Advanced Analytics (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 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 should have 3.0 or higher cumulative undergraduate g.p.a.
Prerequisite Knowledge
Candidates are expected to well-versed in basic probability and statistics and also familiar with some programming language. Courses will be available in the summer months for admitted applicants to refresh their knowledge or makeup for any deficiency in this knowledge.
Students without this prerequisite knowledge but otherwise possess good credentials will be given conditional admission and have to take this remedial coursework in the summer months prior to starting the program in the fall term
Graduate Management Admission Test (GMAT) and Graduate Record Examination (GRE)
Applicants must complete the GRE or the GMAT with minimum scores in the top 75 percentile.
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 Advanced Analytics.
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 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 | ||
| Choose two courses from the following list: | 6 | |
| Introduction to Machine Learning and Applications | ||
| Deep Learning | ||
| Data Mining: Algorithms and Applications | ||
| Intelligent Analytics | ||
| Choose one course from the following list: | 3 | |
| Statistical Learning for Data Science and Analytics | ||
| Operations Research | ||
| Decision Analysis and Simulation | ||
| Module 3: Electives | ||
| Elective courses can come from other tracks of the Data Science & Business Analytics program or from outside the program. (See the list of approved electives below.) | 6 | |
| Module 4: Applied Analytics Practicum | 6 | |
| Data Science & Analytics Practicum | ||
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 5220 | Fundamentals of Software Testing | 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 |