Guide to a Master’s Degree in Data Science
| ComputerScience.org Staff
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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
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.
- 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.
|Master's Degree in Data Science Careers: Median Salaries by Experience|||||||||
|JOB TITLE||ENTRY LEVEL (0-12 MONTHS)||EARLY CAREER (1-4 YEARS)||MID-CAREER (5-9 YEARS)||EXPERIENCED (10-19 YEARS)|
|Computer and Information Research Scientist||$101,000||$103,000||$118,000||$149,000|
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.: 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.
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.
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:
Small schools typically offer more individualized attention from faculty but fewer program options than larger schools.
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.
Low student-to-teacher ratios ensure that students benefit from one-on-one time with their professors.
Programs with selective admission criteria tend to hold higher prestige but accept fewer students than schools with less selective admission criteria.
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 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.
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.
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.
Frequently Asked Questions
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