Loyola University Maryland

Data Science

Master of Science in Data Science

What is Data Science?

Data Science is an evolving discipline that sits at the nexus of computer science, statistics, and business. Through data analysis, a data scientist identifies strategic and operational opportunities for organizations, whether they be Fortune 500 companies, small businesses, start-ups, government offices, or non-profits. Organizations need to derive value from available data. Because of this need, data scientists are in great demand, commanding up to six-figure salaries. According to glassdoor.com, data science positions are among the top for work-life balance and ranks it Number 1 on their "Best Jobs in America" list for 2019.

About the Master of Science in Data Science Program

Take your career to the next level with a Master of Science in Data Science at Loyola University Maryland. The master’s program provides the skills you need to become a data scientist. Students are prepared with programming skills in Python needed to transform messy web data into clean data and the knowledge to apply sophisticated statistical modeling using R, to address any data-driven problem. Leveraging Loyola's Jesuit background, the program also provides students the opportunity to examine the ethical implications of the work they will do as a data scientist. As part of the program, Loyola’s strong commitment to social justice encourages students to engage with nonprofits.

Loyola’s Master's in Data Science offers students a thorough data science educational experience through a 31-34 credit program. Most courses are offered in a hybrid format with a mix of in-class learning and online activities. This degree blends computer science and statistics courses with business courses graduating students with rigorous statistical and computational skills adaptable to any domain.

Program Format and Specializations

The M.S. in Data Science offers two specializations to choose from that may be completed in 31 or 34 credits. All students take courses from the data science core, which includes a blend of course from statistics, business, and computer science.

Technical Specialization (31-34 credits)

For the Master of Science in Data Science with a Technical Specialization, there are seven required courses: two statistics courses; two computer science courses; one elective from business, statistics, or computer science; and the research project sequence. Students with an appropriate programming/computer science background may waive the initial introduction to programming course. The required statistics courses develop students' modeling skills, and the computer science course exposes students to machine learning and artificial intelligence.

Data Analytics Specialization (31-34 credits)

The Master of Science in Data Science with a Data Analytics Specialization includes five required courses in business, statistics, and computer science followed by two computer science electives, and two free electives wherein a student may choose courses from any of the three core subject areas. This specialization adds flexibility, allowing students to focus on their area of passion or interest within the field of Data Science.

Both Data Science specializations culminate with a one-credit design course for the research project followed by a capstone research project conducted with a partner in local industry/government/non-profit. All students also participate in the two-semester practicum, which could include a summer internship. The practicum is an independent or group project that uses the data science techniques acquired during the program in an applied manner to solve a practical problem with a local partner. In the first semester, students design the project and present their plan to the program's board; this could be part of a paid internship. The program director will work to develop opportunities by developing a strong advisory board, comprised of industry, government, and not-for-profit representatives. In the second semester, students implement their project and present the results of the project to the board for approval.

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