The Difference Between Computer Science and Data Science: A Guide for Undergraduate Students
February 6, 2024
Author
Nicholas Chung
UCLA College of Letters & Science
Grad 2021, B.S. Computational and Systems Biology
As an educational consultant, we often get asked by students and parents about the difference between computer science and data science, two fields that are often confused or conflated. While both fields deal with processing and analyzing data, they have distinct differences in education and career paths, skills required, and competitiveness in undergraduate study. In this blog post, we will outline the key differences between computer science and data science to help undergraduate students make informed decisions about their major selection.
Differences in Education/Career Path
Computer science is the study of the theory, design, and implementation of computer systems and algorithms. Computer science graduates typically pursue careers as software engineers, software developers, or computer programmers. They work on developing software, apps, and computer systems, and are responsible for writing code, testing, and debugging. On the other hand, data science is an interdisciplinary field that combines computer science, statistics, and domain expertise to extract insights from data. Data science graduates typically work as data analysts, data scientists, or data engineers, and are responsible for collecting, analyzing, and interpreting large datasets to inform business decisions.
In terms of education, computer science majors typically focus on programming languages, data structures, algorithms, computer systems, and software engineering. Data science majors, on the other hand, focus on statistics, machine learning, data visualization, and data processing. While computer science majors may take a few courses on data analysis, data science majors take a deeper dive into statistical modeling, data mining, and data visualization.
Differences in Skills in Industry
In industry, computer science graduates are typically expected to have strong programming skills, knowledge of software development methodologies, and experience with software development tools. They are responsible for building and maintaining software systems and are often part of development teams. Data science graduates, on the other hand, are expected to have strong analytical skills, knowledge of statistical modeling, and experience with data visualization tools. They work closely with business stakeholders to identify business problems and develop data-driven solutions.
Data science graduates need to be proficient in programming languages such as R, Python, or SQL, and be familiar with data visualization tools such as Tableau or Power BI. They also need to have strong statistical knowledge and be able to work with large datasets. Computer science graduates, on the other hand, need to be proficient in programming languages such as Java, C++, or Python and be familiar with software development frameworks such as Spring or Django.
Competitiveness in Undergraduate Study
Both computer science and data science are highly competitive fields, but data science is a relatively new field that has seen explosive growth in recent years. As a result, data science programs are often more competitive, with more stringent admission requirements and higher standards for academic performance. Computer science programs, on the other hand, have been around for longer and may have more flexibility in terms of admission requirements and academic expectations.
Skills Required (Languages, Workflow, Frameworks, etc.)
Computer science graduates need to be proficient in programming languages such as Java, C++, or Python, and be familiar with software development frameworks such as Spring or Django. They also need to have experience with version control systems such as Git and be familiar with agile development methodologies. Data science graduates, on the other hand, need to be proficient in programming languages such as R, Python, or SQL and be familiar with data visualization tools such as Tableau or Power BI. They also need to have experience with data processing frameworks such as Hadoop or Spark and be familiar with machine learning algorithms such as regression or clustering.
In conclusion, while both computer science and data science are exciting fields with strong career prospects, they have distinct differences in education and career paths, skills required, and competitiveness in undergraduate study. Undergraduate students considering a major in computer science or data science should carefully consider their strengths, interests, and career goals before making a decision. As an educational consultant, I encourage students to explore both fields, talk to professionals in industry, and take courses in both computer science and data science to get a better sense of which field is right for them.