How Much Overlap is There Between Computer Science and Data Science?

How Much Overlap is There Between Computer Science and Data Science

Data science is a specialization of computer science that has become more relevant as greater amounts of data become available to researchers. The study of data science overlaps almost completely with computer science, and data analysts must have excellent programming and computer science skills to do their job effectively. Modern data science consists mainly of machine learning programming, which is a technique of artificial intelligence programming that uses statistics to obtain insights from large sets of data. Machine learning is also referred to as “black box programming” because developers are unable to see how it arrives at a result.


Data scientists must have professional programming skills in order to write machine learning code that can analyze large data sets. Software engineers must have much more advanced programming skills than data scientists because the job of a developer is to build applications for consumers and businesses to use for work and entertainment. The only type of software that data scientists need to develop is the basic AI code needed for machine learning algorithms, and these programs are tailored to specific use cases. Data scientists have a much more specific and modest programming objective than application developers. While software engineers and application developers typically write code in C++, Java and Objective C, data scientists focus primarily on Python, Ruby, and other scripting languages. The machine learning algorithms used by data scientists don’t require polished user interfaces or convenient software features, so data science majors typically don’t take the same software design courses that application developers take during college.

Statistical Analysis

A computer science degree program includes several math courses, and a data science specialization puts a particular focus on statistics. Machine learning algorithms use statistics to find solutions to problems that are used as program input. The programmers who write these algorithms are unaware of their inner workings because they are coded in such a way that the software “learns” how to find the desired result after thousands or millions of iterations. When the result has a probability above a specified threshold of being correct, the program stops processing the data and returns the result. The results returned by a machine learning algorithm are incorrect a certain percentage of the time, and the amount of time it takes to calculate the result depends on the accuracy of the calculation.

Job Outlook

According to the Bureau of Labor Statistics, data scientists can look forward to surging job growth over the next 10 years. Software developers can also count on excellent job prospects during the same time period. As the economy becomes more dependent on technology and AI algorithms become more reliable, both data science and computer programming professions are likely to increase in importance. Programmers with excellent math skills will be able to find jobs in data science, and data scientists with advanced software engineering skills will be able to find jobs as developers. It’s a good idea in general for anyone pursuing a career in data science to learn as much about software development as possible, and vice versa.

Job seekers with a strong interest in mathematics and computer science will have great employment opportunities in a wide range of industries. The overlap between computer science and data science is the perfect starting point for anyone interested in these subjects.

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