So many data scientists go to graduate school that the entry-level degree in this field is, essentially, the master's. Luckily, online programs are making it easier for aspiring data scientists to earn the qualifications they need to launch lucrative careers. This guide covers everything you need to know about pursuing an MSDS online.
Master's in data science programs were once relatively rare. It's not that humans weren't generating vast quantities of data. However, the technology needed to derive useful insights from the kinds of unstructured data people create en masse just wasn't widely available.
Then all of a sudden it was, and everyone wanted to harness the power of that data. Between 2012 and 2014, interest in data science exploded. Companies started creating jobs for data scientists faster than people could be trained to fill them. In 2015, data scientist was #9 on Glassdoor's annual list of the best jobs in America. One year later, it hit #1, where it stayed for five years.
Colleges and universities couldn't help but notice there were hundreds of thousands more data science jobs than there were qualified professionals in the field. They responded by creating data science master's programs. Master's degrees quickly became de rigueur in a field once populated by MOOC and boot camp graduates.
Fast forward to 2021. Covid-19 drove massive unemployment across most industries in the year prior, but data science was largely immune. The outlook for data science jobs moving forward is more promising than ever—provided you have a master's degree.
Luckily, it's also easier than ever to get this credential. Colleges and universities are expanding their existing data science degree programs and putting those graduate-level programs in data science online. More importantly, master's degrees for distance learners are common and as respected as on-campus programs.
We created this guide to earning a data science online masters for those curious about what it takes to join this growing—and quickly evolving—field. In it, we cover:
Put simply, data science is:
What makes data science hard to define precisely in just a few short sentences is that it's an amalgam of many different technical and non-technical disciplines and specialties.
Dr. Ganapathi Pulipaka, Chief Data Scientist at Accenture, describes data science as a field that blends "software engineering, predictive analytics, machine learning, deep learning, HPC, supercomputing, mathematics, data mining, databases (SQL, NoSQL), Hadoop, streaming analytics platforms for live analysis (Apache Kafka, Apache Flink, Apache Spark, Apache Impala), IoT platforms, edge computing, fog computing, networks, statistics, web development, cloud computing, data engineering, and data visualization."
In the guide to its online Master of Science in Data Science program, the University of Virginia (UVA) accurately points out that "Data science is not a siloed endeavor; we know that the most meaningful insights are found when we work together and collaborate on methods of analysis and discovery."
Data science has broad applications. Businesses, organizations, and governments use it to solve abstract problems, predict future events, comb through data types that were once impossible to analyze, and even influence human behavior.
In the early days of data science, data scientists could be aptly described as advanced analysts or technical analysts. Today, they're more likely to be domain experts, leveraging data for purposes specific to industries like retail, logistics, and finance. They also occupy roles that aren't as nerdy as most people assume. Many data scientists spend less time fiddling with computers in back rooms than interfacing with executives in boardrooms.
That makes it difficult to sum up exactly what data scientists do. According to University of California - Berkeley's School of Information, they use information to "answer questions and drive strategy."
Dr. Cathy O'Neil and Dr. Rachel Schutt (both talented data scientists) describe the duality of the role in their book Doing Data Science this way:
"A data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills—skills that are also necessary for understanding biases in the data...She'll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications."
Some say yes, and some say no, but there's no way to answer this question definitively. Those who argue in favor of data science as a genuine science point out that this discipline is the careful study of information using observation and experimentation. Some people on this side of the divide even claim that data science is the mother of all science because data scientists work with pure information.
Ironically, those who assert that data science is not a true science use a similar argument to defend their position. Their stance is that scientific disciplines study information about the world, not information itself. Data science may borrow techniques from science, but that doesn't make it a science. "To call oneself a 'data scientist' makes no sense," writes data visualization consultant Stephen Few. "One cannot study data in general. One can only study data about something in particular."
The closest we can come to answer may be that data science is a science when people treat it as one. Data scientists in research and academia purposefully take a very scientific approach to their work, while data scientists working in the corporate world may use data as a means to a profitable end.
Yes, and not only because 90 percent of data science professionals have advanced degrees. Nearly half of all job postings for data scientists specify that candidates without master's degrees need not apply.
There's still a talent shortage in the world of data science, but innovations in automation are shifting the barriers to entry in a big way. "Entry-level" data science jobs go to analysts who know their way around DataRobot and autoML. The top-paying data science jobs are reserved for those with deep domain knowledge, sophisticated tech skills, and advanced degrees. Competition for these positions is fierce.
The frustrating answer is maybe. Close to 50 percent of data scientists have doctoral degrees, and this is a rare field where PhDs aren't funneled into academic positions. That doesn't mean you need one to launch a successful career in data science, however. More job openings in data science call for candidates with master's degrees than doctorates, but some companies—especially those that specialize in data science and high-profile tech firms—do hire data science PhDs in greater numbers. If your dream is to work for a big-name technology company or in research and development, a data science doctorate can help smooth your path.
Something to consider as you research degree options is that the Master of Science in Data Science, or MSDS, is just one possible academic pathway for data scientists. People enter this field with graduate degrees in disciplines like:
Data science master's programs like UVA's MSDS are arguably the best option for aspiring data scientists, but you should also look into degrees like the:
The differences between online MSDS programs and data science master's programs on campus vary substantially from school to school. Some colleges and universities make no distinction between online and on-campus programs. Students choose from among the same specialization options, take the same core courses and elective classes, follow the same course schedule, study under the same professors, and complete the same number of credit hours of work. They may even pay the same tuition rate and have access to the same financial aid packages.
Other schools develop entirely new online master's programs in data science designed to accommodate distance learners' needs. These programs typically offer students more flexibility in the form of asynchronous courses, a relaxed course schedule, or more generous project deadlines.
This is why reading guides for specific programs (not just schools) is so important. Consider two programs: Stevens Institute of Technology's online MS in Data Science and the school's MSDS delivered on campus. Students in both programs take many of the same core classes, but the on-campus data science master's offers concentrations while the online MSDS offers a much more streamlined experience. Whether you'll get more out of one versus the other has less to do with delivery formats than with the content of individual programs.
At schools with strong programs, the answer is an unequivocal yes, for two reasons. First, students graduate with the same knowledge and skills regardless of which delivery format they choose. Students may not take the same courses or have to meet identical thesis, capstone project, practicum, or research requirements, but the goals of online MS in Data Science and on-campus data science master's programs are the same.
Second, the best online MSDS programs take steps to replicate the on-campus experience by giving students lots of facetime with faculty members and coordinating opportunities for students to connect virtually with one another and with industry leaders. Some higher education institutions host optional or mandatory immersion weekends or campus residencies for online MSDS students. Others encourage distance learners to come to campus for recruitment and other networking events. And many schools offer online students the same pre- and post-graduation career support that on-campus learners receive.
Ultimately, the value of any degree program is up to you. You can maximize the ROI of an online data science degree by taking advantage of all the networking opportunities, mentorship programs, recruiting initiatives, and career services support available—the same way you would in a program delivered on campus.
The baseline prerequisites you'll need to meet when applying to online MSDS programs will likely include:
Be aware that because data science master's programs are still relatively new and often quite small, admission can be very competitive. Admission requirements for online master's programs in data science vary by institution. Still, it's not unusual for students in programs at top colleges and universities to already be data scientists with letters of recommendation that speak to their professional skills.
Core courses and electives can vary significantly from school to school, but the curricula in most full-time and part-time MSDS programs share several common features. Students in nearly all data science master's programs tackle coursework focused on artificial intelligence, advanced programming languages, algorithm design, and data mining. Most programs are project-based and involve copious hands-on work.
The best way to get a sense of what you'll learn when you pursue a master's in data science online is to compare the courses offered in different programs. Students in the University of Virginia's online Master of Science in Data Science take courses that include:
Meanwhile, students pursuing Stevens Institute of Technology's online MSDS take:
And students at Drexel University pursuing the school's online Master of Science in Data Science take core and elective courses like:
As you do more research into what it's like to study data science online, where to pursue master's degrees for data science professionals, and how going to graduate school can improve your prospects, keep your career goals in mind. Some programs are designed for generalists, while others focus on specialty areas of data science. There are even MSDS programs for data scientists who want to transition into management. The right program for you will be the one that supports your aspirations.
People associate data science with the tech industry, but becoming a data scientist doesn't necessarily mean working in tech. While the technology industry is one of the top employers of data scientists, the finance sector employs more data science professionals than any other industry. Additionally, thousands of data scientists work for retailers, manufacturers, healthcare networks, insurance companies, pharmaceutical companies, and other types of employers, and in sectors as diverse as agriculture, entertainment, and waste management.
The companies with the largest data-related workforces are all tech firms, however, like:
The reason data scientists can be found in so many industries is that data science can use structured and unstructured data to answer so many different questions. It can predict whether a specific customer will click a link or a machine part will fail in the next month. It can determine whether images contain people and who those people might be—or generate images of people who don't exist. It can assess the mood of a tweet or identify the speaker in a recording. It can even make art.
Data scientists have many titles. After earning a data science master's online, you might become a:
Data science professionals, regardless of title, can earn anywhere from $96,000 to $123,000—or in some cases, even more. One recent Robert Half Technology Salary Guide predicted the median salary for data science roles in 2021 would be about $129,000. Experienced data scientists and data engineers can earn more than $200,000 if they land managerial roles at the big tech firms.
The ROI of an online master of data science is almost identical to that of an MSDS earned on campus. You'll learn valuable skills in a data science master's program, whether you study online or in-person. Your degree will help you advance farther, faster, and negotiate for higher salaries, regardless of how you earned it. And the best online master's degree programs are designed to give distance learners plenty of opportunities to interact with professors and peers, engage in group work, interface with industry leaders, and otherwise build their networks.
Consider, however, that not everyone in graduate school is there to schmooze. Most students in data science master's programs are already experienced professionals who know how to network. Some data analysts and data scientists enroll in MSDS programs to broaden their knowledge base or gain expertise in specialty areas of the discipline. Others pursue master's degrees because they need an MS on their resumes to move up the corporate ladder or land interviews at their dream companies.
There was a time when potential employers might look askance at your choice to enroll in an online degree program instead of an analogous program offered on-campus, but those days are over. Even before Covid-19 turned us all into distance learners, most hiring managers regarded master's degrees earned online as no different from those earned in traditional programs. If you're a busy professional with a family to care for or other personal obligations, enrolling in an online data science master's program is a smart way to approach career advancement.
You can keep working and building up experience while virtually attending class, completing assignments, and taking exams in your off-hours. Just keep in mind that some programs are more flexible than others. You might not have to attend classes on campus, but unless you choose one of the rare 100 percent asynchronous programs, you'll still need to log into your school's learning platform for live instruction and group project work at fixed times.
Only you can decide whether you'll be successful in an online data science master's degree program. Some students thrive in a flexible educational environment, while others do better with more structure. What you can be sure of, however, is that your decision to study online won't negatively impact your career. If you have the grades, the background, and the means to enroll in one of the top data science master's programs delivered online, your post-graduation credentials will open many doors.
You'll find some of the best online Master of Science in Data Science programs at:
The following schools are home to some of the most affordable online data science master's programs:
The affordable MSDS program by which all other budget data science degree programs ought to be judged is arguably the one offered by Georgia Tech. It's one of the top data science and analytics master's programs in the US but costs less than $10,000. You don't get a lot of TLC for that price, but you do walk away with a bona fide degree from a highly regarded institution.
You may be asking yourself whether you really need a master's in data science to succeed in the field. There are hundreds of Quora and Reddit threads full of experienced data scientists arguing that data science degree programs are a waste of time and money. At first glance, they make a compelling case, but dig deeper and their arguments fall apart. They kicked off careers in the data science field after learning Python and completing a few MOOCs because they launched those careers a decade or more ago. They were new to the field when employers were desperate to hire data scientists after the Harvard Business Review called data science sexy. In the same 2012 article, the publication pointed out that there were no schools offering degrees in data science and "little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured."
Data science has changed a lot since then. In 2021, employers expect data scientists to have impressive credentials that justify their six-figure paychecks and deliver quantifiable ROI. If you're new to the field, you're going to need a master's degree at minimum just to get your foot in the door. And if your goal is to advance quickly, get used to the fact that the goalposts for data scientists will probably never stop shifting. You may find yourself back here in a few short years researching online data science PhD programs.
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