The Master in Data Science Program at Maryville University offers a 36 credit hour program (12 courses) leading to the Master of Science in Data Science. The 12 required courses of the program are spread across four semesters, with three courses taken each semester. Eight courses of data science (DSCI) at 500 level or above are required. The program allows for possible completion in three semesters. Courses are offered mainly in the evening and weekend to accommodate part-time working students. Courses offered jointly with undergraduate students are mainly in the day.
With an emphasis on experiential learning, the Master of Science in Data Science program incorporates experiential learning from current or previous professional positions as an integral part of the active learning curriculum for each graduate course and for the overall program objectives.
Accelerated Master of Science in Data Science
This unique option enables the Maryville undergraduate majoring in data science, mathematics and sciences to complete the master’s program in just one additional year. In this option, four courses (12 credits) taken during the senior year as part of the undergraduate curriculum also count toward the master’s degree. The students complete 24 credits in the fifth year of study to complete the requirements of the master’s degree. Students with 60 undergraduate credits, at least 30 at Maryville University, and GPA 3.6 can apply the early Accelerated Master’s Option. No GRE or TOEFL is required for Maryville students entering the accelerated master’s program.
Data Science as a Career
The data scientist combines business, statistics, artificial intelligence (AI) and machine learning to advance human capabilities across various professional fields (business, engineering, health, social sciences, computer science, etc.). Data scientists work in all kinds of roles from large corporations to lean startups. Data scientists might help policy makers develop better decisions, or they might work in finance or healthcare. Data scientists are technically proficient and often use a number of computer code languages to compile and synthesize data. A background in advanced statistics and intermediate to advanced levels of programming skill are required. Depending on the field, data scientists also have to be creative to interpret and visualize data expressions.