Projections indicate that humans will generate 463 exabytes of data per day by 2025. That’s about 500 billion gigabytes, enough to fill the hard drives of one trillion top-of-the-line MacBook Pros, which would create a stack about 10 million miles high (about 20 round trips to the moon).
Impressed yet? TL/DR: that’s a whole lot of data.
There’s gold hidden in all that data. That’s why companies seek, and often struggle to hire, professionals with the right data management skills. There simply aren’t enough qualified applicants to meet growing data needs.
This shortage raises the question: do you even need a master’s degree? Experts on KDnuggets, a leading site in data and analytics, debate whether prospective data scientists should pursue a graduate degree. The answer? Individual study, open coursework, and certifications can help a diligent student land a dream job. That said, a master’s provides excellent learning and job opportunities. For many, it is the surest road to success.
Those who earn a Master of Science in Data Science can benefit from alumni and corporate connections unavailable to self-taught students. Graduates pursue data careers and develop the skills for interdisciplinary applications in fields like finance and healthcare. Those with a master’s can land six-figure data jobs, including careers in:
So, who gets a big data master’s degree, anyway? Read on to learn more about what it’s like to earn a data science master’s and where it can lead. This article covers:
Data science is one of the main branches of computer science. Data scientists typically utilize:
While data scientist may be the job most linked to data science, it’s far from the only one.
According to the tech company Oracle, data science “combines multiple fields, including statistics, scientific methods, and data analysis to extract value from data.”
Data science professionals can use their knowledge and skills in many ways and in almost every industry. You might specialize in business intelligence or robotics or healthcare informatics. There are almost too many options.
90 percent of data scientists hold master’s degrees, and 47 percent hold doctoral degrees. ( )
The Bureau of Labor Statistics sets median data scientist annual pay at just over $100,000. Top-paying sectors include ( ):
- Computer and peripheral equipment manufacturing ($148,290)
- Semiconductor and other electronic equipment manufacturing ($142,150)
- Specialized information services ($139,600)
- Data processing, hosting, and related services ($126,160)
- Accounting, tax preparation, bookkeeping, payroll services ($124,440)
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While each item on this list is a data science specialization, it’s important to note that even specialized jobs require multiple skills. For instance, artificial intelligence and machine learning are related subjects, and professionals who specialize in one also typically utilize the other. Most of the sample positions come from Indeed, the job search website.
Artificial intelligence is the process of designing computers to think like humans.
Big data is just like regular data, except you need specific computational methods to interpret it. Those who work with big data and data analytics have excellent data mining and data visualization skills.
Analytics education is tough to describe with a single statement, given how much variation the field has. A bachelor’s in analytics can be all you need to qualify for great positions. Conversely, there can be significant overlap between
data analytics and data science master’s programs. Master’s in business analytics degrees, such as the one at Tulane University of Louisiana, leverage data techniques for business solutions. It may be useful to determine your exact goals before pursuing a Big Data master’s.
This field combines biology and data. Its many applications include analyzing drug efficacy, studying climate change, and even engineering insect-resisting plants.
Computational finance utilizes mathematical processes to solve financial equations and determine whether to buy or sell at a given time. There are great jobs in finance for those with data backgrounds—especially those rooted in math and statistics.
This specialty is focused on data-gathering infrastructure, rather than interpretation. It plays a large role in generating usable data.
Closely linked to AI, machine learning is the process of teaching technology to recognize patterns and make predictions—a good example is the Netflix algorithm that predicts what you might enjoy watching next.
Modeling is the process of organizing and juxtaposing data. It’s the basis for most data science practices.
Data science degrees focus on applying analytics. According to a Burtch Works study. nearly 90 percent of data scientists hold at least a master’s. Students who get into data science master’s degree programs typically have an existing background in data science and work in the field.
Experience is the biggest difference between a BS and MS. Though core topics can have similarities, undergraduate programs, such as the one at University of Utah, typically focus on introductory courses that establish a foundation in data. MS programs usually build on this foundation and help graduates advance their careers.
According to Burtch Works, 48 percent of data scientists have a PhD. Career trajectory is the main difference between a PhD and master’s. PhDs are research-focused and often lead to academic and research positions, though not always. PhDs are longer, often five years, though individual factors contribute to how long you’ll spend. Programs can involve direct work with professors, and perhaps the greatest draw of a PhD is the prospect of funding. It’s more common for schools to fund PhD candidates than master’s students.
Full-time programs average two years of study, while part-time programs usually require three or more.
An MS in data science, like most master’s programs, can be expensive. Top programs can easily cost over $100,000, and that’s not including the opportunity cost of missed work. Still, it’s not all bad news. An affordable online program usually costs under $30,000. The University of Texas at Austin keeps total tuition to $10,000.
Though expertise can vary, MS applicants should already know how to analyze data and use it to improve decision-making. A firm grasp of core competencies and methodologies, including algorithms, data management, and statistical models, serves you well. While not every data science program has the same admissions standards, prospective students often use a master’s program to further specialize in their field or shift focus to a related area.
Recently, more schools have begun offeringundergraduate data science programs, according to an article in US News & World Report.
Those who enter a master’s in data analytics usually have real-world experience in data analytics, engineering, or architecture across fields like information technology, cloud computing, finance, and even defense. University of San Francisco students have a median of 2.5 years of work experience, with an upper-range of 13. About one-third of students have a year or less experience. Naturally, each school has different demographics, but these numbers are decently representative.
There’s no set age range for a master’s in data science. University of California – Berkeley lists students as anywhere from 21 to 67. Considering that most have roughly two years of post-undergraduate work experience, you can expect many of your classmates to be in their mid-twenties—though many will be older.
Master’s programs are a huge draw for international students. The University of Washington class is 59 percent international, and the University of San Francisco is 44 percent, including students from China, India, Italy, and Eritrea.
White men take up most data science positions, like all STEM careers. Recently, numbers have been shifting at some schools. The University of Washington student body is nearly 60 percent female and 21 percent Asian American; however, only six percent of students are underrepresented minorities. The University of San Francisco data science student body is 71 percent US minority and 40 percent female.
Though there’s no single path to admissions, PhD candidates can benefit from having a master’s. The University of Nevada – Las Vegas prefers students with a master’s in math or statistics. Worcester Polytechnic Institutelooks for students who meet one of these three criteria:
Aside from curriculum requirements, certain master’s programs set timelines for students to complete their degrees. The standard track at Berkeley, which is designed to be completed while working, takes 20 months. The decelerated track takes 32 months, maximum. Not all institutions have strict time requirements.
Most schools ask students to complete a capstone project. Not just a graduation requirement, projects are an excellent way to boost your portfolio and market yourself to potential employers. On-campus students at University of Virginia – Main Campus, for example, begin work on their capstone in the second term, and have opportunities to collaborate with industry members.
Master’s in data science curricula can vary greatly by school and focus. Schools like University of Chicago offer data analytics programs that are essentially data science programs, so it’s important to go by a program’s curriculum outline rather than its title.
What follows is a general description of what you can expect to learn in a data science master’s.
Remember, MSDS curricula aren’t standardized, so individual programs will vary. Core courses frequently include:
You may also be able to pick a specialization track. As part of its master’s in data science, Stevens Institute of Technology offers:
Each track has core and elective requirements.
Contrary to their optional-sounding name, students must complete a certain number of elective credit hours to graduate. These can either concern your chosen speciality, or just be general electives. Tufts University students must complete one course in each topic:
Top programs may get institutions to sponsor students’ final projects. University of Washington students have worked with companies like:
Though there are subtle differences, “capstone,” “final project,” and “thesis” are three terms that often address the same goals. They allow students to showcase their abilities and benefit from real-world learning experiences. Examples of student projects include:
It’s impossible to say definitively which master’s program is “best” because each student has different needs. Still, knowing about well-established programs can be a useful starting point.
Top-ranked MSDS programs include:
Deciding to earn an online master’s can drastically increase your options.
Top ranked online MSDS programs include:
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