Master’s in Data Science Requirements

If you’re looking to enter a high-demand, high-impact field that offers competitive salaries, look no further than data science. According to Quanthub, open data science positions outnumber qualified data scientists and likely will continue to do so for some time. US Bureau of Labor Statistics data support that assessment, indicating that the job demand in data science should increase by 27.9 percent through 2026. That’s more than seven times the growth rate for the overall job market.

If you are interested in data science and have the skills required to succeed in this field, now is a great time to consider pursuing a master’s degree in data science. Graduates with a data science degree can apply their skills to many different fields, including technology, industrial manufacturing, financial services, and healthcare.

Do you have what it takes to build a career in data science? If you’re unsure, read on; this article addresses many questions you may be asking yourself. It covers:

• What is data science?
• Data science specializations
• What is a master’s in data science?
• Master of Data Science admissions prerequisites
• Requirements to complete a data science master’s degree program
• Best Master of Science in Data Science programs
• Best online Master of Science in Data Science programs

What is data science?

Data science is a computer-science discipline that exploits software, machine learning tools, and statistical practices and theory to collect, analyze, and interpret complex data sets. Successful data scientists are data-driven professionals who excel in data analysis, computer science, mathematics, and statistics.

Organizations rely on data scientists to interpret and leverage complex data to make critical decisions. Many data scientists hone their skills within a specialization area. There are good reasons for this: first, data science is a big field, impossible to master in its entirety. Second, an effective data scientist needs expertise not only in data practices but also in the enterprise to which their practices are applied. A data scientist employed to improve supply chain management, for example, can be more effective and productive if they understand supply chain as well as the database practices, statistical analyses, and reporting protocols they implement.

Data science specializations

Artificial intelligence

AI enables machines to imitate human functions (think Amazon’s Alexa or Apple’s Siri). AI offers many benefits to both consumers and organizations. Consumers use AI daily within their smart devices and streaming services, while organizations utilize AI to research and define opportunity.

From healthcare to retail, many career paths require AI. Some examples of job titles within AI are:

• Artificial intelligence developer
• Artificial intelligence engineer
• Artificial intelligence researcher
• Research scientist
• Software engineer

Big data and data analytics

Big data analytics describes the collection and analysis of massive data sets, a process that enables organizations to identify opportunities and influence behavior. For example, organizations use data analytics to inform marketing practices, identify new revenue opportunities, and improve customer service. The demand for big data and data analytics is prevalent across industries. Job titles include:

• Big data engineer
• Business intelligence analyst
• Data modeler
• IT systems analyst
• Marketing analyst
• Quantitative analyst

Bioinformatics

Bioinformatics is an interdisciplinary specialty combining biology and computer science to drive biological discoveries. Professionals in bioinformatics typically spend time looking for patterns within sequences of DNA or protein. Bioinformatics is a growth field with high demand for professionals with real-world experience in biology, statistics, and computer science. Career options for those interested in bioinformatics include:

• Bioinformatics engineer
• Bioinformatics research scientist
• Bioinformatics scientists

Computational finance

Also known as financial engineering, computational finance combines mathematics, statistical modeling, and computing to inform the finance field. Computational finance professionals apply expertise in financial data science and quantitative risk management; many pursue computer science or finance for their bachelor’s degrees. Job focused on computational finance include:

• Asset manager
• Finance analyst
• Financial engineer
• Fund manager
• Risk manager

Cybersecurity

Cybersecurity is the practice of protecting systems and networks against cyberattacks. Roles within cybersecurity offer competitive salaries and robust opportunities. With the growing threat of cyberattacks, the demand is rapidly increasing for cybersecurity professionals. According to the US Bureau of Labor Statistics, cybersecurity jobs should grow by 31 percent between 2019 and 2029. Cyber Defense Magazine projects a need for “1.8 million additional cybersecurity professionals to fill the workforce gap by 2022.” Jobs that require cybersecurity skills go by a range of titles. Popular job titles within cybersecurity include:

• Chief information security officer
• Cybersecurity analyst
• Information security analyst
• Security architect
• Software security engineer

Data engineering

Data engineers build data pipelines and organize data for analysis. A data engineer typically has deep experience in system architecture programming, database design and configuration, and interface and sensor configuration. To become a data engineer, you will need to complete a data science master’s program and take courses that focus on programming skills. Data engineering jobs include:

• Big data engineer
• Data architect
• Data engineer
• Senior cloud data engineer

Data modeling

Data modeling is an essential skill for a data scientist. Data modeling’s primary goal is to ensure that complex data is stored and retrieved in the most efficient way possible. To become a data modeler, you will need to be an abstract thinker with skills in information science, applied mathematics, and computer studies. Here are some jobs that you can expect to find within data modeling:

• Database administrator
• Data modeling analyst
• Data modeling engineer
• Data modeling specialist
• Quantitative modeler/coder

Machine learning

In many ways, artificial intelligence (AI) and machine learning go hand-in-hand. Machine learning allows artificial systems to learn from data and complete human-like tasks. To get started in AI and machine learning, you need to have the necessary mathematics and computing skills. Jobs in machine learning include:

• AI engineer
• Data mining and analysis
• Machine learning engineer
• Machine learning researchers
• Machine learning specialist

What is a master’s in data science?

A master’s in data science is a graduate-level degree covering the practice and theory of gathering, organizing, interpreting, and applying data. The best data science programs include coursework in high-level mathematics, data visualization, data mining, and IT ethics.

Many students enter these programs with previous education and professional experience in analytics, advanced technology, IT, computer science, engineering, and/or mathematics. Students who complete this graduate program in data science might also explore careers in computer science, data management, or information research.

How is an MS in data science different from a BS in data science?

An MS and BS in data science typically result in different career opportunities. Those with a BS in data science qualify for entry-level positions in tech-related fields, while those with an MSDS qualify for advanced roles with higher pay.

Bachelor’s degree programs typically provide a foundation in such areas as linear algebra, data structures and algorithms, calculus, geometry, statistics, and computer programming. Master of Science programs assume students have this knowledge and so focus on higher-level content. Most master’s programs require a capstone project, allowing students to gain real-world data science experience.

How is an MS in data science different from a PhD in data science?

A PhD is the highest-level graduate degree in data science (it is sometimes referred to as a “terminal degree” for this reason). It qualifies holders for high-level professional roles in private enterprises and other institutions. Many PhDs engage in cutting-edge research, either at a university or other institutions (e.g., healthcare organizations, government agencies). A PhD can take six years to complete (it can take even longer, in fact; most schools set a six-year limit but will extend it for many candidates).

A master’s degree is often a prerequisite degree for a PhD. Master’s holders more frequently enter the corporate and nonprofit world in many of the roles identified earlier in this article. Full-time master’s candidates can complete the degree in 18 months to two years (faster in an accelerated program); part-time students may take three years or longer.

How much does an MS in Data Science cost?

The cost of an MS in data science varies depending on whether the school is private or public and the length of time one takes to complete the program. Graduate school doesn’t have to break the bank, as there are plenty of affordable data science programs.

Students can find some of the most affordable online data science graduate programs at:

• Georgia Institute of Technology – Main Campus: (online) $275 per credit
• Indiana University – Bloomington: (online) $478.82 per credit
• Oregon State University: (online) $560 per credit
• University of Maryland – Baltimore County: $659 per credit
• The University of Texas at Austin: (online) $333 per credit

How much will you make with an MS in Data Science?

A master’s in data science should boost your earning potential. Data scientists can earn salaries in the six-figure range; many such jobs require at least a master’s degree. According to Labor Insight, the national median salary for a data scientist is $114,888. Bachelor degree holders average at $113,200, while those with a master’s degree average $116,700. PhDs earn, on average, $116,900.

Master of Data Science admissions prerequisites

Enrollment into a data science master’s program requires specific admission prerequisites. These prerequisites vary based on the program, but in general, these are the requirements you are likely to face:

• Undergraduate record (transcripts): Typically, applicants need to provide official transcripts showing their credits and GPA. You can usually submit your undergraduate record by mail or electronically.
• Graduate record (if any): If you have a graduate record, you will need to arrange to send that to the admissions committee as well.
• Standardized test scores (GRE): More and more schools are making standardized tests optional. Many do still require test scores, however; those that do usually ask for GRE scores.
• Work experience/resume: Almost all MSDS programs seek students highly skilled in mathematics, computer programming, and programming languages such as Java and Python. Your resume should include your education, academic background, work history, and professional accomplishments. You can also include a list of skills, research experiences, and other qualifications that demonstrate your abilities.
• Letters of recommendation: A direct supervisor who can speak directly on your abilities in an academic or employment setting will send a letter of recommendation on your behalf. Your recommender should be able to address your potential to excel in an MSDS program. Some programs require as many as three letters of recommendation.
• Essays or personal statement: The essay or personal statement portion of an application provides an opportunity to express why you want to pursue the specific program, your future career goals, and any aspects of your background that might elevate your application. Most schools provide an essay prompt (in the form of a question, e.g., Why do you believe our program best meets your career goals?) and expect a well-written and focused response.
• Interview: An interview is your opportunity to show your personality and express your enthusiasm for the program.
• Employer sponsorship: One way students fund their degree is through employer sponsorship. Employers can also provide support by adding flexibility to your work schedule.
• English fluency (non-native speakers): If English is not your first language, many schools require that you demonstrate English language proficiency by providing official TOEFL (Test of English as a Foreign Language) scores.
• Financial responsibility (international students): The US government requires international students to provide evidence of funds sufficient to pay for educational and living expenses. Students can start this process by completing the appropriate paperwork.

Requirements to complete a data science master’s degree program

Every data science program requires a unique set of standards for degree completion. Most data science programs require specific core courses that involve a final thesis or research project. Some universities, like the University of Washington, require a capstone project, while other universities, like Georgia Institute of Technology, require a practicum to develop real-world analytics experience.

Master’s in data science curriculum

A typical data science curriculum includes core courses related to mathematics, programming, data, and analytics. Most programs require a specified number of electives and completion of a final research project.

Typical MSDS core curriculum

A typical MSDS core curriculum covers big data analytics, visual analytics, computing, statistics, and operations research. The core curricula of MSDS programs differ based on the specific program and specialty area you pursue.

For example, the curriculum at the University of Virginia (Main Campus) combines technical, quantitative, and philosophical study. Students complete the program in two years with 14 graded courses and 32 credit hours and take the following core requirements:

• Bayesian Machine Learning
• Big Data Analytics
• Data Mining
• Data Science Capstone Project Work
• Ethics of Big Data
• Exploratory Text Analytics
• Foundations of Computer Science
• Linear Models for Data Science
• Machine Learning
• Practice and Application of Data Science
• Programming and Systems for Data Science

The MSDS at the Georgia Institute of Technology takes one-to-two years to complete and consists of 15 hours of core coursework covering big data analytics in business, visual analytics, computing, statistics, and operations research. The following core courses are required:

Examples of MSDS electives

Most MSDS programs offer electives that allow students to focus their degree toward a specific area. For example, at Stevens Institute of Technology, students can choose from a list of interdisciplinary electives related to topics in business application, industry, or academia. These electives include:

• Cognitive Computing
• Database Management Systems
• Discrete Mathematics for Cryptography
• Distributed Systems and Cloud Computing
• Engineering Programming: Python
• Enterprise and Cloud Computing
• Foundations of Cryptography
• Fundamentals of Cybersecurity
• Introduction to Programming, Data Structures, and Algorithms
• Numerical Linear Algebra for Big Data
• Spatial and Spatio-Temporal Statistical Modeling
• Time Series Analysis I
• Web Analytics

MSDS final project/thesis

Many MSDS programs require a final research project/thesis. For example, Indiana University and Oregon State University require a final capstone project, while Stevens Institute of Technology allows students to complete a final thesis for six credits as part of their electives.

Best Master of Science in Data Science programs

Earning a master’s degree in data science will provide you with the knowledge you need to excel in the field. From big data to data analytics, there are many different degree paths you can take while pursuing a master’s in data science program.

Top Master of Science in Data Science programs include:

• Carnegie Mellon University
• Columbia University
• New York University
• Stevens Institute of Technology
• Tufts University
• Tulane University of Louisiana
• University of Rochester
• University of Virginia (Main Campus)
• University of Washington

Best Online Master of Science in Data Science programs

The best online Master of Science in Data Science programs include:

• CUNY City College
• Indiana University – Bloomington
• Pennsylvania State University – World Campus
• Stevens Institute of Technology
• Tufts University
• University of Colorado – Denver
• University of Maryland – Global Campus
• University of Michigan – Ann Arbor

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