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

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There are no true entry-level data science jobs. Why? Because this interdisciplinary subfield of computer science is so complex. Data scientists are programmers who write code to collect and clean data. They're also analysts, capable of using statistics to analyze information to answer specific questions. They have to be domain experts who understand enough about the topic in front of them to formulate compelling questions. And in many cases, they're also machine learning engineers who can build predictive models to visualize data in new ways.

Given that, it's unsurprising that successful data scientists tend to have many years of both academic training and professional experience. They also have many avenues of opportunity they can follow. The simple answer to the question 'What can you do with a master's in data science?' is you can become a data scientist. The more accurate answer is that there are numerous career paths for data science master's holders because there are many specialty areas of data science.

Tufts School of Engineering's Online Master of Science in Data Science (MSDS) can prepare you for a career in decision science and analysis, data architecture, data modeling, or data quality and governance. The program explores statistics and machine learning in-depth in courses focused on data infrastructure and systems, data analysis and interfaces, and the theory underlying it all. This guide looks at not only what you can do with the leading-edge skills and credentials you'll gain in an MSDS program, but also how this degree will impact your earning potential and make you a better data scientist.

Who typically gets a master's in data science?

MSDS programs are for students with professional experience in business, mathematics, computer science, or tech plus a background in mathematics and statistics. They often work in business analytics, business intelligence, data administration and management, software engineering, or data-driven fields such as finance or healthcare administration. Some are career-changers who are intrigued by the potential uses of data. Others are already data scientists in junior and mid-career positions looking to earn a higher average salary or transition into leadership roles. Still others are preparing to enter the PhD programs in which they'll launch careers in research. What they tend to have in common are strong math and analytical skills, an interest in new job opportunities, and an understanding of how an MS in Data Science will help them achieve their professional and personal goals.

As you research what you can do with a master's in data science, keep in mind that there is no universal definition of data scientist, data engineer, or data architect.

What will you learn in an MSDS program?

What you'll learn in an MSDS program depends on which program you choose. In the online MS in Data Science program at Tufts, you’ll learn to evaluate data to answer organizational questions, predict consequences, quantify risks, and avoid misinterpretations.

Core courses in Tufts' MSDS curriculum include:

  • Artificial Intelligence, which focuses on the history, theory, and computational methods of artificial intelligence.
  • Aspects of Data Analysis, which covers principal component analysis, algorithms in numerical linear algebra, unsupervised clustering and density methods, nearest neighbor classifiers, supervised methods such as support vector machines and neural networks, and spectral graph theory.
  • Big Data, which introduces the latest techniques and infrastructures developed for big data including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing.
  • Introduction to Machine Learning, which provides an overview of the various methods computers use to learn from data or experience.
  • Principles of Data Science in Python, which teaches common Python data structures and algorithms, the design of Python programs, coding standards and practices, and the use and creation of Python libraries.
  • Probabilistic Systems Analysis, which teaches advanced analysis in probabilistic systems with an emphasis on theoretical methods.
  • Reinforcement Learning, which covers the main theory and approaches of reinforcement learning (RL) and the common software libraries and packages used to implement and test RL algorithms.
  • Statistics, which teaches parameter estimation, convergence of random variables, properties of estimators, statistical tests and confidence intervals, and non-parametric statistics.
  • Stochastic Processes, Detection, and Estimation, which helps students develop basic analytical tools for the modeling and analysis of random phenomena and then apply those tools to a range of problems.

Students also complete an intense two-course capstone research project in which they identify a substantial data science challenge, address it to completion, and share their results with faculty and peers in a professional presentation.

How long does it take to get a master's in data science?

Many full-time data science master's programs take two years to complete, but Tufts School of Engineering delivers its 10-course, 32-credit hour MSDS for part-time students 100 percent online in less than two years. The university's program is every bit as academically rigorous as longer programs for on-campus learners, however. The difference is that Tufts built its part-time data science master's program around course content instead of timelines.

For an investment of just over $54,000, MSDS candidates receive expert real-time live instruction, robust career support, and opportunities to learn from high-achieving peers. Flexibility is a key feature of the program, so students can work full time and continue meeting personal obligations while earning a versatile career-enhancing master's degree that can take them in many different directions.

What can you do with a master's in data science?

There are many roles in data science beyond 'data scientist', and the list below is by no means exhaustive. Data scientists with master's degrees can do analysis, work on pipeline creation, design data infrastructure, focus on modeling, or supervise teams. They have titles such as:

Data architect

These professionals create blueprints for the complex data management systems that make data science possible. They typically have experience using multiple database systems and are comfortable with technologies including Hive, Pig, and Spark. Data architects are responsible for designing systems that integrate, centralize, protect, and maintain the integrity of data sources. Average annual salary: $121,000

Data engineer

Data engineers often come from a software engineering background because this is a highly technical role. They build the data pipelines organizations use to collect a large amount of data from multiple sources and transform it into a usable format for analysis by data scientists. Annual average salary: $93,000

Data science manager / data analytics manager

Data managers oversee data science teams and are heavily invested in the business applications of data. They're responsible for everything from data architecture and the flow of data to the coordination of people and must be comfortable with data science, data engineering, data modeling, data storage, and more. It's up to them to ensure data science teams meet organizational goals in a timely fashion without sacrificing the integrity of data. Average annual salary: $137,000

Data modeler

Data modelers design the blueprints for databases by determining which information is needed to support business processes and decision-making. They start with loosely organized lists and build logical models of how items should be organized within a database. Their goal is to create easily accessible and extensible databases that make it easy to manage the flow of information into and out of a database system. Average annual salary: $118,000

Data storyteller / data visualization developer

Professionals with these titles turn data into a cohesive, easy-to-understand narrative that stakeholders can use to make strategic decisions. They bridge the gap between raw data and human understanding by simplifying information into reports focused on specific insights. Average annual salary: $121,000

Data warehouse architect / data warehouse developer

Data warehouse architects build organizations' data storage and retrieval infrastructure. This is a highly technical role for data engineers with programming, SQL, and data management skills. The data warehouse architect's goal is to create a streamlined central repository for integrated data and a functional system for reporting and data analysis. Average annual salary: $125,000

Decision scientist

Decision scientist is an interdisciplinary role that involves equal amounts of mathematics, business, technology, and behavioral science expertise. These data scientists work on data analysis in the context of the decision-making process. Their analyses almost always relate to specific business questions posed by stakeholders and their findings help drive organizational decisions and strategy development. Average annual salary: $119,000

Machine learning engineer

Machine learning engineers develop self-running AI software capable of learning without human intervention. In data science, these specialists typically focus on the technical side of data collection and analysis. They productionize models using programming languages such as Java and Python, freeing up data scientists to focus on statistical analysis and the development of better models. Average annual salary: $113,000

As you research what you can do with a master's in data science, keep in mind that there is no universal definition of data scientist, data engineer, or data architect. The responsibilities associated with specific roles are dictated by individual companies, not set conventions. Positions with the same title may require different skills while positions that require the same skills may have different titles. Noah Gift, a member of the Forbes Technology Council, predicts there will be no data science job titles by 2029. The broad data scientist role will give way to roles such as AI wrangler, AI communicator, AI product manager, and AI architect thanks in large part to automation. Keep your search parameters broad when researching data science jobs before earning your master's and then again when searching for open positions. In the future, more titles will likely emerge as data scientists' responsibilities grow increasingly granular.

Are there jobs for data scientists with master's degrees outside of tech?

According to Diffbot's State of Data Science, Engineering & AI Report, the companies with the largest data-related workforces include Amazon, Apple, Facebook, Google, IBM, and Microsoft. That doesn't mean, however, that all data scientists who earn master's degrees go on to work for FAANG firms and other technology companies. Every business and organization that deals with large amounts of data can benefit from data science, which means an MSDS opens doors for data science professionals in retail, banking and finance, Big Pharma, healthcare, entertainment, and other industries. Many data scientists are domain experts who specialize in data warehousing, engineering, and analysis in a specific field, but some move between industries and work closely with subject matter experts when answering questions or testing hypotheses.

What you can do with a master's in data science changes to some degree based on which program you choose.

How an MSDS will advance your career

Going to graduate school for an MS in Data Science will not only give you the minimum academic credentials necessary to advance in data science but also signal to employers that you have the high-level skills necessary to move the field forward. The reality is that data science is a discipline in which advanced degrees are commonplace. Studies conducted by executive recruitment firm Burtch Works found that 90 percent of working professionals have advanced degrees and nearly half of all job postings require that applicants have master's degrees on top of experience.

Data scientists with master's degrees also earn more, though it's not always clear how much more because graduate degrees are common among data scientists. Very few salary aggregators track salary information for data scientists with bachelor's degrees, and chances are most salary averages for data science jobs are calculated using self-reported data submitted by professionals with master's degrees. What's clear is that the average data scientist probably earns between $100,000 and $120,000, while data science professionals in managerial and executive roles—e.g., director of data science or Chief Data Officer—can earn $200,000 or more with the right technical skills, soft skills, and credentials.

Access to opportunity and higher salaries aren't the only reasons to invest in an MSDS, however. A data science master's can advance your career by helping you meet goals related to your interests and aspirations. In a top-rated data science master's program, you'll learn not only new skills but also leading-edge and specialty skills. In fact, more data analysts and data scientists may be motivated to go to graduate school by a desire to grow than by money or the desire to advance more quickly. One Kaggle survey found that about 70 percent of data scientists with graduate degrees were motivated to enroll in their chosen programs by a desire to learn more and update their skills.

Why earn your online master's in data science from Tufts?

What you can do with a master's in data science changes to some degree based on which program you choose. Enroll in Tufts' 100 percent online MSDS program and you get the benefit of not only flexibility but also the university's well-deserved reputation for excellence. The interdisciplinary, collaborative curriculum is rigorous. In each of the program's 10 courses, you and your fellow students will build the analytic expertise you need to find actionable insights in data and guide high-level decision-making. Tufts purposefully keeps its online class sizes small—most have 15 students or less—to ensure students can work closely and develop beneficial relationships with world-class faculty and peers who work in senior software engineering, data science, or technology-focused leadership roles. That's important because data science is a collaborative discipline.

Tufts also ensures distance learners can take full advantage of the university's proximity to Boston, one of the top tech cities in the country. The School of Engineering maintains a Piazza board where faculty post research opportunities, internship opportunities, and information about scholarships, hackathons, career fairs, and opportunities for professional development. Master's in data science candidates also receive one-on-one guidance from the university's enrollment team, faculty mentorship, and career counseling. After graduation, online MSDS graduates can tap into the power of the elite Tufts alumni network and get career support and direction from the Tufts Career Center.

Everything about Tufts' online MSDS program is student-centered and designed to maximize the career-boosting power of this degree. It gives graduates all the tools they need to make a meaningful impact with data, regardless of title. Is it the right program for you? Review the program's admission requirements, information about the student experience, and faculty bios to find out. When you're ready, it's easy to apply, but if you still have questions, sign up for an upcoming enrollment event.

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