Major/Minor Data Science

Data scientists use analytical tools and techniques to extract meaningful insights from data. Data science leverages the fields of applied mathematics and computer science to interpret large datasets through new and innovative models, algorithms, and other processes. Using business logic, statistics, programming, and machine learning/AI models, data scientists turn large amounts of data into easily understandable summaries, especially visuals. As the amount of data collected and stored by organizations grows, data science is an increasingly important field; company leaders rely on data scientists to analyze raw data and create observations that guide their business decisions. The Academic Data Science Association speaks to the critical importance of the field: “Data science is rapidly becoming a new paradigm for research and discovery, integrating approaches from computer science, statistics, applied mathematics, visualization and communication, and many application domains.” At RMC, a major in data science requires the completion of 10 core courses, two electives (choose from select courses in biology, chemistry, criminology, economics, environmental studies, mathematics, physics, political science, psychology, and/or sociology), and a senior capstone course. Data science graduates enter a rapidly growing workforce with a multi-disciplinary understanding of topics like data analysis, statistical modeling, and machine learning, and how to help solve complex problems and drive data-driven decision-making across industries.

The U.S. Bureau of Labor Statistics reports the median pay for data scientists as just over $108,000 annually in 2023. More specialized roles, such as Big Data Engineers and Machine Learning Engineers, earn an average of $130,000 to $150,000+ per data collected by career website Indeed.com.

The BLS also reports that data scientists account for nearly 60% of the projected new jobs in math occupations from 2022 to 2032, so now is a great time to be studying data science.

While there is significant overlap between data science and computer science, data science is considered by many to be an offshoot or sub-science of computer science. In fact, some scientists consider data science to be the child of computer science and statistics.

Both fields seek to understand and utilize computers to solve real-world problems and leverage subjects like mathematics, logic, and more to do so. However, where computer science focuses more on the how of computers, data science leverages tools from computer science to more effectively analyze data and metrics.

Data science and data analytics are very similar in purpose, as both seek to analyze and interpret data to create actionable insights. However, where data analysts examine datasets using existing models and tools, data scientists are tasked with designing and constructing new processes for data modeling using algorithms, predictive analytics, and statistical analysis.

IBM further explains, “Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models, and develop artificial intelligence (AI) applications. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets.”