Focus on Majors: Data Science

With the widespread adoption of mobile phones and computers and the resulting huge quantities of data generated from these devices, there is now a demand for analysts to extract knowledge and insights from that data. It is now easier for organizations to learn about their users as they interact with the company’s website, social media, or mobile channels, all of which leave records that can be accessed and analyzed. Such datasets are information-rich and can help guide organizations to improve products and services. Accessing and analyzing this data requires trained data scientists, and demand for them is high. To address this demand, many colleges and universities have created programs specifically focused on data science and ancillary studies.

What is data science?

Data science is an interdisciplinary field. In this major, students learn to combine statistics, computer science, mathematics, information science, and knowledge about systems to pull and analyze data for a specific purpose. As this is a rapidly changing field, data science degree programs aim to teach students how to think about the field flexibly and to engage critically with new problems.

An undergraduate studying data science will start by building a basis of knowledge in mathematics, particularly calculus and linear algebra, and take several statistics and computer science courses. While computer science will provide the programming skills needed to extract data and use helpful software tools, a statistical background will provide students with the analytical skills needed to interpret data
scientifically. In addition to focusing on these core subject areas, many data science programs have specialized fields of study:

Machine Learning is focused on using data to make predictions. To do this, students learn the basics of neural networks and artificial intelligence to mimic how neurons in the human brain work together to solve problems and arrive at conclusions.

Data Analytics uses large data sets to find trends and answer questions. Data Analytics can be valuable to businesses and other organizations not only to improve decision making, but also to increase employee productivity and help create personalized customer experiences.

Inference uses experiments, predictive modeling, and A/B testing of data to predict outcomes. Inference typically requires additional courses in statistics.

Natural Language Processing (NLP) uses Machine Learning (ML) technology to enable computers to understand natural language as humans do. Whether the language is spoken or written, natural
language processing can use AI to take data, process it, and make sense of it in a way a computer can understand.

What careers stem from this major?

With an undergraduate degree, students can pursue junior-level roles as data analysts or data engineers at a wide variety of companies. While analysts focus on organizing and interpreting raw data, data engineers help build the systems analysts use. These careers can be found at both large and small companies in industries as diverse as healthcare, agriculture, technology, and consulting, to name a few.

Within the role of a data analyst, there are specialties such as risk management, market research, and business analysis. Risk management analysts tend to work for insurance companies or on risk prevention teams. Typically, the goal is to ensure maximum profit. Students may also become database administrators, focusing their skills on overseeing a company’s database.

To pursue higher-level positions or research-based careers, students may also choose to obtain a graduate degree, which can take up to two years or more while they attend full-time.

Photo by Anton Mislawsky: https://www.pexels.com/photo/close-up-of-machine-part-246753/