Master's in data science programs train students in the analysis and leadership skills needed to work as computer systems managers and chief information officers. In a data-intensive world, companies and organizations in all sectors hire data scientists to transform vast digital information into actionable strategies. Students can complete traditional master's programs in this field in two years. They may expedite graduation by enrolling in accelerated, one-year tracks.

Graduate data science learners typically take core classes like data preparation, decision analytics, and project management. They pursue advanced topics through concentrations and round out their training with a capstone project. This guide helps prospective learners find the master of data science programs that fit their needs and objectives. Students also receive an overview of the admissions process.

What Is Data Science?

Data science is an occupational field focused on obtaining insight from complex and often seemingly disparate digital information. Due to the pervasive and essential nature of data, practitioners in this field are in high demand across most industries, particularly education, industrial manufacturing, financial services, and healthcare. Companies and organizations generate vast quantities of data each day. Data scientists turn that data into useful strategies that facilitate corporate decision-making and program development.

Data scientists possess a unique combination of skills centered on data analytics, technical communications, and business leadership that make them a valuable part of any professional team. According to the Wharton School of the University of Pennsylvania, data science-based jobs have multiplied nearly 20 times between 2015 and 2018. The demand for these specialists is so high in fact, that there is currently a shortage of data scientists in the United States. Furthermore, the Wharton School projects that, moving forward, all business professionals will need a base understanding of data science to advance their careers.

To learn more about the data science field, explore this in-depth degree guide. In addition to detailing the best online master's in data science programs, the guide explains academic accreditation and the financial aid process.

What You Can Do With a Master's in Data Science

You can discover additional opportunities for data science majors by accessing this complete career page. The guide includes information on job options by degree level, major industries for data scientists, and professional development resources.

A master's degree in data science prepares graduates for competitive careers in technology and business. The field of data science is so new that organizations that collect occupational data, including the Bureau of Labor Statistics, do not collect data on data scientist jobs. However, individuals with a degree in data science can also pursue several other careers, including computer and information research scientist, operations research analyst, and management analysts. Known for being naturally inquisitive, data scientists love to solve complicated problems. They take raw data and interpret it in ways that make sense to the rest of the population.

Computer and Information Research Scientist

Most computer and information research scientists work for the federal government or in computer systems design. These scientists often specialize in data science. They design computing technology and create new uses for existing technologies. Typically, candidates need at least a master's for this occupation.

Median Annual Salary: $118,370

Projected Growth Rate (2018-28): 16%

Operations Research Analyst

These analysts use mathematical methods and sophisticated software to help organizations solve complicated problems and make better decisions. They typically work in teams. Many employers prefer to hire operations research analysts with a master's degree.

Median Annual Salary: $83,390

Projected Growth Rate (2018-28): 26%

Management Analyst

Management analysts use analytical, communication, interpersonal, and problem-solving skills in order to improve efficiency and increase profitability in organizations. Analysts collect information, interview personnel, analyze data, and make recommendations based on their findings. Most work as consultants. Holding a master's can lead to more advanced job opportunities.

Median Annual Salary: $83,610

Projected Growth Rate (2018-28): 15%

Computer Systems Analyst

Computer systems analysts study organizations' computer systems and procedures. They use their understanding of information technology and business to help organizations operate more efficiently. Entry-level positions typically require a bachelor's, but candidates with a master's might find more advanced and high-paying job opportunities.

Median Annual Salary: $88,740

Projected Growth Rate (2018-28): 9%

You can discover additional opportunities for data science majors by accessing this complete career page. The guide includes information on job options by degree level, major industries for data scientists, and professional development resources.

What to Expect in a Master's in Data Science Program

Master's programs in data science generally total a minimum of 30 semester credits, which you can finish in 1-2 years depending on course format and your prior academic and work experience. The degree plan often involves required coursework like linear models, data visualization, and IT ethics. The best online master's in data science programs also offer immersive industry experiences. The following lists detail course and concentration options for data science students.

Concentrations Offered for a Master’s Degree in Data Science

Big Data
Students who choose a big data concentration learn the primary methods for analyzing, managing, and visualizing large quantities of data. Coursework includes a hands-on project building end-to-end computing solutions. Classes required for this track include information visualization, natural language understanding, and advanced database systems.
Biology
Designed for students who want to learn to apply their computational skills to problems in the biomedical sciences, the biology concentration requires students to take five biology elective courses. Available classes include systems biology, bioinformatics and genomes, and biological databases and data mining. Students must complete a biology-based capstone.
Mathematics
A mathematics concentration prepares students to perform advanced data analysis in areas such as high-dimensional statistics, compressed sensing, and graph signal processing. Students take courses in optimization-based data analysis, convex and nonsmooth optimization, and inference and representation. The concentration also explores contemporary research problems related to new data science techniques.
Physics
A physics specialization features courses in astrophysics, statistical physics, and biophysics. The concentration also allows students to learn inference skills and modeling techniques. Especially well suited for individuals with a physics background, this concentration explores physics research topics from a data-intensive perspective.
Natural Language Processing
The natural language processing concentration focuses on building machine learning models that use natural language text. Students explore issues such as advanced linguistics, deep learning, text as data, and inference and representation. Available courses include natural language understanding, computational semantics, natural language processing, and representation learning.

Courses in a Master's in Data Science Program

Curricula for master's in data science programs vary by school, but most require students to complete high-level mathematics and computer programming courses. Many programs also include an optional or required internship or practicum designed to give students hands-on experience in the data science field. The sample curriculum below describes a few common courses students can expect to take.

Introduction to Data Science

This course explores the way that organizations can use data analysis technologies to better their decision-making. Combining the foundational theories of data science with real-world case studies and examples, the course helps students think from a data science perspective. Topics include Python programming, data mining, data abnormalities, biases in data, and matrix factorization.

Big Data

Students learn about the most important contemporary issues in big data management, with a focus on algorithms and tools for supporting big data processing. The course also includes programming assignments use big data platforms, including Amazon AWS. Students explore the evolution of data management, analysis, cloud computing, and big data algorithms.

Machine Learning

This core course covers the fundamentals and applications of machine learning and statistical modeling. Students practice solving the types of data science problems typically encountered in the real world. Topics covered include convex optimization, classification trees, probability models, kernelization, and properties of Lasso.

Programming for Data Science

This course teaches students how to complete data analysis in scientific computing using Python. Students gain experience reading and writing CSV files, using the pandas data analysis library, and working in the Matplotlib 2D library. The course also introduces students to software engineering best practices.

Fundamental Algorithms

Students learn how to efficiently and correctly use algorithms to analyze data. Issues explored include binary search trees, shortest paths, dynamic programming, sorting algorithms, and divide-and-conquer techniques. To take the course, students must hold at least one year of experience using high-level computer languages like Java or C++. Learners should also understand data structures and recursive programming methods.

How to Choose a Master's in Data Science Program

Whether you are a traditional student or working professional, accessible master of data science programs exist to suit your needs. This section covers the important attributes you should consider when searching for the right graduate school.

Accreditation
Higher education institutions must maintain national or regional accreditation to confer valid academic certificates and degrees. National accreditation comes from organizations supported by the U.S. Department of Education and the Council for Higher Education Accreditation. Colleges and universities earn regional accreditation through one of six location-specific organizations. Master's programs in data science can also feature specialized accreditation from the Accreditation Board for Engineering and Technology (ABET).
Program Structure
Colleges and universities usually let students pace their degree plans individually as long as they satisfy minimum requirements. However, some graduate schools operate a cohort learning structure that requires candidates to take classes at a predetermined rate. This collaborative format fosters peer-to-peer learning and networking but can slow program completion. Students who want to enroll in an online master's program should consider how asynchronous or synchronous classes could affect their work and family obligations.
Concentration Options
Although master's programs in data science typically feature similar core classes, students can generally individualize their curriculum through a concentration. Popular options include data engineering, business analytics, and healthcare informatics. Candidates can also take on an artificial intelligence concentration, learning to apply machine learning techniques to develop data-adaptive computer visions and software robotics.
Cost and Financial Aid
According to the College Board, more than 74% of master's candidates leave school with student loan debt. Learners can minimize this financial burden by attending graduate programs that offer low tuition rates and adequate financial aid. Distance education represents a great option for saving money. Many online master's programs in data science deliver affordable tuition that ignores residency status. Distance learners also cut costs by avoiding a commute.
Student Support
Students can bolster their chances of academic success by attending institutions that offer one-on-one degree counseling and tutoring services. Other important resources include accessible research libraries and networking opportunities through campus organizations. Effective graduate schools should provide internship and externship opportunities. They should also help students create engaging resumes and prepare for job interviews.

Master's in Data Science Program Admissions

The following section delves into general enrollment requirements for master's programs in data science. You also gain insight into the admissions process and learn how to set aside enough time to gather and submit application materials.

Prerequisites

  • Minimum GPA: Master of data science programs generally require first-time students to satisfy a minimum 3.0 GPA requirement and transfer candidates to possess a GPA of at least 2.0. Some schools accept applicants with unsatisfactory GPA on a case-by-case basis if they display high test scores and relevant professional achievements. Many online graduate programs do not feature GPA requirements at all.
  • Academic Preparation: On top of GPA requirements, many master's programs in data science assess a candidate's prior schooling for the skills needed to succeed at the graduate level. These competencies often include quantitative analysis demonstrated through applied statistics and calculus coursework. Data science students should also possess a firm grasp of programming languages like SAS, JAVA, C, C++, Python, or R.
  • Work Experience: Accelerated or executive-style master's programs may require candidates to demonstrate at least two years of professional experience in data analytics and management. Traditional programs do not prioritize work experience as an admissions criteria. However, students with relevant career experience can typically turn their accomplishments into transfer credits, thereby saving money on tuition and graduating in less time.

How to Apply

Standardized Test Scores
GRE standards vary by school, so prospective students should confirm the details of this requirement with an admissions counselor. Some master's programs in data science waive standardized testing if a candidate displays three or more years of work experience, or if they already have a graduate degree. Online colleges increasingly ignore standardized testing altogether, opting for more holistic methods of evaluation.
Personal Essay
This one- or two-page document allows prospective students to explain their academic history, work experience, and career goals. Also known as a letter of intent, the personal essay lets schools discern if a candidate's worldview aligns with the institutional mission. In lieu of a formal statement, some programs ask applicants to answer short essay questions.
Recommendation Letters
Applicants should expect to submit 2-3 recommendation letters from professors, supervisors, and organizational leaders who can attest to their skills, successes, and personal qualities. Some schools specify who may or may not write these letters. Other institutions prefer to contact references directly. In either instance, applicants should make sure they give their recommenders enough time to craft detailed testimonies.

Timeline

If your master's program in data science requires GRE scores, prepare to spend 2-3 months (or a total of 120 hours) regularly reviewing materials and practicing for the exam. After the test date, you can expect to receive online results in 10-15 days. Educational Testing Service also sends out official reports to designated schools during this time. GRE scores last for five years.

You can determine when to start preparing application materials by identifying your school's deadline. Most graduate programs set the final deadline two months before classes begin. Many online master's in data science programs operate rolling admissions, accepting students year-round. In any case, do not wait until the last minute to start the application. Give yourself 4-6 weeks to gather academic transcripts and create engaging personal statements and resumes. When in doubt, seek the guidance of your school's admissions specialists. These counselors can help you create a plan that fits your schedule.

Resources for Master's in Data Science Students

Association for Computing Machinery

As the world's largest computing society, ACM maintains leadership councils in the U.S., China, India, and all over Europe. Through its Learning Center, ACM delivers over 55,000 online books and skill-development videos. Membership benefits include financial awards, volunteer opportunities, and discounts on major professional certification programs. Members connect through international conferences and 37 special-interest groups.

Digital Analytics Association

DAA centers its mission on creating a better digital world through data. To this end, the association facilitates webinars, on-site workshops, local symposia, and an annual Marketing Analytics Summit. Members may gain the certified web analyst designation through DAA. They can also take advantage of the career center, which provides compensation reports, self-assessment tools, and job postings.

IEEE Computer Society

The IEEE Computer Society advances the data science field by facilitating research conferences and global volunteer opportunities. Students can access a vast digital library and apply for academic scholarships and research grants. Working data scientists may collaborate with peers by engaging with technical committees in areas like semantic computing, data engineering, and machine intelligence. The society also has three professional certification programs.

Institute for Operations Research and the Management Sciences

With over 12,500 members, INFORMS is the leading professional organization for operations research managers and analytics specialists. In addition to general conferences, the organization sponsors events focused on special topics like cybersecurity and health informatics. Students can apply for scholarships, fellowships, and mentorship opportunities. INFORMS operates continuing education classes and a certified analytics professional program.

Research Data Alliance

Founded as a community-driven initiative in 2013, RDA serves 8,800 members in 137 countries. The alliance's mission is to build global infrastructure that allows open sharing and reuse of data. Members collaborate through 36 working groups and 66 special-interest groups. They can engage in shared research projects and develop skills through webinars. The alliance also offers opportunities specifically for students and early-career professionals.