If you’re looking to pursue a robust career in data science, a master’s degree can prepare you to take on advanced and highly complex responsibilities, from computer programming to data visualization and analytics. With the in-depth knowledge gained during your graduate-level studies, you will be able to help companies embrace emerging technologies like machine learning and automation while promoting cybersecurity and information ethics.
Once you start to make strides in your career, you’ll find that your interest in data science pays off. For reference, computer and information research scientists earn a median annual salary of $122,840. This is a 39% increase over the median earnings of all computer careers combined — and a 208% increase from the national average across all industries and occupations.
What is a data science degree?
A Master of Science in Data Science is a graduate degree that can prepare you for advanced roles in the computer IT sector and related fields that rely heavily on data analysis, such as business and finance.
Prepare for an expanding job market
A master's in data science can position you to enter the rapidly changing IT and data analytics industries. The degree can translate directly to the role of data scientist. However, you will have plenty of opportunities beyond this position — especially if you decide to specialize your degree with a concentration.
What jobs can you get?
A master’s degree in data science can help position you above the competition, while also preparing you for a variety of other roles in the computer IT field. Jobs that typically require a master's in data science include:
Computer and information research scientist
Machine learning engineer
Computer systems analyst
Business intelligence analyst
Chief technology officer
What is the job outlook for data science graduates?
There were over 30,000 data science job postings in 2019 and roughly 25% of entry-level opportunities called for a master’s degree. This dynamic occupation is expected to experience a 15% projected national change in employment between 2019 and 2029. This is nearly three times higher than the national average growth rate of 4% across all occupations.
Laying the foundation for a computer IT career
Your course curriculum will cover a range of topics, from data science theory to technical skills. You can expect to acquire and hone foundational knowledge, soft skills, and technical abilities while earning a master’s degree in data science.
While completing a master’s in data science, you will take core courses that address the theoretical and practical aspects of the field. Typical course subjects include:
Data science programming
Data mining and exploration
Information ethics and privacy
You will need to cultivate a variety of soft skills to thrive in a range of professional environments and in more advanced roles. The most sought-after baseline skills in data science include:
Written and verbal communication
Inductive and deductive reasoning
Planning and organization
Teamwork and collaboration
Beyond baseline skills, a data science curriculum will help you develop more advanced skills and technological competencies at the graduate level.
Technical skills and concepts
A master’s will prepare you with the technical skills and conceptual knowledge that employers value. In-demand skills and data science concepts include:
Natural language processing
Data lakes and reservoirs
Big data analytics
Business intelligence software
Internet of things (IoT)
Infrastructure as a service (IaaS)
Learning and becoming fluent in certain programming languages will prepare you for a variety of roles in the data science field. Widely used programming languages include:
Data visualization, processing, and analysis solutions
Throughout your graduate studies, you will become familiar with a variety of software solutions that support data visualization and analysis. These types of programs include:
Finally, experience with the following operating systems will help prepare you for a data science role:
Some programs will offer you the chance to choose a specialization through degree concentrations or electives. Pursuing one of the following academic paths while earning your master’s can prepare you for a role in data science or an adjacent industry that relies on similar skills and competencies:
Statistical analysis — With a statistics focus, you’ll gather large datasets and analyze them for patterns, probabilities, and other significant findings. By pursuing this type of concentration, you’ll build up computational skills, utilize statistical modeling programs, and more.
Risk assessment — Businesses rely on risk management experts who can translate big data into actionable recommendations. If you choose this as your focus, you’ll be responsible for things like identifying the risk of lending money to a prospective borrower and looking out for fraudulent activities.
Social sciences — If you position yourself at the intersection between data science and the social sciences, you can use a variety of techniques from machine learning to data modeling to answer questions related to society, culture, and human behavior.
Public policy — Merging your interests in data science and public policy means that you’ll present your findings to policy experts, helping to influence governance, and promote public welfare.
Business intelligence — With a focus on business intelligence, you’ll use a variety of data analysis methods and techniques, from artificial intelligence to statistical modeling, to help inform business decisions.
Enterprise data management — Enterprise-level companies collect and process a vast amount of data. By focusing on data management, you’ll help companies extract meaningful insights from various digital assets.
Cybersecurity — By specializing in cybersecurity, you’ll create data-backed solutions to cybercrime threats and issues, helping organizations spot malicious activities and protect their data from costly breaches.
Finance — In the finance sector, you can use data science methods to support risk analysis, data privacy measures, forecasting, and more.
Online vs. on-campus: What you can expect
Here are answers to some of the most frequently asked questions you’ll encounter when researching your options for a master’s in data science:
The course content in an online program will be very similar to — or the same as — the content you would cover in a campus-based program. However, the way this course material is delivered will vary, depending on the type of online program you enroll in. You can be sure that the quality of your online, remote education will be on par with the education you would receive on campus.
While you’re pursuing an online master’s in data science, you will likely participate in synchronous or asynchronous video lectures, exams, and assignments. You will engage with faculty members and student peers in a virtual classroom that makes use of resource hubs, discussion boards, and real-time video discussions. Some universities offer 100% online coursework.
Given the computer-oriented focus of many courses — from programming to cloud computing — the online format lends itself well to a data science degree program. For instance, you may have the chance to collaborate in virtual labs with other online students in your cohort.
No, the word “online” won’t appear on the diploma you receive. Your diploma will look just like one given to an on-campus graduate. It will feature the full program name along with other details like the university name, your full name, and your graduation year.
If you become a full-time graduate student, you may be able to complete your master’s in data science in as few as two years. Some universities may offer accelerated programs to help you earn your degree even faster. If you choose to study part-time, completing your degree could take three or four years or more, depending on university policies.
A part-time, online master’s degree program could be a great choice if you’re looking for flexibility while working to advance your career and meeting other important professional and personal obligations.
Professional associations in data science
Students and professionals who want to connect with other members of the data science field can join a membership organization. While involvement in a particular organization is typically not a job requirement, such an affiliation can offer a range of benefits. You’ll typically be able to attend networking events and conferences, support or lead teams and initiatives, keep up with industry news and best practices, and demonstrate your commitment to the field of data science.
Some relevant professional associations include:
Data Science Association
Association of Data Scientists
American Statistical Association
You have goals. We have a path.
The knowledge and experience you will gain while studying data science at the graduate level will be highly valuable to employers as the field continues to evolve.
As the world’s learning company, we proudly partner with universities to offer a suite of online graduate degrees in data science. Whatever your career goals are, we have a path for you.