Companies and organizations in all industries are collecting more information than ever before. However, interpreting, organizing, and applying all that data can prove challenging for even the most sophisticated organizations. Data scientists extrapolate meaning from raw data using algorithms and data analytics programs. As naturally inquisitive and critical thinkers, data scientists ask questions, challenge assumptions, and identify trends difficult for most people to see.
The field's growth in recent years and salary of $110,000 a year explain why Glassdoor named data scientist the best job in America in 2018.
The field's growth in recent years and salary of $110,000 a year explain why Glassdoor named data scientist the best job in America in 2018. Generally, data scientists also report extremely high levels of job satisfaction.
Should I Get a Master's in Data Science?
If you want to enter the field of data science, earning a data science master's degree can open many opportunities. Although some entry-level positions accept candidates with only a bachelor's, many employers prefer to hire prospective employees with at least a master's degree. Completing an internship while still in school can give you a competitive advantage over your peers.
Professionals already working in data science and individuals looking to change careers might prefer the convenience and flexibility of an online program. On the other hand, students coming straight from an undergraduate education may like the idea of a traditional on-campus program. On-campus programs usually offer more networking opportunities for new professionals.
Data science master's programs teach students hard skills in mathematics; diffusion geometry, matrix computation, and statistics; programming and scripting languages; distributed computing systems; relational databases; data modeling and mining; visualization; and machine learning. The best best data science master's programs also teach students the importance of so-called “soft” skills, which includes curiosity, creativity, storytelling, project management, communication, and ethics.
In addition to education, students attending a data science master's program receive several career benefits. While in the program, students can network with their classmates and instructors. These informal connections can often lead to job interviews and other opportunities. Most colleges and universities also host career centers that provide assistance with internships and job placement, advice for fine-tuning resumes and cover letters, workshops, and mock job interviews.
What Can I Do With a Master's in Data Science?
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 Scientists
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: $114,520
Projected Growth Rate: 19%
- Operations Research Analysts
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: $81,390
Projected Growth Rate: 27%
- Management Analysts
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: $82,450
Projected Growth Rate: 14%
- Computer Systems Analysts
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: $88,270
Projected Growth Rate: 9%
How to Choose a Master's Program in Data Science
Choosing the right master's in data science can seem overwhelming. Students need to consider many different factors, including school location, program length, cost, and accreditation status. If you attend an on-campus program, the location of the school can affect your employment opportunities, cost of living, and overall quality of life. Location can prove important even if you choose an online program. Some online master's in data science degrees require students to complete in-person residencies. Location also affects cost; most schools charge out-of-state students higher tuition rates.
Many factors can affect program length, including if you attend school on a part-time or full-time basis. Part-time students usually take longer to finish programs. However, these students have an easier time maintaining a full-time job or other obligations. Certain programs allow students to graduate sooner by doubling up on credits, taking classes year-round, or earning credit for previous training or work experience.
Students should also look at each school's curriculum. Each program offers different classes, concentrations, and thesis or project requirements. Look for programs that offer classes and specializations that deal with the specific topics you wish to study more closely.
Before you begin applying to programs, you should figure out how much you can afford to spend on your master's. All programs list tuition rates on their websites, as well as information about financial aid.
Programmatic Accreditation for Master's Programs in Data Science
Before you decide where to complete your master's in data science, make sure any schools you consider hold regional accreditation. Accreditation demonstrates that a college or university meets minimum quality standards agreed upon by the larger educational community. If you do not attend an accredited school, you may not receive financial aid or transfer credit.
Programmatic accreditation indicates that an academic program or department meets standards set by a particular field or industry. These programs have been proven to give students the skills they need to succeed. For the field of data science, programs at the undergraduate level usually receive accreditation from the Computing Accreditation Commission of the Accreditation Board for Engineering and Technology. Data science programs at the master's level do not currently receive programmatic accreditation.
Master's in Data Science Program Admissions
The admissions process for data science master's programs varies from school to school. Traditional on-campus programs tend to require more application materials. Online programs usually take less time to apply to; however, certain schools require students to demonstrate that they can succeed in an online program. All master's in data science programs require a bachelor's, usually in a related field. Most of the time, candidates also need to meet a minimum GPA requirement.
Schools tend to request official transcripts from all previously attended colleges and universities, letters of recommendation, and GRE scores. Most schools also require an application fee, ranging from $40-$100. Some programs ask prospective students to submit answers to essay questions or personal statements describing their interest in the program.
It often makes sense to apply to multiple schools in order to increase your chances of acceptance. Apply to a combination of programs, including some you feel confident you will get into and others that might seem like a stretch. To avoid wasting time and money, only apply to schools that you would seriously consider attending.
- Bachelor's Degree: You must hold a bachelor's degree in a field related to data science, usually from a regionally accredited college or university. Most programs also require applicants to have a number or prerequisite math and computer courses.
- Professional Experience: Most data science master's programs do not require prospective students to hold relevant professional experience. However, demonstrating relevant professional experience may improve your chances of acceptance into the program.
- Minimum GPA: Many programs require students to hold at least a 3.0 GPA. More competitive programs may require higher GPAs, whereas other schools do not set a specific minimum GPA for incoming students.
- Application: Prospective students must fill out an online application form when applying to a data science master's program. The application usually takes a few hours and includes personal data and essay questions.
- Transcripts: You must submit official transcripts from all previously attended colleges and universities. You can request your transcripts from the office of the registrar's website, usually for around $10 per copy.
- Letters of Recommendation: Most programs request three letters of recommendation. Ask for letters from previous college professors and work supervisors who can speak to your strengths. Give your references at least two weeks to write the letters, preferably longer.
- Test Scores: Many master's in data science programs request that applicants submit GRE scores. The average score varies by program, but many schools do not set a specific minimum score.
- Application Fee: Graduate application fees typically cost $40-$85, but some programs ask for more than $100. If you can demonstrate financial hardship, you may qualify for a fee reduction or waiver.
What Else Can I Expect From a Master's Program in Data Science?
Each master's in data analytics degree offers different courses, concentrations, and capstone options. However, most programs share a set of common core courses, including classes in machine learning and big data. If you already know that you want to focus on a specific area in the data science field, research programs that offer a specialization in your area of interest.
|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.||Data scientist, big data engineer|
|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.||Health data scientist|
|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.||Data scientist, computer scientist|
|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.||Data scientist|
|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.||Data scientist, programmer, machine learning engineer|
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 Long Does It Take to Get a Master's in Data Science?
Most data science master's programs require students to complete about 60 credits. Full-time students usually take about two years to earn their master's degree. Programs with internship or thesis requirements may require more time. Students who attend school part time usually take between three and four years to graduate.
Several characteristics can also affect the length of a master's program. Some programs offer courses year-round, while others do not host classes during summer and winter breaks. If you want to complete your degree as quickly as possible, make sure to find out your program's schedule of courses when you apply. Some students, particularly working professionals, seek programs with flexible schedules. These learners may take only one course during busy times of year, then enroll in more classes later on. Find out your program's scheduling policy before you enroll.
How Much Is a Master's in Data Science?
The cost of a master's in data science varies by program and school. Graduate credits tend to cost significantly more than undergraduate credits. However, many graduate programs offer the opportunity to work as research or teaching assistants in exchange for reduced or free tuition. On-campus programs tend to offer these opportunities more often than online programs.
In general, data science master's degrees cost the least at public institutions. On average, public schools cost $11,617 per year. By contrast, private nonprofit schools average $26,551 annually. The most affordable way to earn your master's in data science is to attend a public university where you qualify for resident tuition. Make sure you consider additional educational expenses, including costs for housing, books, commuting, and materials.
Certifications and Licenses a Master's in Data Science Prepares For
- Certified Analytics Professional
The CAP certification signifies advanced expertise in the analytics profession. Candidates must hold a bachelor's and five years of professional experience or a master's and three years of professional experience. Applicants must also pass an exam and sign a code of ethics.
- Associate Certified Analytics Professional
Designed for entry-level analytics professionals, the aCAP signifies that candidates have the potential to excel as data scientists. The certification benefits individuals that hold education but lack substantial professional experience. To receive the credential, applicants must hold a master's in data analytics, sign a code of ethics, and pass an exam.
- Microsoft Certified Solutions Expert: Data Management and Analytics
The MCSE data management and analytics credential demonstrates advanced skill in building enterprise-scale data solutions, analyzing business intelligence data, and administering SQL. To receive and maintain the credential, you must pass an exam and complete continuing education credits. Applicants can choose from a variety of different exams.
Resources for Graduate Data Science Students
The Data Science Association (DSA) frequently updates this collection of news stories related to data science. The DSA adds new listings about once a week.
The Data Science Conference enables data science professionals to network with each other and discuss professional issues. Only professionals can register for the conference; vendors and recruiters may not attend.
DrivenData hosts online contests for data scientists around the world. Participants collaborate to solve advanced predictive problems using statistical models.
An online platform for data scientists and others working with data, Data Society delivers a variety of trainings and online courses. The platform also hosts a data science blog, a forum, and data sets.
This website collects a variety of articles and useful tools related to data science. Topics include machine learning, programming, and visualization.
Professional Organizations in Data Science
Professional organizations offer numerous benefits to current students and recent graduates, including networking opportunities, continuing education programs, discounted annual conferences, free publications, and listservs. Many associations also maintain job boards and career services. Students can usually pay a heavily discounted student membership rate.