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Data Analytics (B.S.)

Learn to Answer Tomorrow’s Business Questions Today With Big Data

The ever-expanding digital world is constantly collecting valuable information from people interacting with technology, and also from devices communicating with other devices (“the Internet of Things”). As a student in the Data Analytics Program at Seton Hill, you will learn to use the predictive power of mathematics with the processing power of technology to discover hidden patterns in “big data” that provide high-level business insights. In the process, you’ll position yourself for a career in a fast-growing field that is a combination of computer science, mathematics and business.

Why the Data Analytics Program at Seton Hill University?

Our curriculum is structured in four parts: foundational courses, discipline-specific courses, three elective courses or a minor in an area of specialization, and an experiential-education component suited to each student's employment goals. Students also complete Seton Hill University's liberal-arts core curriculum.

  • Career Strength: Seton Hill university's Data Analytics Program will provide you with a strong foundation in data analysis, as it is designed with the intention that students could minor or potentially double major in a related area, such as accounting, finance, business management, mathematics or computer science. 
  • Communication Skills: By applying Setonian ideals of ethics into everyday business and organizational activities, Seton Hill University students are better able to effectively and professionally communicate the results of data analysis in a clear and concise way to nontechnical coworkers and business leaders - a key skill that will benefit you long after you leave the Hill to pursue your dream career.
  • Experiential Internship: In your junior year, you will complete an internship in a professional environment that closely simulates your career field, allowing you to demonstrate leadership, decision-making, management and teamwork skills, and apply the organization theory that you have learned and developed as a Seton Hill University student.

Program Mission

The mission of Seton Hill University's Data Analytics Program is to give you the ability to leverage skills in business, statistics and information technology that will prepare you to analyze big data sets and develop data-driven solutions for employers. By integrating Seton Hill's liberal-arts core curriculum with a variety of classes in mathematics, computer science and business management - while also putting a strong emphasis on the practical aspects of data analytics through hands-on classes and internships - we're providing you with the skills necessary to be a competent data analyst.

Careers in Data Analytics

Currently, the need for skilled data analysts exceeds the number of qualified candidates. A study by the McKinsey Global Institute projects that by 2024 the demand for data analysts in the U.S. will grow by 19%. Right here in Pittsburgh, approximately 89% of predicted annual openings through 2025 will require a bachelor’s degree, particularly in the areas of business and finance where technology and the rise of big data has driven strong growth for data analysts.

Faculty

Seton Hill University’s data analytics courses are taught by dedicated faculty with expertise in business, mathematics and computer science who will help you master skills such as:

  • An understanding of statistics needed to maximize the value of data.
  • Identifying the opportunities, needs and constraints of data usage within an organization.
  • Applying quantitative modeling, programming and data analysis techniques to solve real-world problems, communicate findings and effectively present results using data visualization techniques and software. 
  • Using new and emerging tools and technologies to analyze big data with cloud based data mining. 

Admissions & Aid

At Seton Hill University, we offer streamlined admissions requirements and a comprehensive aid program.

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Faculty

Jared Burns

Dean of School of Business and Technology; Associate Professor; Director of MSITM; Coordinator for Data Science, and Data Analytics