Master of Science (M.S.) in Data Analytics
College: College of Science and Health
Department: Mathematical and Computational Sciences
Student Type: Graduate Semester
Degree: Master of Science (M.S.)
Campus: Lisle Campus
Overview
The Master of Science in Data Analytics program at Benedictine University is designed to prepare students for careers in the rapidly growing field of data analytics. Through an industry-aligned curriculum, students will develop the technical and analytical skills necessary to process and analyze complex, large-scale data, build predictive models, and enhance decision-making across various industries.
The program is ideal for students from diverse academic backgrounds who seek to gain expertise in data analytics methodologies and applications. Graduates will be well-positioned for roles such as data scientist, machine learning engineer, data architect, data analyst, and business intelligence analyst.
Learning Goals for the M.S in Data Analytics Program
Graduates of the M.S. in Data Analytics program will:
- Develop advanced programming skills in languages such as Python and R.
- Apply mathematical and statistical techniques fundamental to data analytics.
- Analyze and visualize complex data sets effectively.
- Implement machine learning and data mining techniques in real-world scenarios.
- Design and execute large-scale data analytics projects and communicate findings clearly to diverse audiences.
Program Structure
The M.S. in Data Analytics program requires the successful completion of 33 semester credit hours, structured as follows:
- 3 credit hours of introductory courses (may be waived based on prior coursework).
- 21 credit hours of core courses covering essential data analytics concepts and techniques.
- 9 credit hours of elective courses in Business Applications.
Admissions Requirements:
To apply for the M.S. in Data Analytics program, students must submit:
- A graduate admission application and a $40 non-refundable application fee.
- Official transcripts from all previously attended universities/colleges.
- Students wishing to have DASC 5100 waived (likely Mathematics, Computer Science or Data Science undergraduate majors) should email transcripts to the Program Director for approval.
Curriculum:
| Code | Title | Hours |
|---|---|---|
| Introductory Courses (3 semester credit hours) | ||
| (May be waived if equivalent undergraduate coursework has been completed.) | ||
| DASC 5100 | Programming Fundamentals | 3 |
| Core Courses (21 semester credit hours) | ||
| DASC 5150 | Ethics for Data Analytics | 3 |
| DASC 5200 | Mathematics for Data Science | 3 |
| DASC 5300 | Advanced Programming | 3 |
| DASC 5320 | Data Analysis & Visualization | 3 |
| DASC 5383 | Machine Learning | 3 |
| DASC 5400 | Data Simulation, Bayesian Modeling, and Inference | 0 |
| DASC 6398 | Capstone Project | 3 |
| Elective Courses (9 semester credit hours-choose from the following courses) | 9 | |
| Business Analytics I: Predictive Analytics | ||
| Business Analytics II: Prescriptive Analytics | ||
| Databases and Data Warehousing | ||
| Operations Management | ||
| Business Intelligence | ||
| Total Hours | 30 | |
Students in the Master of Data Analytics program will achieve the following student learning outcomes (SLO):
Student Learning Outcome 1: Demonstrate a comprehensive understanding of the Python Programming language.
• University SLO: 5. Analytical Skills
Student Learning Outcome 2: Gain expertise in effectively communicating insights from data analysis.
• University SLO: 3. Communication Skills;
Student Learning Outcome 3: Demonstrate a strong understanding of algorithms applicable to data science.
• University SLO: 2. Critical and Creative Thinking Skills;
Student Learning Outcome 4: Develop a research question and design analysis related to that question.
• University SLO: 1. Disciplinary Competence and Skills;