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Computer Science vs. Data Science

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Data science is a relatively new field, but it is already getting its time in the spotlight. Harvard Business Review named data scientist the sexiest job of the 21st century in 2012, and a well-cited McKinsey report predicted that there would be a need for more 150,000 deeply analytical data scientists and another 1.5 million managerial analysts by 2018. It is not just a buzzword anymore; it is, as the Economist notes, an entirely new global commodity more valuable than oil.

The field of computer science (CS) might feel a little overshadowed, with all the attention its offspring, data science (DS), is receiving. Even though data science utilizes many other fields of study, the entire discipline is built upon and processed through the infrastructure of computer science. What those on the inside understand, though, is that the two are part of a symbiotic relationship, with nuances to each that nurture the other.

CS vs. DS – Similarities, Differences, and Overlap

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 business. Modern data scientists typically have a 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 ScienceData 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:

  • Algorithms and complexity
  • Architecture and organization
  • Computational science
  • Discrete structures
  • Graphics and visualization
  • Human-computer interaction
  • Information assurance and security
  • Information management
  • Intelligent systems
  • Networking and communications
  • Operating systems
  • Platform-based development
  • Parallel and distributed computing
  • Programming languages
  • Software development fundamentals
  • Software engineering
  • Systems fundamentals
  • Social issues and professional practice

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:

  • Algorithm design
  • Analytical (computational and statistical) thinking
  • Concepts of projects and code management
  • Functions and basic coding
  • Programming concepts and data structures
  • Matrix computation
  • Mathematical foundations
  • Statistical and machine learning
  • Model building and assessment
  • Multivariate thinking
  • Algorithms and software foundation
  • Data curation
  • Knowledge transference (communication and responsibility)
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 are 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:

  • Artificial intelligence
  • Biocomputation
  • Computer and network security
  • Human-computer interaction
  • Information management and analytics
  • Mobile computing
  • Theoretical computer science

Data science touches several pillars of study and reaches across a variety of tangential domains. Some specializations include:

  • Application development
  • Healthcare
  • Bioinformatics
  • Business analytics
  • Computation
  • Statistical modeling
  • Genomic data science
  • Cybersecurity
  • Advanced machine learning
  • Robotics
  • Signal processing
Which occupations can students seek after receiving a degree?

A degree in computer science can prepare a student for any number of careers, including:

  • Database administrator
  • Computer programmer
  • Information security analyst
  • Network architect
  • Web developer

A degree in data science can prepare students for several careers, including:

  • Data scientist
  • Data architect
  • Data mining engineer
  • Database administrator
  • Business intelligence analyst
  • Statistician
  • Software developer
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:

  • Application development
  • Artificial intelligence
  • Forensic analysis
  • Machine learning
  • Mobile computing
  • Cybersecurity

Emerging occupations for data scientists often explore the diverse applications of data, such as:

  • Algorithms administrator
  • Healthcare informatics specialist
  • Genomic data scientist
  • Machine learning
  • Predictive analytics
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 lineComputer 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.

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