely coveted and applicable in business, graduates are typically highly sought-after candidates by prospective employers. Professionals are often valued in the workplace and enjoy decent job satisfaction.

Other benefits to earning this type of degree include lucrative pay potential, exposure to cutting-edge software and technology, diverse career growth options, and the opportunity to make significant differences at a global level.

Data science degrees are available at most higher education levels. Students can choose from a wide variety of relevant associate, bachelor’s, master’s, and doctoral programs. It’s important to note, however, that associate degrees in this field are a relatively new addition. Most colleges and universities offer this major solely to those seeking bachelor’s and master’s degrees.

Online Associate Degree in Data Science

The majority of online associate degrees in data science consist of about 60 credit hours of coursework that takes full-time students approximately two years to complete. Those attending classes part-time should expect a longer timeframe. While program specifics vary, most are designed to introduce essential topics in the field such as data mining, data visualizing, statistics, algorithms, and data acquisition.

Prospective students should expect to meet several admissions requirements when applying to associate degree programs in data science.

Every institution has different expectations, but most require candidates to have the following:

  • High School Diploma or Equivalent
  • Minimum Grade Point Average (GPA)
  • Standardized Test Scores
  • Letters of Recommendation
  • Personal Essay for Admissions Packet

Graduates may be prepared to apply for some entry-level employment opportunities in the field, but those with bachelor’s and master’s degrees will often receive preference. Many who earn this type of degree go on to enroll in further education. In general, associate degrees from properly accredited institutions will satisfy about half of the course requirements for bachelor’s degrees offered by traditional, four-year colleges and universities.

Associate Degree Coursework (Classes and Courses)

Every curriculum is different, but required courses at this level may include:

  • Introduction to Analytics
  • Introduction to Programming
  • Introduction of Databases
  • Data Structures for Analytics
  • Applied Predictive Modeling
  • Analytical Methods
  • Probability and Statistics
  • Data Visualization
  • Database Management

Career Options

Those who choose to pursue employment after graduation will have opportunities in many different industries.

Some of the most attainable jobs with associate degrees include:

  • Business Analyst
  • Operational Analyst
  • Data Scientist
  • Operations Analyst

Online Bachelor’s Degree in Data Science

Most online bachelor’s degrees in data science consist of 120 credit hours of coursework that takes full-time students approximately four years to complete. This timeframe is extended for those enrolled part-time. Conversely, those with prior college credit, especially from associate degrees in related subject areas, may require significantly less time to complete graduation requirements.

This type of degree is generally meant to provide students with all of the technical skills necessary to be successful in the field. As a result, those enrolled can typically expect instruction related to data analysis and the creation of actionable assessments. Key topics are likely to include programming, statistics, math, and social science.

Additionally, many colleges and universities allow students to select concentrations, or specialties, in relevant disciplines. Once designated, concentrations help tailor degree plans to ensure instruction addresses subjects of greatest interest. Common examples are neuroscience, robotics, social policy, and law.

Prospective students should expect to meet several admissions requirements when applying to bachelor’s degree programs in data science.

Every institution has different expectations, but most require candidates to have the following:

  • High School Diploma or Equivalent
  • Minimum Grade Point Average (GPA)
  • Standardized Test Scores
  • Letters of Recommendation
  • Personal Essay for Admissions Packet

Additionally, many colleges and universities set other prerequisites and entrance requirements. Data science departments may stipulate that candidates need to achieve certain grades in a number of courses related to computer science and/or statistics.

Data Science Bachelors Coursework (Classes and Courses)

Every curriculum is different, but required courses at this level may include:

  • Discrete Mathematics
  • Statistics and Probability Modeling
  • Structured and Unstructured Data
  • Object-Oriented Programming
  • Algorithms
  • Database Systems and SQL
  • Data Mining
  • Machine Learning

Career Options

Those who choose to pursue employment after graduation will have opportunities in many different industries.

Some of the most attainable jobs with bachelor’s degrees include:

  • Data Scientist
  • Data Mining Engineer
  • Data Analyst
  • Data Manager
  • Data Architect

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Online Master’s Degree in Data Science

Most master’s degree in data science consist of 60 credit hours of coursework that takes full-time students approximately two years to complete. Those enrolled part-time will require additional time to meet graduation requirements, however. Most programs are designed to help students develop a broad range of skills that can be applied to almost any tech-related career. Instruction and coursework build upon subjects covered at the undergraduate level, providing a more in-depth assessment of key topics such as applied statistics, database systems, data preparation, and practical machine learning.

Again, many institutions encourage students to designate concentrations to further specialize coursework. Some common options include data acquisition, data management, data security, business applications, computational intelligence, and big data informatics. Additionally, those enrolled will often need to complete capstone projects prior to graduating.

While a bachelor’s degree is generally considered sufficient for many entry-level data science jobs, earning a master’s degree can lead to more attractive and lucrative career opportunities. In many cases, a graduate education is essential for promotion into leadership and management positions.

Prospective students should expect to meet several admissions requirements when applying to master’s degree programs in data science.

Every institution has different expectations, but most require candidates to have the following:

  • Bachelor’s Degree
  • Official Transcripts from an Accredited Institution
  • Minimum Grade Point Average (GPA)
  • Minimum Graduate Record Examinations (GRE) Scores
  • Letters of Recommendation
  • Personal Statement and/or Resume

Some colleges and universities also require, or give preference to candidates with, prior professional experience in the data science field.

Data Science Masters Coursework (Classes and Courses)

Every curriculum is different, but required courses at this level may include:

  • Data Visualization for Data Scientists
  • Human-Centered Data Science
  • Data Management for Data Science
  • Software Design for Data Science
  • Scalable Data Systems and Algorithms
  • Applied Statistics and Experimental Design
  • Statistical Machine Learning for Data Scientists

Career Options

Those who choose to pursue employment after graduation will have opportunities in many different industries.

Some of the most attainable jobs with master’s degrees include:

  • Data Scientist
  • Statistician
  • Business Intelligence Analyst
  • Big Data Engineer
  • Machine Learning Engineer

Data Science MBA Coursework (Classes and Courses)

A master’s in business administration (MBA) is one of the most popular degrees in the United States. Like most graduate programs, MBAs typically consist of 60 credit hours of coursework that takes full-time students approximately two years to complete. Those attending classes part-time should anticipate graduating one to two years later. It may also be possible to find accelerated programs offered by some institutions. Topics covered often relate more to business and finance, but the knowledge and skills gained can prove invaluable in almost any workplace and students can choose to earn an MBA with a specialty in another subject, such as data science.

Earning this type of degree can drastically increase employment opportunities, salary potential, marketability, advancement options, and job security. It is particularly well-suited for prospective students interested in establishing their own businesses.

Prospective students should expect to meet several admissions requirements when applying to MBA degree programs.

Every institution has different expectations, but most require candidates to have the following:

  • Bachelor’s Degree
  • Official Transcripts from an Accredited Institution
  • Minimum Grade Point Average (GPA)
  • Minimum Graduate Management Admission Test (GMAT) Scores
  • Letters of Recommendation
  • Personal Statement and/or Resume

It’s also not unheard of for some colleges and universities to require MBA candidates have a minimum number of years of professional experience prior to enrolling. Unpaid internships can satisfy this requirement in some cases.

Data Science MBA Coursework (Classes and Courses)

Every curriculum is different, but required courses at this level may include:

  • Finance
  • Marketing
  • Accounting
  • Leadership and Ethics
  • Teamwork and Organizational Behavior
  • Economic Statistics and Operations
  • Business Accountability and Ethics
  • Human Resources
  • Microeconomics
  • Business Strategy
  • Production
  • Information Systems

Career Options

Those who choose to pursue employment after graduation will have opportunities in many different industries.

Some of the most attainable jobs with MBAs include:

  • Financial Analyst
  • Accounting Manager
  • Data Analyst
  • Investment Banker
  • Marketing Manager

Doctoral/PhD Degree in Data Science

PhDs in data science generally consist of between 70 and 90 credit hours that takes full-time students five to seven years to complete. Every curriculum is different, but many programs are meant to provide rigorous instruction and training for aspiring data science researchers. Classes generally help students develop skills related to gathering and organizing fundamental research insights, as well as translating information to meet high accuracy standards. Assignments are heavily research oriented and require a lot of time to complete.

It's worth noting that degrees of this kind are only required for top professions in the field. As graduates have advanced knowledge of programming, statistics, data analysis, and machine learning, they are best qualified for jobs as researchers and scientists.

Prospective students should expect to meet several admissions requirements when applying to PhD in data science programs.

Every institution has different expectations, but most require candidates to have the following:

  • Bachelor’s or Master’s Degree
  • Official Transcripts from an Accredited Institution
  • Minimum Grade Point Average (GPA)
  • Minimum Graduate Record Examinations (GRE) Scores
  • Letters of Recommendation
  • Personal Statement and/or Resume

It’s also relatively common for colleges and universities to designate certain course prerequisites. Some examples typical of the field are linear algebra, multivariate calculus, computer programming, statistics theory, and probability theory.

Common PhD Coursework (Classes and Courses)

Every curriculum is different, but required courses at this level may include:

  • Algorithm Design, Analysis, and Implementation
  • Computational Methods in Analysis
  • Informatics Research Design
  • Visualization Design, Analysis, and Evaluation
  • Applied Cloud Computing for Data-Intensive Sciences

Career Options

Those who choose to pursue employment after graduation will have opportunities in many different industries.

Some of the most attainable jobs with PhDs degrees include:

  • Senior Data Scientist
  • Applications Architect
  • Infrastructure Architect
  • Computer and Information Scientist
  • College Professor

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What is the Cost of a Data Science Education?

Deciding to enroll in an online data science degree program can be a significant financial investment. Major factors impacting the overall cost of higher education include degree level, school type, enrollment status, and student residency.

According to the National Center for Education Statistics (NCES), the average tuition and fees charged by public institutions for first-time, full-time undergraduate students at the associate degree level was $3,800 per academic year in 2020. The average for four-year public institutions was $9,400. Notably, private for-profit and nonprofit colleges and universities charged more, with average rates ranging from $15,700 to $18,600 for two-year institutions and $17,700 to $31,000 for traditional four-year institutions.

Prospective students should anticipate paying more in tuition and fees when applying to higher level degree programs. While this expense can seem daunting, those with master’s and doctorate degrees tend to receive a significant return on investment later. These types of programs often lead to the most prestigious and lucrative employment opportunities in the field.

Certificates/Licensures in Data Science

In addition to data science degrees, students and professionals can choose to pursue various certifications related to the field. These credentials allow for further skills development and help show mastery over certain types of knowledge. While not necessarily required for many professions, many potential employers give preference to candidates who possess one or more relevant certificates. Additional credentials may also lead to higher pay and more advancement opportunities.

Some of the top data science certifications available include:

  • Certified Analytics Professional (CAP)
  • Cloudera Data Platform Generalist Certification
  • Senior Data Scientist (SDS) via Data Science Council of America (DASCA)
  • Principal Data Scientist (PDS) via Data Science Council of America (DASCA)
  • IBM Data Science Professional Certificate
  • Microsoft Certified in Azure AI Fundamentals
  • Open Certified Data Scientist (CDS)
  • SAS Certified Data Scientist
  • SAS Certified AI and Machine Learning Professional

It’s important to realize that most certifications require some amount of continuing education. Those who are certified will need to complete yearly training in order to keep credentials active. Qualifying courses are often available through academic institutions, professional organizations, and field-specific conventions.

Online vs. Traditional Options

Data science degrees are available on campus and online. While many students choose to enroll in a traditional, four-year institutions and complete their coursework in person, remote learning is quickly gaining traction. This is particularly true for prospective students who already work full-time or have restrictive responsibilities at home.

A major benefit associated with online data science programs is the availability of flexible scheduling options. This makes taking classes significantly easier for people studying part-time. The ability to join lectures and complete coursework remotely also means students can participate from anywhere in the world with internet access. Additionally, some colleges and universities charge lower tuition and fees for their online programs.

Prospective students should be aware of potential drawbacks presented by this type of education as well, however. The primary problem with an online data science degree programs is the lack of fact-to-face interaction with instructors and peers. Networking is an integral part of a successful career in the field. This can also negatively impact learning for some students.

One option that helps to bridge the gap between these two extremes is the hybrid degree program. Some institutions create curriculums that are predominantly completed online but require students to visit the campus for short residencies each year. These in-person sessions are often function like workshops that allow participants to receive some instruction in person, meet with their cohorts, and network with current professionals in the field.

Ultimately, both on-campus and online degree programs are viable options for prospective students. In most cases, the decision depends entirely upon individual preference.

Professional Organizations

Data science students and professionals also have access to a number of associations, organizations, and societies relevant to their career. There are many benefits to joining one or more of these groups including professional development opportunities, related news updates, scholarship options, access to forums, and event discounts.

Some of the most prominent options include:

  • Association of Data Scientists (ADaSci)
    The Association of Data Scientists (ADaSci) is a global professional body comprised of data science and machine learning professionals. It is a leader in the development, dissemination, and implementation of knowledge, research, and analytical decision-making. ADaSci members have access many networking and professional development opportunities.
  • American Statistical Association (ASA)
    The American Statistical Association (ASA) is the world’s largest community of statisticians serving in industry, government, and academia. It works to promote the practice and profession of statistics. ASA members work to advance research, promote sound statistical practices, inform public policy, and improve overall human welfare.
  • Data Science Council of America (DASCA)
    The Data Science Council of America (DSCA) is a leader in data science certifications, accreditations, partnerships, and data science initiatives. DASCA certifications are powerful professional qualifications that validate readiness for career success.
  • Institute for Operations Research and the Management Sciences (INFORMS)
    The Institute for Operations Research and the Management Sciences (INFORMS) is a leading international association for operations research and analytics professionals. Members work to solve problems, find solutions, and make a difference in how the world works. Joining INFORMS allows access to educational materials and provides opportunities to meet peers.

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What Should You Consider When Looking for a Data Science Program?

  • Majors Available
    It’s important to realize that there are many different majors that can lead to data science careers. Every college and university is unique, so prospective students must review the options available carefully. Those seeking specific concentrations and/or minors should also verify they are offered prior to enrolling. In most cases, pertinent program and curriculum information is available on institutional websites for easy review. It’s also helpful to research academic standards for all specific professions and/or certifications desired; some may require certain majors in order to apply.
  • Accreditation – Regional and Programmatic

    Prospective students should also pay attention to the accreditation status of colleges and universities. Institutions are not required to be accredited, but most reputable schools do to demonstrate their programs meet industry standards.

    Most colleges and universities are either regionally or nationally accredited as a whole. While both can offer degrees, regionally accredited institutions tend to provide more benefits. This is particularly true for students when transferring credits to other schools. Additionally, only regionally accrediting agencies are overseen by the U.S. Department of Education and the Council of Higher Education Accreditation (CHEA). Choosing to attend institutions lacking accreditation of any kind can lead to significant difficulties obtaining jobs.

    In addition to being accredited by a national or regional agency, academic departments can receive field-specific accreditation. Programmatic accreditation helps to ensure national standards are met. One prominent specialized accreditation organization for data science is the Computing Accreditation Commission of Accreditation Board for Engineering and Technology (ABET). The Data Science Council of America (DASCA) also offers accreditation for academic programs.

  • Program Length

    Prospective students can choose from online data science degrees of varying lengths. Options typically range between two and seven years, depending on the type of degree sought. Deciding to complete multiple programs will likely result in a larger time commitment.

    Program length is a significant factor for some prospective students. This is especially true for those interested in joining the workforce as soon as possible. Two-year programs often cost less and allow graduates to begin earning incomes faster. Earning degrees at higher levels, however, often results in larger starting salaries, which can sometimes offset the expense associated with attending school for longer.

    It’s worth noting that some colleges and universities offer accelerated or join programs. For example, those hoping to earn bachelor’s and master’s degrees in data science may find options that can be completed in as little as five years.

What Does a Data Science Professional Do?

Data scientists are responsible for analyzing various forms of data and developing actionable insights that can be utilized to improve accuracy, efficiency, and growth for businesses and organizations. These professionals use computer science, statistics, and mathematics to process and model information for future use. They often have to communicate complex ideas in simplified terms and with visual aids.

As previously mentioned, data science professionals can be found in almost any industry. The field selected will greatly impact the type of tasks performed most often. Generally, data scientists and managers establish methods for cleaning, processing, and storing information. They also identify data-analytics problems and offer possible solutions.

Frequently Asked Questions

How much do data scientists make?

Overall, the earning potential for data scientists is quite high. According to PayScale, their average base salary is $97,254 per year. This is significantly more than the mean yearly wage as reported by the United States Bureau of Labor Statistics. Additionally, pay tends to increase based on the amount of experience obtained. While those with only one to four years of experience make an average of $97,000, the average rises to $122,000 after 10 years. Those with 20 or more years of experience in the field make an average of $136,000 each year.

It's important to note that data science degrees can lead to a wide variety of employment opportunities. Income potential depends on the career chosen, with some options being much more lucrative than others. Some of the highest paying jobs in computer and information technology include computer and information research scientists, computer network architects, and quality assurance analysts.

Should I get a data science degree online?

There are many online data science degrees, with options available at every academic level. While there are certainly benefits associated with completing programs in traditional, on-campus settings, the flexibility offered by remote learning is significant. Both solutions result in viable and credible degrees. Notably, potential employers should not discriminate against online degrees so long as they are awarded by accredited colleges and universities.

Ultimately, students must decide for themselves whether or not to enroll in online data science degree programs. Important factors to consider include the type of academic experience desired, individual student learning preferences, and any potential cost differences.

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