Computer scientists and data scientists have overlapping skills. Each utilizes computational processes. A working understanding of programming languages and algorithms is a must in both fields, but what one does with that understanding is the primary differentiation between the two tracks. Computer science focuses on the “how,” while data science looks at the “why.”
Computer scientists work on the nuts and bolts of computational processes. Usually studying under the school of engineering, CS students architect networks and databases through which data can flow. While much of computer science functions in algorithmic principles, it does so with a specific logical outcome in mind.
In data science, the algorithmic principles are applied to greater areas of uncertainty, often producing probabilistic answers to interdisciplinary questions about the business. Modern data scientists typically have proficiency in computer science, but they can come from mathematical, statistical, or even business backgrounds.
Working on top of what computer science has built, data scientists design unique ways to filter through the massive amounts of data that flow through network systems and then extract actionable insights. Those actionable insights can make a business more efficient and effective; they can widen the world’s understanding of health sciences; and they can even filter back into computer science to create better datasets and customer experiences.
The relationship between computer science and data science is growing increasingly symbiotic, even as each of them evolves. Read on for a side-by-side comparison of the differences and similarities between these two fields of study.
| Computer Science | Data Science | |
|---|---|---|
| How do the fields define and differentiate themselves? | Computer science encompasses all aspects of computational systems, from theory to application. Computer scientists work with algorithmic processes and data and can explore both concrete and abstract lines of thinking, across a broad spectrum of applications. Computer science produces tangible results. | Data science is an interdisciplinary field of study that utilizes scientific processes, methods, algorithms, and systems to extract insights from massive quantities of data. Data science applies elements of CS to the business world with applications in business analytics, business intelligence, statistics, and scientific research, among other fields. |
| What department is the program typically part of within the educational institution? | Undergraduate computer science programs are often housed within the engineering department at universities. | Undergraduate data science programs are often nestled in with computer science, but also can be housed within the departments of mathematics or engineering. |
| Which topics do students focus on in pursuit of this degree? |
Students study a variety of topics with a primary focus on the following specific areas outlined by the Association for Computing Machinery and the IEEE-Computer Society:
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The discipline of data science is still emerging, and so is a consensus about curriculum standards. The most robust attempt to date has been put forth by the Annual Review of Statistics and Its Applications, where the suggested framework includes:
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| Is hardware training part of the curriculum? | While computer science programs provide a basic understanding of the interactions between hardware and software, specific training in hardware and its development is often not part of the curriculum. | While data science programs necessitate an understanding of the basic interactions between hardware and software, specific training in hardware is often outside the purview of data science curriculums. |
| Is algorithmic training part of the curriculum? | Accredited computer science programs will include courses on algorithms as a fundamental feature. | Data science programs will include algorithmic training and applications as a core feature of their design. |
| Which specializations are available in pursuit of this degree? |
As information technology grows more ubiquitous, the number of specializations available to computer science students increases. Some specializations include:
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Data science touches several pillars of study and reaches across a variety of tangential domains. Some specializations include:
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| Which occupations can students seek after receiving a degree? |
A degree in computer science can prepare a student for any number of careers, including:
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A degree in data science can prepare students for several careers, including:
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| Which emerging occupations can students seek after receiving a degree? |
New roles are emerging for computer science graduates practically every day, particularly in the areas of:
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Emerging occupations for data scientists often explore the diverse applications of data, such as:
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| Schools that offer online graduate degrees in these fields |
Computer science, as a field, lends itself well to hybrid and online programs, such as those available at: |
Data science is in its infancy, especially at the undergraduate level, but many programs are available, including: |
| Bottom line |
Computer science is a broad field of study that incorporates all applications of computational processes. Computer scientists may develop applications, write new programming languages, or architect a system that produces and sorts a flow of data. But for a computer scientist, those processes are often grounded in the symbolic logic of voltages to bits with predictable outcomes, and any probabilistic framings are often built over discrete logic-based elements. This is a nuts and bolts discipline that is building out the infrastructure of the 21st century. |
Data science is a relatively new field, the offspring of statistics and computer science. Data scientists may design algorithms, refine data sets, and parse vast swaths of data through mathematical models that yield actionable insights. To do so, data scientists must take an interdisciplinary approach and embrace uncertainty, incorporating not only computer science, but also statistics, mathematics, business, and communication skills. If computer science is the building of new infrastructure, then data science is the extraction and filtering of the new and valuable global commodity it enables. |
Arizona State University’s School of Computing and Augmented Intelligence offers an online master of computer science (MCS) program in partnership with Coursera—a U.S.-based massive open online course provider. Students in this program gain a solid understanding of advanced topics such as big data, cybersecurity, and AI. the program also includes real-world projects helping students in strengthening their skill set.
Students will also have two concentration options to choose from: big data systems or cybersecurity. Comprising 30 credits, the program includes courses such as knowledge representation and reasoning; foundations of algorithms; mobile computing; distributed and multiprocessor operating systems; information assurance and security; applied cryptography; advanced computer network security; and software security.
Applicants to the program must have programming knowledge in a variety of languages such as Python, HTML, and C/C++ Java. They must possess an undergraduate degree from a regionally accredited university with a minimum cumulative grade point average of 3.0. Additional requirements include a statement of purpose or a current resume, official transcripts, and English proficiency scores for international students.
Graduates of the program will be ready to take up roles such as computer network analysts, computer programmers, computer scientists, computer systems analysts, database administrators, information technology managers, and software developers.
Columbia University’s online master of science program in computer science can be completed entirely online. This program is ideal for those who wish to deepen and broaden their understanding of computer science. Concentration options include computational biology; foundations of computer science; computer security; machine learning; network systems; natural language processing; vision, graphics, interaction, and robotics; and software systems.
Made up of 30 credits, the program includes courses such as network security; operating systems; computer graphics; computational genomics; advanced software engineering; computer animation; biometrics; networking laboratory; introduction to quantum computing; and computer vision, among others. All concentrations have different courses.
To get accepted into the program, applicants must have an undergraduate degree in computer science or any other related field with a minimum grade point average of 3.3. Application requirements include three recommendation letters, official transcripts, a personal-professional statement, and a current resume. IELTS or TOEFL scores are required of students who have not earned a degree from an institution in which the instructional language was English.
The Whiting School of Engineering at the Johns Hopkins University offers an online master of science degree in data science providing students with a solid foundation of data science and preparing them for a variety of specialized careers such as statistical analysis, storage, and data pipeline. The faculty of the program includes practicing engineers and data scientists.
Applicants to the program must have a bachelor’s or graduate degree from a regionally accredited university or college with a minimum grade point average of 3.0. Application requirements include a completed online application, official transcripts from all post-secondary institutions attended, and a detailed resume. Proof of proficiency in English through TOEFL or IELTS scores is required of international applicants. GRE scores are not required for admission.
As part of the program, students will delve into topics such as principles of database systems; data visualization; introduction to optimization; statistical models and regression; data science; advanced machine learning; and introductory stochastic differential equations with applications.
University of California, Berkeley
The University of California, Berkeley School of Information offers an online master of information and data science (MIDS) program prepares students to become leaders in this field. The program also involves a three to four-day in-person immersion experience where students will be able to connect with their professors and classmates. However, due to the impact of Covid-19, immersion experiences are currently being held virtually.
Admission requirements to the program include a bachelor’s degree from an accredited college or university, a 3.0 GPA, official transcripts, a statement of purpose, GRE or GMAT scores (waivers available), two letters of recommendation, TOEFL scores for international students, and considerable work experience.
Consisting of 27 credits, the program’s multidisciplinary curriculum includes courses such as introduction to data science programming; research design and application for data and analysis; experiments and causal inference; deep learning in the cloud and at the edge; statistical methods for a discrete response, time series, and panel data; and data visualization.
On successful completion of the program, graduates can take up roles such as business data analysts, data architects, data analysts, data engineers, data scientists, solutions architects, and systems engineers.
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineeri...
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