Data Computing (M.S. in Data Science and Business Analytics)
Analytics is a fast-growing STEM field with a high demand for individuals who possess the skills and expertise necessary to navigate the process of transforming data into insight for making sound business decisions. It's the reason that the WSU James and Patricia Anderson College of Engineering and the Mike Ilitch School of Business launched an innovative and interdisciplinary new master's program in data science and business analytics. Leaders in this field use data to fundamentally rethink all facets of business in many sectors, including manufacturing, supply chain, finance, and healthcare.
Admission Requirements
Admission to any graduate program is contingent upon admission to the Graduate School. Applicants must have earned an undergraduate degree in a STEM discipline, business discipline, or closely related field from an accredited college or university with a GPA of 3.0 or above. Please visit the program webpage for a complete list of admission requirements.
Prerequisite Knowledge
Applicants from a non-STEM discipline applying for the Data Computing major will need to have successfully completed Calculus I, Calculus II and Elementary Linear Algebra, or the course equivalent at another institution, and have completed significant coursework in computer programming.
Graduate Record Examination (GRE) and Graduate Management Admission Test (GMAT)
The GRE is not required for applicants who possess an undergraduate GPA of 3.0/4.0 or above (or the equivalent from a foreign institution). Applicants with a GPA that is below a 3.0/4.0 are reviewed on a case-by-case basis and are expected to provide a GRE score report that is no more than five years old.
Program Requirements
Students must complete a total of 30 credits in order to earn the M.S. in Data Science and Business Analytics with a major in Data Computing.
The interdisciplinary core includes 9 credits of coursework across business, computer science, and industrial engineering. On top of this integrated breadth of study covering the core areas of data science and business analytics, each student has 9 credits of major courses to give them depth in an engineering, business, or analytics area. Each student's 6 credits of elective choices can be personalized to support their individual career goals. The final piece of the curriculum is a 6-credit applied analytics practicum, in which students will work with companies and organizations on real analytics problems. All course work must be completed in accordance with the regulations of the Graduate School and the James and Patricia Anderson College of Engineering.
| Code | Title | Credits |
|---|---|---|
| Module 1: Core Courses | ||
| DSB 6000 | Data Science Strategy & Leadership | 3 |
| DSA 6000 | Data Science and Analytics | 3 |
| DSE 6000 | Computing Platforms for Data Science | 3 |
| Module 2: Major Courses | ||
| DSE 6100 | Data Modeling and Management | 3 |
| DSE 6200 | Modern Databases | 3 |
| DSE 6300 | Data Science Applications Development | 3 |
| Module 3: Electives | ||
| Elective courses can come from other tracks of the Data Science & Business Analytics program or from outside the program. | 6 | |
| Module 4: Applied Analytics Practicum | ||
| DSE 7500 | Data Science and Analytics Practicum | 6 |
| Total Credits | 30 | |
Electives
| Code | Title | Credits |
|---|---|---|
| ACC 7148 | ERP Systems and Business Integration | 3 |
| ACC 7280 | Accounting Data Analytics | 3 |
| ACC/TIS 7290 | Blockchain: An Accounting and Business Perspective | 3 |
| CSC 5050 | Algorithms and Data Structures | 3 |
| CSC 5250 | Network, Distributed, and Concurrent Programming | 3 |
| CSC 6800 | Artificial Intelligence I | 3 |
| CSC 6860 | Digital Image Processing and Analysis | 3 |
| CSC 7220 | Parallel Computing II: Algorithms and Applications | 3 |
| CSC 7260 | Distributed Systems | 3 |
| CSC 7300 | Bioinformatics I: Biological Databases and Data Analysis | 3 |
| CSC 7301 | Bioinformatics I: Programming Lab | 1 |
| ECE 7610 | Advanced Parallel and Distributed Systems | 3 |
| ECO 7100 | Econometrics I | 4 |
| ECO 7110 | Econometrics II | 4 |
| ECO 7120 | Econometrics III | 4 |
| IE 6010 | IoT and Edge AI Programming | 3 |
| IE 6325 | Supply Chain Management | 3 |
| IE 6720 | Engineering Risk and Decision Analysis | 3 |
| IE 7860 | Intelligent Analytics | 3 |
| STA 5830 | Applied Time Series | 3 |
| STA 6840 | Applied Regression Analysis | 3 |
| TIS 7505 | Information Analytics: Inbound Information Technology | 3 |
| TIS 7510 | Database Management | 3 |
| TIS 7570 | Advanced Business Analytics | 3 |
| TIS 7994 | Digital Content Development | 3 |
| TIS 7996 | Principles for Customer Relationship Management | 3 |
*Major courses from the other majors in the MS Data Science and Business Analytics program may also be used to satisfy the elective requirement.