Master's Degree in Data Science Degree Guide

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Updated October 4, 2023

Discover accredited online data science master's degree programs and learn what it will take to advance your career.

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What Is a Master's Degree in Data Science?

Data science is a branch of computer science that focuses on analyzing large data sets and using data through programming. Data scientists work to find solutions for businesses and organizations by communicating results and offering insight into strategic options.

Graduate data science programs focus on teaching current and cutting-edge data science skills and tools. Most master's in data science programs prepare learners to pursue new careers or fields. Some programs offer specialized courses or concentrations, which lead to specific careers within data science.

Data science ranks among the fastest-growing fields. The Bureau of Labor Statistics (BLS) projects jobs for computer and information research scientists -- a common career for data science master's graduates -- to grow 16% from 2018-2028. Computer and information research scientists also enjoy a median annual salary of $122,840.

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Should I Get a Master's in Data Science?

Students interested in pursuing a master's in data science should hold a bachelor's degree in computer science or a related field. Bachelor's degrees lead to many careers, but master's degrees in data science can lead to advancement in the field. The following list outlines several benefits of earning a master's degree in data science.

  • Higher Salary: Master's degrees regularly lead to higher-paying positions, and a master's in data science is no exception. Salaries vary by position, but some high-paying data science positions require master's degrees.
  • Specialization: Earning a master's in computer science lets professionals specialize their skills and knowledge.
  • New Skills Learned: Data science constantly evolves, so professionals must pursue education to keep up with changes in the field. A master's degree teaches the most current skills needed to succeed in data science.
  • New Career Outcomes: Master's degrees in data science qualify graduates for leadership positions in the field.
  • Professional Networking: Between faculty and other students, data science learners regularly interact with current and future data scientists, which can lead to professional opportunities.

Advice from a Data Science Graduate

Portrait of Stacey Schwarcz

Stacey Schwarcz

Stacey Schwarcz, a data scientist, is passionate about creating order from chaos. She started her career in the insurance industry before moving into transportation and, eventually, data science. She has worked with analytics throughout her career and always considered herself a data scientist, even before the term existed. Over the last few years, she completed an MS in data analytics and served as head of people analytics for a global financial institution.

She founded Ariel Analytics — a data analytics, business intelligence, and data strategy consultancy company — to guide companies in accessing the power of data and people to make better business decisions. Schwarcz also authored the Data Wilderness blog, an analytics blog, for non-analytics people.

I’ve always been on the more quantitative side of things. I have a BA in math, and most of the roles I’ve worked in throughout my career have had an analytics component to them. I decided to pursue a master’s in data science because I wanted to improve some of my technical skills with respect to coding in Python and R and learn more about data science methods like machine learning.
Since I already had an MS in transportation engineering from MIT, and I was working full time in a transportation analytics-related role at the time, I was interested in part-time remote programs where I could continue to work and didn’t have to commute to school in addition to work.
I think the credential itself was valuable, because it demonstrated that I was serious about being a data scientist. It helped me land the role of head of people analytics for a financial institution. I also strengthened my technical skills and increased my knowledge of data science methods.
It was challenging to find the right type of roles. Early on, many of the data science roles were in media and digital marketing. I wasn't as interested in these particular areas of data science, and I found that those areas also had a strong preference for hiring people with a software engineering background. I think the biggest surprise was the data science challenges, including how much time they expected people to spend on them and how they would make or break a hiring decision. Some companies send a data science challenge without even speaking to the candidate first. I successfully modified my search to focus on data science jobs in areas where I had a lot of subject matter expertise, like operations and workforce management.
What I discovered pretty quickly is that only a small part of data science is about modeling and the purely quantitative, with the possible exception of those developing new data science algorithms at the academic level. It’s always about the data, the people, and the processes that create that data. The biggest challenges are cultural, not technical. What I really enjoy is working with people at companies to help them improve their data, their process, and their analytics knowledge/skills.
I decided to start my own consulting firm, because I wanted more control over my schedule as well as the type of work that I did and who I worked with. I spent many years in the corporate world in analytics roles within operations. I saw a lot of challenges around data and analytics in business operations functions, like HR and finance. It takes a long time for these functions to adopt new methods and tools, which leads to a lot of unnecessary manual work. I felt that I could help more companies and their people as an outsider bringing a different perspective. I love meeting new people and solving new problems, and consulting provides opportunities to do this.
I gave a talk on launching a career in data science about a year ago and my recommendations are the same now as they were then:
  • Learning how to network effectively is the most important thing you can do for your career.
  • It can be very difficult to transition into data science if you don't have a computer science background. There is a lot of emphasis on the purely technical.
  • Research data science programs well before you make a decision; know what you need to get out of it and if the program will provide what you need.
  • The data science interview process is often grueling; approach your first few interviews as practice, and don't get discouraged.
  • Data science can mean a lot of different things. Companies have different needs. Figure out what it means to you and find your niche. Ask a lot of questions before you accept a job offer. Research the company and department culture. Is it right for you?

Admission Requirements for a Master's Degree in Data Science

A master's in data science builds on previous education and experience. At minimum, applicants need a bachelor's degree, ideally in a data science-related field. Some colleges and universities might require a minimum 3.0 GPA on previous college transcripts. Previous work experience is also a common requirement, though programs do not always require a specific amount of experience.

Testing requirements vary by program, but applicants may need to take the GRE, GMAT, or both. Most programs do not set specific score requirements, and some do not require any exams at all. International students must take the TOEFL.

Additional materials required for admission may include college transcripts, letters of recommendation, professional resumes, personal essays, and application fees.

What Can I Do With a Master's Degree in Data Science?

Master's in data science graduates typically follow two routes: entering the workforce or continuing their education. Both options provide long-term advantages. Master's degrees lead to high-paying positions not available to bachelor's graduates. Some doctoral programs and professional certifications require master's degrees, as well.

Career and Salary Outlook for Data Science Graduates

Data science professionals enjoy strong job prospects. Computer and information research scientists, for example, can expect a 16% job growth rate from 2018-2028. The top 10% of earners in this position make more than $189,780 per year.

Master's in data science graduates can also pursue careers as data architects. The BLS projects a 5% job growth rate for computer network architects from 2018-2028. This position pays a median salary of $112,690 per year, with the top 10% of earners making more than $168,390.

Graduates with master's degrees in data science enjoy dozens of potential career opportunities, including many not referenced in this resource. Anyone interested in a master's in data science should learn more about the careers available to graduates.

Data architects design, structure, and manage complex databases within businesses and organizations. As leaders within IT, data architects must collaborate and communicate with other IT workers and executives, which may require communicating complex computer science processes in an easy-to-understand manner.
Before embarking on large-scale, computer-related initiatives, business leaders look to computer and information research scientists to determine potential outcomes. These professionals conduct research either individually or in teams and communicate results to business leaders.
Applications architects create computer applications from start to finish, including designing, coding, and implementing applications. They also assist in larger architectural projects and consult other members involved in the application development. Applications architects usually hold master's degrees and at least eight years of professional IT experience.
Data engineers develop and translate computer algorithms and identify and organize trends in large data sets. Data engineers typically need at least a bachelor's degree and professional experience with data structures and algorithms. Data engineers often work in teams and regularly collaborate with others.
Data scientists interact with data to find opportunities for their businesses and organizations. Day-to-day work includes collecting large data sets, analyzing the data, and communicating data interpretations to non-IT executives. Data scientists often work in teams and occasionally start their own initiatives.
Master's Degree in Data Science Careers: Median Salaries by Experience
JOB TITLEENTRY LEVEL (0-12 MONTHS)EARLY CAREER (1-4 YEARS)MID-CAREER (5-9 YEARS)EXPERIENCED (10-19 YEARS)
Data Architect$69,000$87,000$104,000$123,000
Computer and Information Research Scientist$101,000$103,000$118,000$149,000
Applications ArchitectN/A$89,000$103,000$117,000
Data Engineer$77,000$87,000$104,000$118,000
Data Scientist$86,000$94,000$108,000$120,000
Source: PayScale

Continuing Education in Data Science

After earning a master's degree in data science, some learners choose to continue their education to further specialize their skills and advance their careers.

  • Ph.D. in Data Science: The highest level of education possible for a data scientist, a Ph.D. in data science takes 3-5 years to complete. Admission requirements vary, though programs typically prefer students with a master's degree in data science and professional experience. Earning a Ph.D. is the best way to become a professor of data science.
  • Graduate Certificate in Data Science: Graduate certificates in data science take several months to complete. Most certificates allow students to select elective courses to specialize their certificates. These certificates teach new skills through hands-on learning experiences.
Learn More About Ph.D.s in Data Science
Explore Data Science Degree Levels
The Difference Between Data Science and Actuarial Science

Earning Your Master's Degree in Data Science

Master's in data science programs usually require 30-36 credits and take 1-2 years to complete. Full-time students may take 12-18 credits each semester, completing the program in 1-1.5 years. Part-time students take fewer credits each term, often finishing the program in two years.

Some master's students choose to complete their degrees online. Online master's degrees in data science may differ from their on-campus counterparts in program length and cost, but online students complete the same courses as on-campus learners.

Comparing Master's Degree Options

Data science represents just one branch of computer science and frequently crosses over with other branches. Therefore, prospective data science master's students can choose from a variety of relevant degrees to meet their goals. Visit this resource to learn more about divergent career paths for the following master's degrees and more.

This degree combines the technical nature of data science with management skills. The curriculum covers topics like data analysis, data structuring, management, leadership, and communication. Master's degrees in applied data science best suit those interested in leadership or executive positions.
This master's degree draws upon students' pre-existing computer science skills, applying data science techniques and practices. Students learn skills like how to create insights from structured and unstructured data and solve problems through data tools.
A master's in data analytics focuses solely on the mining, management, mapping, and analysis of data. While similar to data science, data analytics avoids data science's computer science requirements and teaches students to work with popular data analytics programs.

Popular Master's Degree in Data Science Courses

Every school creates its own curriculum, but accredited institutions tend to require similar courses. Data science learners typically take core, elective, and capstone courses. Some programs may also require internships.

Because data science is an ever-evolving field, colleges and universities regularly update their curricula to stay current. However, most master's in data science programs offer some or all of the following courses.

Machine Learning
This introductory course teaches students how to apply machine learning techniques to solve problems, evaluate information, and interpret results. Machine learning plays an increasingly important role in analyzing large data sets.
Big Data in Finance
Usually offered as an elective course, big data in finance applies the collection, processing, and analysis of large data sets to the finance industry. Students work with big data and trading algorithms to improve financial performance and generate larger, more consistent returns. This in-demand skill could lead to careers in the financial sector.
Algorithms for Data Science
This course builds on existing data structure skills and teaches methods for organizing data, including hashing, trees, and queues, along with how to use algorithms to sort this data. Students also learn about algorithm design and techniques.
Natural Language Processing
This elective course teaches students how to analyze linguistic patterns through machine learning. Students gain an understanding of how computer systems process, comprehend, and communicate human languages.
Capstone Project
The capstone project usually requires students to gather data from industries, governments, or nonprofits and evaluate the data to provide actionable insight. The capstone project takes place during students' final semesters.

The Master's Practicum and Thesis

Master's degrees may culminate with a capstone, practicum, and/or thesis course. These courses provide students with opportunities to use the skills they learned during their graduate studies.

Most data science master's programs require capstone courses, which often require students to work in teams to demonstrate strategic thinking, communication, and data science skills. Because capstone courses call for collaboration, some online master's in data science programs require online students to attend on-campus capstone preparation seminars.

Alternatively, some programs allow students to pursue thesis projects instead. A data science thesis is an independent study project demonstrating the student's ability to conduct independent research. It culminates in a scholarly paper or an applied project.

Selecting Your Master's Degree in Data Science Program

When weighing potential programs, prospective data science graduate students should take various factors into account, including:

School Size

Small schools typically offer more individualized attention from faculty but fewer program options than larger schools.

Program Cost

Students should consider expenses such as tuition, fees, and housing when considering prospective programs.

Average Class Size

Smaller classes typically provide students with more faculty interaction.

Student-to-Teacher Ratio

Low student-to-teacher ratios ensure that students benefit from one-on-one time with their professors.

Admissions Difficulty

Programs with selective admission criteria tend to hold higher prestige but accept fewer students than schools with less selective admission criteria.

Program Outcomes

Students should research the outcome data of prospective programs to ensure the program can prepare them for their intended career.

Online Vs. On-Campus

Some institutions allow students to study online, on campus, or through a hybrid of the two.

Accreditation

Accreditation indicates that a school meets high academic standards and expands employment, education, and financial aid opportunities.

Accreditation for Data Science Schools and Programs

Accreditation represents one of the most important considerations when researching data science programs. Accreditation indicates that a school or program follows academic guidelines set by government-backed organizations.

Schools may hold regional or national accreditation, with regional accreditation generally considered the more prestigious of the two. Many colleges and universities only accept transfer credits and degrees from regionally accredited institutions. Employers might also prefer candidates from regionally accredited institutions.

Programs within a school may also receive accreditation, but because data science is a relatively new field, programmatic accreditation does not yet exist for data science programs.

Should You Get Your Master's Degree in Data Science Online?

Future data science learners should thoroughly research both on-campus and online programs to determine which best suits them. Each option provides clear advantages for some students.

Online programs offer more flexibility over their on-campus counterparts. Additionally, some colleges and universities extend discounted tuition rates to online students, who also avoid on-campus fees. However, the flexible format of online learning requires high levels of self-discipline and motivation. Some learners prefer the structure of on-campus courses, as it keeps them on track and holds them accountable.

Resources

Professional Organizations for Data Science

Professional associations and organizations often provide data science students and graduates with benefits like continuing education, networking, and professional development opportunities. The following list outlines several popular data science organizations.

INFORMS is one of the largest international associations for professional researchers and analysts. This resource provides data scientists with networking opportunities, a job board, and specialized data science publications. This nonprofit association consists entirely of data scientists. The association sets professional and academic standards for data science and offers members professional advancement opportunities. With over 100,000 members, ACM offers members access to a vast, international professional network. It also hosts over 860 local chapters, which provide valuable networking opportunities.

Scholarships for Master's Degree Programs in Data Science

Many schools, organizations, and employers offer scholarships to help students cover the cost of college. The following list outlines several scholarships for master's in data science students.

Who Can Apply: Created for graduate students in their second, third, or fourth semesters, these scholarships support learners planning to become K-12 teachers. Amount: $2,500
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Who Can Apply: The A.T. Anderson Memorial Scholarship goes to citizens or descendants of Native American tribes. Applicants must be full-time students pursuing a STEM degree with a minimum 3.0 GPA. Amount: $2,000
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Who Can Apply: This scholarship goes toward a male student currently pursuing a STEM degree. Applicants must hold membership with the NAACP and demonstrate a minimum 3.0 GPA. Amount: Up to $3,000
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Who Can Apply: Female students interested in pursuing STEM degrees may apply for this scholarship. The one-time scholarship can go toward tuition, fees, and books. Amount $10,000
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Who Can Apply: Only available to master's and doctoral students, these fellowships usually go to women and/or those from minority racial or ethnic groups. Amount: $15,000
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FAQ's About Master's in Data Science

Is a data science master's worth it?

A master's in data science leads to multiple high-paying, high-growth jobs. Some graduates find leadership positions.

What can I do with a master's in data science?

Graduates can work as executives, managers, and consultants within private, public, and nonprofit industries.

Is a master's in data science hard?

Data science is a highly complex field that requires a bachelor's degree and professional experience. Most programs are fairly difficult.

How long does a master's in data science take?

Full-time students can often complete a master's in data science in 1-1.5 years, while part-time students usually take 2-3 years to complete the degree.

Additional Reading

Master's in Computer Science
Ph.D. in Data Science
Explore Computer Science Careers

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