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Choosing a Master’s Degree: Data Science or Artificial Intelligence

Some universities offer master’s degrees in data science and artificial intelligence in the form of degree tracks, while other universities offer entirely dedicated degree programs for each of these fields. You might be wondering what the difference between these two fields is, and how to decide which one best matches your career goals.

Deciding which program to choose takes looking beyond degree title, and understanding what employers are really looking for: rigorous coursework experience and key skills, which both degrees can provide. Read on to learn more about each program, how to navigate overlap in coursework and job opportunities, and ultimately how to choose the right path for you.

Understanding Overlap and the Most Relevant Key Differences

Data science and artificial intelligence are nascent fields—quickly evolving in real time—and share much overlap in knowledge and skills both in the job market and graduate coursework and projects.

  • Coursework: Courses in both fields require students to possess similar technical background knowledge and expose students to foundational coursework in cloud computing and data management.
  • Projects: Students often use the same software libraries, like TensorFlow, to perform advanced analysis and optimization of large datasets.
  • Job opportunities: In the labor market, data and artificial intelligence scientists find employment in various industries, from healthcare to financial services, sometimes on the same teams.

Due to this overlap, the key factors you want to focus on when making your decision about a degree program are the quality and reputation of the institution, instructors, and curricula. Employers and doctoral programs look for candidates who have taken the most advanced courses, regardless of which program they attended.

From Theory to Application: Skills From a Data Science Master’s Degree

Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract insights from structured and unstructured data. This data is then applied across different domains to drive decision-making.

To become a data scientist, you’ll need to have a strong background in statistics and mathematics, as well as programming. Most graduate programs in data science expect students to possess an in-depth understanding of and advanced skills in programming.

Data science programs offer students a chance to develop a deeper understanding of the fundamentals of data science. Students develop a strong skill set in data cleaning, data analysis, data management and data visualizations. Additionally, students can take elective data science courses that allow them to specialize in particular areas of the field, including:

  • Algorithms
  • Big data
  • Business analytics
  • Data analytics
  • Data mining
  • Deep learning
  • Machine learning
  • Natural language processing

coursesIncreasingly, online master’s degree programs allow working professionals to obtain an advanced degree without a career pause. Students typically attend these programs part-time and asynchronously while receiving rigorous training in data analytics. Reputable universities such as The University of Texas at Austin (UT Austin) offer online master’s degree programs in data science that empower students to apply their learning directly to real-world problems.

Cutting-Edge Solutions: Skills From an AI Master’s Degree

The phrase artificial intelligence describes software and processes that aim to mimic human intelligence and a range of areas of study—machine learning algorithms, computer vision, natural language processing, robotics, automation, learning models, and other autonomous systems, such as self-driving cars.

Artificial intelligence uses algorithms to perform autonomous actions, as well as software engineering to develop cutting-edge solutions in research and business.

When pursuing a degree in artificial intelligence, students are exposed to core courses on computational learning, algorithm design and analysis, and software engineering. In addition, some programs allow students to select a concentration in one of several tracks, including:

  • Computer Vision
  • Intelligent Interaction
  • Knowledge Management and Reasoning
  • Machine Learning Techniques

Most degree programs offer specific courses on neural networks, intelligent systems, applied optimization, visualization, virtualization, and modeling.

Program Requirements: Technical Background and Prerequisites

To successfully enroll in a master’s program in data science or artificial intelligence, students must possess a strong mathematics, statistics, and programming background.

Sample Prerequisites for Data Science Programs Shared Requirements Sample Prerequisites for AI Programs**
Multivariate Calculus  Linear Algebra Computer Science
Linear Algebra Calculus Computer Science II for Data Scientists
Introduction to Statistics Statistics Data Structures and Algorithm Analysis
Statistical Modeling Programming Discrete Structures
Programming, including languages such as Python, R, and C++   Calculus II
    Linear Algebra
    Introduction to Probability
    Applied Statistics II
    Engineering Statistics and Probability

**Prerequisite requirements for AI Programs researched and summarized based on admissions requirements listed by several graduate programs in Artificial Intelligence in the US.

Most data science master’s programs require students to have a technical background and prerequisite coursework. Especially important are advanced programming skills. For example, rigorous programs that expect students to have a bachelor’s degree in statistics, computer science, mathematics, or another technical field. Before enrolling in such a program, students must also have completed coursework in:

  • Multivariate Calculus
  • Linear Algebra
  • Statistics
  • Statistical Modeling
  • Programming languages such as Python, Java, R, and C++
Artificial intelligence master’s programs require students to possess a technical background, as well as prerequisite coursework. Programs are typically rigorous and expect students to have a background in science, technology, engineering, or mathematics. Regardless of background, most students are also required to have completed relevant coursework in:
  • Computer Science
  • Data Structures and Algorithm Analysis
  • Discrete Structures
  • Calculus

  • Linear Algebra
  • Probability
  • Applied Statistics
Interchangeable Terms: What's the Difference?

People often use the terms data science and artificial intelligence interchangeably, even in job and department titles. Data scientists often work in artificial intelligence departments, and those with a degree in artificial intelligence often work in data science departments. While the field of data science uses artificial intelligence in its operations, it entails a broad spectrum of tools and analyses. Artificial intelligence implies more advanced modeling. Rather than focusing on the title of the degree a prospective candidate obtained, employers are usually looking for candidates with advanced coursework from reputable universities and high computational, optimization, and modeling skills.


layer“Data science provides more targeted and specialized training that may give an easier foothold for folks without a bachelor's degree in computer science, while still giving students depth of both theoretical knowledge and practical skills.”


If a student were interested in pursuing a Ph.D. or conducting further research in the field, I would certainly recommend they pursue an MS in AI,” said Greg Durrett, assistant professor in the Department of Computer Science, leader of the Text Analysis, Understanding, and Reasoning (TAUR) Lab and instructor for the master’s program at UT Austin. “The MS in AI would also open doors in industry as well; it's definitely not a purely academic degree. However, the MS in AI likely relies more on knowledge of computer science and math fundamentals. Data science provides more targeted and specialized training that may give an easier foothold for folks without a bachelor's degree in computer science, while still giving students depth of both theoretical knowledge and practical skills.”

Career Opportunities for AI and Data Scientists

Data and AI scientists are in demand in most industries. Data scientists tend to be highly educated, with 88% possessing a master’s degree and 46% a doctorate degree. To get hired in high-paying roles, you typically need an advanced degree. Below, we provide some of the most common job titles and salaries for professionals who obtain a master’s degree in data science or artificial intelligence.

Job Title Salary*
Artificial Intelligence Engineer $156,651
Artificial Intelligence Researcher  $124,841
Artificial Intelligence Scientist  $134,498
Applications Architect  $126,726
Business Intelligence Developer  $90,383
Data Analyst  $68,336
Data Architect  $116,624
Data Engineer  $110,905
Data Scientist  $115,353
Enterprise Architect  $148,047
Infrastructure Architect  $125,977
Machine Learning Engineer  $128,832
Machine Learning Scientist  $135,195
Software Engineer- Artificial Intelligence  $108,694
Statistician  $84,467

*Data collected from Glassdoor

Matching Industry Needs: How To Choose the Right Program

Data science and artificial intelligence are the leading technologies of the day. Much of the industry uses these terms interchangeably, with job titles and descriptions often including both terms.

So, how do you decide which master’s program to choose?

When making your decision, choosing the right program should depend on factors beyond the program title. Some of the most important factors to include in your decision are:

  1. Program Reputation: To position yourself as a competitive candidate in the industry, you must choose a reputable master’s program. While many programs offer data science and artificial intelligence courses, you’ll want the program you choose to have an established reputation of rigorous coursework, expert faculty, and a strong network.

  2. Faculty Reputation: Learning directly from expert faculty is invaluable. Your instructors will likely also be writing your letters of recommendation for further studies or job positions, so ensuring you work with reputable experts can go a long way in setting you up for success.

  3. Rigor of Curriculum: Scoring a job after graduate school will depend very heavily on the quality and rigor of your curriculum. Most hiring managers are looking for candidates with significant experience with high-level computation, modeling, and optimization. More than the specific title of the program you attended, the depth and rigor of your coursework will likely determine the role you end up in upon graduation.

  4. Potential Projects: In addition to advanced coursework, employers will be interested in the projects you worked on during graduate school. Therefore, choosing a program that enables you to build innovative solutions will provide long-term value.

  5. Networking: Finally, one of the most important factors to consider when choosing a graduate program should be the potential networking opportunities. A strong network will set you up with great connections when you enter the job market, as well as a network of peers who can help you continue learning and innovating.


Your Next Step: Explore Top Programs

Choosing the right data science or AI master’s program relies on digging into curricula. To start your exploration of top programs, consider UT Austin’s top-ranked Master of Science in Data Science.

UT Austin’s data science master's degree offers students an opportunity to gain high-level skills in data analytics, optimization and visualization through a highly flexible and affordable program. Students enrolled in the program gain in-depth knowledge about data science directly applicable to real-world problems.

Fill out a Request for Information form to receive more details about UT Austin’s rigorous program, expert faculty, and admissions requirements.

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