Within the rapidly-expanding tech spheres, two of the highest growth, in-demand career fields are computer science and data science. Though the disciplines overlap, computer science and data science are unique in the types of people they attract and distinct career paths they offer.
Which is Better: Computer Science or Data Science?
So, which is better: data science or computer science, and which degree program should you pursue? At Rice University, we see a number of current and prospective students grappling with this question as they consider our Computer Science and Data Science undergraduate and graduate academic programs. With over 65+ years in Computer Science and a reputation for cutting-edge research in areas like AI and machine learning, we have a unique view into top employers' needs (both short-term and long-term), as well as how students' backgrounds and prerequisites, strengths and interests, and career/job placement vary across both paths.
First, let's review the core differences, and then consider which path and outcome might be best for you.
What is Computer Science?
Computer science is the older of the two disciplines and is considered the backbone of data science. Computer science is the holistic study of all aspects of computers with an emphasis on software, offering a “generalist” approach to topics ranging from software systems and algorithms, to AI and machine learning, databases and cybersecurity. Computer scientists hold a broad range of positions, tapping into their vast expertise on all facets of interconnected, modern computer systems and computational thinking.
What is Data Science?
Data science has now emerged as its own discipline, although statisticians, computer scientists and electrical engineers have been focused on drawing inferences from and managing data for decades. According to Rice’s Computer Science Chair, Chris Jermaine, “what has fundamentally changed--and is the reason we developed the Master of Data Science program at Rice--is the ubiquity of data.” With the rise of the internet, the world’s largest public database, and the proliferation of sensors in everything from airplane engines to household light bulbs, immense streams of data are being produced. With the right skills, this “big data” can be harnessed to identify patterns in large datasets, train more accurate algorithms, build predictive models to improve almost every facet of business strategy and operations, and much more.
Data Science vs Computer Science: Two Different Education Paths
First, which better describes you, your interests or your career intentions?
- A: I'm more interested in developing complex software systems, products, tools, and platforms that people and businesses will use to improve security, safety, ease, affordability, effectiveness, overall quality of life, and more
- B: I'm more interested in providing answers to big, strategically-important questions asked by companies, government entities, medical providers, and non-profits, helping them achieve greater effectiveness through data and technology
If you answered "A", you may be more drawn to computer science's emphasis on innovating, developing and implementing complex software systems, databases, products and services across a range of applications. For example, software engineers trained in computer science might design and build the "pipes" through which big data is transferred, so they must understand how complex systems facilitate the movement, use and storage of secure data.
If you answered "B", Data Science's focus on the end-to-end management of data to provide better answers to complex questions and make data-driven predictions may be right for you. As we'll discuss later, Data Scientists are part-computer scientists, part-mathematicians, so working with advanced technology, programming languages and statistical modeling factors prominently into this profession.
Computer science and data science both provide an opportunity to positively impact the world and make modern life easier. Simultaneously, both professions require close adherence to ethics concerns as society navigates complex issues like algorithmic bias, the explainability of AI, and an emerging workforce comprising both humans and machines.
Next, we'll discuss the distinct prerequisites, skills and knowledge required to pursue computer science vs data science careers.
The Computer Science Academic Path
Computer science focuses on a broader scope of computing and digital innovation than data science. It's an excellent choice for Engineering, Math, Physics and other technical career changers. We even see strong candidates with humanities and social sciences backgrounds (for example, Economics, Linguistics, Psychology or English), as long as they already have serviceable programming skills and a strong motivation to succeed.
Within industry, to be adequately prepared for common computer science career paths like software engineering or data engineering, prospective graduate students should look for computer science master's programs that offer a robust curriculum, with hands-on experiential learning in the following topics:
- Modern computer systems and complex software construction;
- Algorithmic models (basic and machine learning based);
- Core concepts that help you understand common syntax and code in multiple programming languages;
- Databases, cloud storage services, and big data;
- Deep understanding of cybersecurity motives, technologies, processes, and techniques
This background will cultivate well-rounded technical experts with the computer science knowledge to drive continuous innovation across multiple industries.
The Data Science Academic Path
Data Scientists are part-statistician, part-computer scientist, part-mathematician, well-versed in all of these fields and how they apply to the study and practical application of big data. At Rice, we see many Computer Science majors who pursue Data Science master's degrees, but Statistics, Math, Economics, Sports and Analysis-related majors (for example, Public Policy analysis) are also common.
Prospective data science graduate students need at least a basic understanding of data science programming languages like Python or R, and they'll further solidify these skills in a reputable data science master's program.
To be adequately prepared for a data science and analytics career, prospective graduate students should look for data science master's programs that offer a robust curriculum, with hands-on experiential learning in the following topics:
- Machine learning, and how data can enable machines to engage in the same inductive learning process that humans do;
- Big data and the software tools and math models required to work with massive datasets;
- Statistical modeling and analysis for computing and data science;
- Programming languages for data science; and
- Data security, privacy and ethics
At Rice, we saw an opportunity to offer specialization tracks in Machine Learning and Business Analytics, further reinforcing the practical application of Data Science concepts and skills across industries.
Data Science vs CS: Similarities & Differences in Career Functions and Job Titles
Within companies and organizations, computer science-trained software engineers, data engineers and data scientists frequently collaborate together, as well as with other cross-functional teams to drive value for their organizations. Though both computer scientists and data scientists work with advanced technologies, they apply them to accomplish different goals.
The Computer Science Career Path: What Do Computer Scientists Do?
Within companies, most professionals trained in computer science work in software engineering, data engineering, IT, cybersecurity or related careers. Most commonly, they ideate and define requirements, design software systems, oversee build by assigning coding work to programmers or developers, and engage in an iterative process of constantly testing, improving and evolving these systems.
Within academia, computer and research scientists engage in cutting-edge research to expand the frontiers of computing. For example, areas include deep learning, computational biology and bioinformatics, robotics and AI, and more.
With a breadth and depth of computer and software systems understanding, computer science-trained technical professionals' knowledge is highly transferable, opening up a range of diverse career paths across sectors. In a hot job market, before many students even graduate, master's students are often sought out by research & development (R&D) or innovation divisions at leading tech companies and corporate recruiters to work on projects ranging from autonomous vehicles and humanoid robots, to blockchain applications and machine learning algorithms to achieve new levels of personalization at scale.
What are Common Computer Science Jobs?
The most common jobs for professionals with computer science educational backgrounds are in software engineering and development, data engineering, IT, cybersecurity and technical product management.
For those who earn their Master’s in computer science, common job titles include:
- (Senior) Software Engineer: Some software engineering positions may require an advanced degree due to the advanced tech required (Deep Learning, Robotics, Computer Vision, Image Processing and Algorithms, etc.).
- (Senior) Technical Product Manager: Top companies like Google, Amazon, and Facebook often prefer an advanced degree in a technical field to enter into a complex, innovation-focused product management role. It can be very difficult for computer scientists even with a bachelor’s in CS to break into product management, so candidates should consider what will set them apart.
- (Senior) Principal Product Manager: Beyond breaking into product management, a master’s degree can help computer scientists enter senior leadership positions where they can drive innovation and work with a variety of business lines. Some senior-level positions may allow an advanced degree to substitute for a certain number of years of experience.
- Chief Technology Officer or Chief Information Officer: Experienced professionals who desire to become a CTO or CIO pursue a master’s in computer science to stay up-to-date on emerging technologies (e.g., Machine Learning/AI, Blockchain, Cloud Systems/Security) and broad, rapidly-evolving areas like Cybersecurity.
The Data Science Career Path: What Do Data Scientists Do?
Data scientists work with big data to identify actionable insights from massive datasets and make data-driven predictions. Their teams generally use the programs and software systems that computer scientists (or data engineers) create to collect, clean, structure, analyze and visualize big data. They may also program algorithms to query data for different purposes.
Data scientists are responsible for partnering with business functions like strategy, product, marketing and operations, adding value by analyzing and interpreting a huge volume of collected data to hypothesize, test and make data-driven recommendations. They use their programming skills to create models that allow them to interpret and track changes in data. Once they model the data and draw inferences or conclusions, they’ll use Python libraries or other data visualization tools like Tableau to create visual representations of the data itself so it’s accessible and usable to the company at large. This can be done for almost any industry and business sector. No matter what field data scientists work in, they typically need the advanced knowledge and training that comes from earning a master’s or PhD degree in the field.
What are Common Data Science Jobs?
Data scientists usually fall into two camps – those who know math really well but don’t code efficiently, and those who are great coders but don’t have a solid foundation in statistics. I can speak to both sides, so I am right in the middle –exactly where I wanted to be.
- David Sullivan, MCS Alumni, Data Scientist at New Knowledge. Read more about David’s data scientist career.
Master’s in data science careers include:
- Senior Data Scientist: These professionals focus on capturing, cleaning, structuring, analyzing, and visualizing big data to inform a range of applications: business strategy, investments, streamlined applications, effective marketing, and more.
- Machine Learning Engineer: Some Data Scientists may take on a more specialized role if they develop the skills required to drive adoption of a ML or AI solution end-to-end. An organization will often require a certification or master’s degree in a related field.
- Principal Data Scientist or Chief Data Officer: While a Ph.D. is often required, a master’s degree is sometimes acceptable pending the level of performance in other senior level data analyst/scientist roles. At this point of leadership in a company, a data scientist has to be tech-savvy and business-savvy. Principal Data Scientists are expected to understand business challenges, build strong internal and external relationships, and have the expertise and executive-level communication skills to help companies quantify organizational opportunities/trade-offs.
Which Pays More: Data Science or Computer Science?
While it depends on several factors including industry, company and geography, according to the Bureau of Labor Statistics, Computer and Information Research Scientists (an occupation commonly associated with senior technical roles within software engineering and computer systems management) made an average of $131,490 in 2021 and grew at 21%. Data Scientists & Analysts, according to the BLS, made an average of $100,910 in 2021 but was a faster-growing occupation at 36% projected growth through 2031.
In both occupations, more senior technical roles like Principal Data Scientist or Senior Software Systems Engineer typically prefer a master's or Ph.D. degree, although many entry- and mid-level roles accept bachelor's degrees or even bootcamp training.
Pay also varies by company, with the MANAMANA companies commonly paying generously to attract the best technical talent.
A Degree to Match Your Goals
Some universities offer Data Science as an MCS specialization, whereas Rice University is proud to offer both a Master of Computer Science and a Master of Data Science. These distinct programs are designed to best prepare students for the roles they want, while ensuring they can effectively collaborate with different functions over the long-term.
If you’re asking yourself, “should I pursue a master’s in computer science or data science?” the best thing you can do is consider where your interests lie. If you love ideating and developing innovative products and systems with your team, a master of computer science degree will align with those goals. However, if the thought of mining big data and identifying better answers to complex questions gives you a sense of fulfillment, pursuing a master of data science degree may be a better choice for you.
Rice graduates have gone on to become Data Scientists, Senior Software Engineers and Technical Product Leads at top tech companies, including Google, Amazon, Meta, Microsoft, JP Morgan Chase, and more. Request more information about both disciplines and see how the MCS@Rice and MDS@Rice programs can set you up for success in your career.