Data science and computer science are two of today’s most impactful fields. In a fast-paced technological environment, we depend on data scientists to analyze a wealth of information and provide powerful insights. We also rely on computer scientists to create and optimize the applications and software that play such a fundamental role in our modern digital lives.
These fields are aligned but distinct. Their core differences influence various aspects, such as the unique skill sets expected of computer science and data science students or professionals. The data science vs. computer science debate can impact applications, highlighting when data scientists are best suited for certain business problems or when computer science experts are needed to create purpose-driven systems, applications, and solutions.
If you are analytical and a skilled problem-solver, you might be drawn to both data science and computer science, making it difficult to decide which field or degree suits you best. We will detail the differences between these programs below, demonstrating what you can do with a data science degree or computer science degree, and what sets these valuable programs apart.
Understanding Computer Science and Data Science
Driving exciting technological innovations that influence numerous sectors and communities, computer science and data science share much in common. This includes an emphasis on analytical thinking, along with a need for proficiency in programming languages and algorithm development.
Despite these similarities, computer science and data science are distinct fields that promise to solve different problems and may also appeal to different types of people.
Data science emphasizes analyzing a wealth of information and extracting actionable insights, all with the goal of driving informed decisions. Meanwhile, computer science takes a broader, more development-oriented approach, creating systems and algorithms meant to spark innovation or solve numerous computational problems.
We will provide a deeper dive into these distinct fields below, touching on how their fundamentals play into skill development and career opportunities.
Computer Science Explained
Computer science is an interdisciplinary, tech-oriented field that draws heavily from mathematics and engineering in the effort to solve complex computing problems. Britannica offers a simple, yet all-encompassing definition: “the study of computers and computing, including their theoretical and algorithmic foundations, hardware and software, and their uses for processing information.”
This field is broad yet dynamic, integrating core principles from several disciplines while also maintaining a clear focus on practical application and innovation. Computer science students explore a wide range of concepts, combining theories and applications to understand how computer systems are structured and how they process, store, and transmit information.
Core Focus Areas
As a diverse field, computer science encompasses several subcategories, several of which are also closely related to data science. Focus areas within the online master’s in computer science may seem diverse, but they share a similar emphasis on the inner workings of computer systems.
Meant to solve problems efficiently, step-by-step procedures known as algorithms use clear instructions to help computer systems complete desired tasks. Data structures provide purpose-driven formats for storing, organizing, and, eventually, accessing data within overarching systems. If arranged in a structured manner, this data can be more efficiently processed.
Computer architecture determines how computer systems are designed and structured. It determines not only the layout of various hardware components but also how they interact with one another and with software.
Applying engineering principles to software design and development, software engineering makes it possible to create scalable software solutions that can meet unique user needs but are also highly efficient. Offering an easy-to-navigate interface so that users can interact with computer systems, operating systems promote efficient resource allocation and strategic memory management.
Among the most cutting-edge computer science niches, artificial intelligence (AI) allows computer systems to perform a variety of advanced tasks without requiring human intervention. Machine learning (ML) takes this to the next level, allowing computer systems to ‘learn’ so that they do not need to be explicitly programmed for every new task or circumstance.
Networking and security are among today’s most important computer science subfields. They center on the design and organization of communication systems and the information they exchange. Networking reveals how computers and other devices are connected and how various components can work together to transmit information. Meanwhile, security solutions aim to safeguard those systems and the potentially vulnerable information they exchange.
What is Data Science?
Data science, like computer science, is interdisciplinary. However, it focuses on using analytics to uncover insights from data and make predictions or recommendations. SAS refers to the modern data scientist as a “new breed of analytical data expert who has the technical skills to solve complex known problems and the curiosity to explore what unidentified problems might need to be solved next.”
Core Focus Areas
Data science is sometimes referred to as a subset of computer science, and for this reason, the two disciplines share many focus areas. However, data science emphasizes analysis over development.
Statistics and probability are among the most important mathematical concepts in the overarching practice of data science. They are crucial for modeling uncertainty and driving predictions. A variety of statistical techniques help data scientists pinpoint trends or patterns and make data-informed decisions based on anticipated outcomes.
Advanced algorithms help data scientists leverage vast data sets to support data-driven decision-making. Data mining uses a blend of machine learning and statistical analysis to extract actionable insights from a wealth of information. Data visualization is just as important, translating complex findings into more intellectually accessible and intuitive formats, which, in turn, help data scientists convey new insights to diverse audiences.
As one of the most important subfields in modern data science, Machine Learning (ML) aims to mimic the power of human intelligence. This allows computer systems to ‘learn’ so they can complete tasks without requiring direct human intervention. ML is increasingly leveraged for automation purposes but can also enhance innovation across numerous fields.
Advanced frameworks such as Hadoop are uniquely equipped to handle vast amounts of data. Without these technologies, it would be difficult to truly leverage the power of big data, as increasingly large and complex datasets may be too complex for traditional systems to handle.
While advanced technologies can spark exciting innovations and significant improvements in productivity, they present unique ethical challenges. For instance, ML can introduce or amplify bias, while many data-driven solutions can prove problematic from a data privacy standpoint. Regulations and standards such as the General Data Protection Regulation (GDPR) play a huge role in shaping our current ethical understanding and best practices for ensuring data privacy.
Comparing Educational Pathways
There is much overlap between data science and computer science as they play out in the workforce. Those trained in one area can leverage their analytical skills to successfully transition to the other discipline. Still, there is great value in selecting a targeted degree program that hones in on whichever discipline aligns best with individual interests or career goals.
After all, while data science courses and computer science courses cover a lot of similar ground, differences will quickly emerge as key concepts are applied in simulations, case studies, or other immersive learning experiences. To help you choose the right degree program, we will provide a deeper dive into the computer science vs. data science discussion below:
Computer Science Degree Programs
Offering comprehensive learning opportunities that emphasize fundamentals like engineering mathematics and statistical principles, computer science degree programs help students develop a holistic understanding of the modern digital landscape. Although these programs emphasize core coursework through class categories such as theory, software, and systems, they also provide extensive opportunities to pursue specialized learning.
Data Science Degree Programs
Focusing on statistical techniques and advanced analytics, data science degree programs help students learn to navigate and interpret huge quantities of data and leverage this information to help solve complex problems. Real-world applications and problem-solving are heavily emphasized, with this degree aiming to prepare students for impactful roles in data science and analytics.
Career Paths and Job Responsibilities
While core concepts and course selection may play a central role in determining which degree you pursue, these concerns are clearly influenced by overarching career objectives. Both programs provide powerful preparation for the most exciting technological roles of tomorrow, but they delve into unique applications. Keep reading to discover the most common career pathways associated with the online MS in Computer Science and graduate-level data science programs:
What Can You Do with a Computer Science Degree?
If you like the idea of working in a tech-driven capacity but need additional time or knowledge to clarify your career goals, computer science could be a helpful program. This degree should provide a comprehensive overview of computing fundamentals but will also expose you to several niche practices.
Potential Careers
Due to the interdisciplinary nature of computer science, this degree has the unique ability to prepare students for a vast range of tech-oriented career paths. Among the most common include:
- Software Developer/Engineer. Tasked with designing applications that meet specific user needs, software developers and engineers leverage a variety of tools and programming languages to create innovative mobile apps, powerful enterprise systems, and everything in between.
- Systems Analyst. Bridging the gap between IT and business, systems analysts help shape software solutions that closely adhere to overarching organizational objectives. These professionals may also be committed to fostering greater efficiency through systems improvements, which they help promote by studying current systems and pinpointing their weaknesses.
- Cybersecurity Analyst. Determined to protect vulnerable data and systems from an ever-increasing array of cyber threats, cybersecurity analysts proactively monitor for vulnerabilities while also helping to develop layered strategies and systems that can limit the potential for unauthorized access or data breaches.
- Artificial Intelligence/Machine Learning Engineer. Increasingly prominent computer science roles involve AI and machine learning, with dedicated engineer models or algorithms that help systems operate without direct human intervention.
In-Demand Computer Science Skills
The inherently interdisciplinary nature of computer science means that students can expect to study advanced mathematics and engineering while also exploring diverse applications. Because computer science job responsibilities are so diverse, this field calls for a unique blend of technical and soft skills:
- Programming Skills. Today’s computer science professionals can expect to master multiple programming languages. Among the most important covered in graduate-level computer science programs? Python, C++, and Java.
- Analytical Thinking. Computer science professionals must be capable of breaking down complex information while identifying patterns and relationships between various concepts. This ability is crucial for everything from creating data models to debugging code.
- Understanding of Computer Architecture. An overarching understanding of computer architecture can provide a solid backbone upon which computer science professionals can expand as they explore the field’s many niches. Specifically, computer architecture explores the principles that support computer systems and allow components like the CPU and memory to interact with one another.
What Can You Do with a Data Science Degree?
Compared to the computer science degree, data science programs may seem highly targeted. This is true insofar as they take a deeper dive into data-driven concepts and solutions, but these are broadly applicable across many industries. In general, data scientist job responsibilities involve collecting and interpreting sizable amounts of data, but in a goal-oriented manner.
Potential Careers
A data science degree can prepare you for success in one of today’s fastest-growing fields. It is a great option for mastering data-driven solutions and developing the adaptability and agility needed to keep up with rapidly evolving technologies.
- Data Scientist. Building predictive models designed to analyze huge data sets, data scientists play a core role in developing the strategies and solutions that other data-driven professionals can leverage to make sense of complex data. These professionals must develop a deep understanding of machine learning and statistical methods.
- Data Analyst. Similar to data science but with a greater emphasis on data interpretation and visualization, data analysts help uncover the hidden stories within datasets and convey these key insights to promote informed decision-making.
- Machine Learning Engineer. Playing a central role in developing and implementing today’s most impactful AI-powered solutions, machine learning engineers develop and train models, striving for exceptional accuracy and performance.
- Business Intelligence Analyst. Drawing on the power of data to drive strategic decisions, business intelligence analysts form a critical bridge between data science and strategic management. They may create dashboards or visualizations to help uncover or convey important business trends.
Making the Decision: Which Degree Is Right for You?
With an understanding of what data science and computer science courses offer, along with the skills they develop and the career opportunities they provide, you can make an informed choice about your academic and professional path. Keep these considerations in mind as you select a degree program that aligns with your goals:
Assess Your Interests and Strengths
At first glance, computer science and data science programs may seem to cover similar ground. Both are highly analytical, tech-driven programs. That being said, there are definite differences, and chances are, you will be more drawn to the subject matter and experiential learning options for one of these programs.
If you’re fascinated by the inner workings of computer systems and show a strong aptitude for engineering, computer science may be the better fit. If you are intrigued by the power of data and eager to take a deep dive into huge data sets and associated algorithms, you could have a bright future in data science. Take a close look at the distinct curricula for these programs to help drive a decision tailored to your skills and strengths.
Consider Career Goals
Your interests and abilities may align closely with your preferred degree program and eventual career trajectory. For this reason, career objectives must be carefully integrated into the degree selection process. Take the time to understand available career paths and which specific skills are required for each specialty or position.
Next, determine whether these competencies are prioritized in your program of choice. Aim for a program that promotes adaptability, as you will need to continue embracing cutting-edge technologies long after you graduate.
Evaluate Educational Programs
In addition to taking a deep dive into the distinct curricula and course offerings for computer science and data science programs, you’ll want to evaluate these opportunities based on format, flexibility, faculty, and many other concerns. Consider not only which programs match your career goals but also which work best with your current way of life. Flexible online programs may be more appealing to working students, who strive for a strong work-life-school balance while also pursuing rapid skill development and, eventually, career advancement.
Learn More About Texas A&M University’s Online Master of Computer Science
Ready to make your mark in an opportunity-filled tech landscape? Prepare for a bright future and enroll in Texas A&M’s Online Master of Computer Science program. Offering engaging coursework in a highly accessible format, this graduate-level program will equip you with a diverse and in-demand skill set. Reach out today to learn more.