Data Science Major, B.S.
B.S. degree
Major Map
Why Study Data Science at Central Michigan University?
Data Science is the career of the future. The advancement of modern technology has transformed our world into a digital universe, where data is created at an astonishing rate. There is exponentially increasing professional demand for individuals with advanced knowledge and skills in data science that greatly outstrips the availability of graduates. Industries are becoming data-driven and new innovations are being made every day. In the modern advanced technological society, the new driving force behind industries is Data. Companies require data to function, grow and improve their businesses. Data Scientists deal with the big and messy data in order to assist companies in making proper decisions.
The program is designed to equip students with knowledge and skills in data management, computing and analytics modeling techniques to meet the ever-increasing demand by business/industry for individuals with such training. Besides the knowledge and skills for solving real world data science projects, the program will equip students with strong communication skills required to work effectively with teams and clients.
Admission Requirements; Retention & Termination Standards
Students can sign a major at any time. They should contact a data science advisor in the Department to plan their coursework map. To progress through the program, it is important to get through some foundational courses such as MTH 132, MTH 133, CPS 180, STA 382QR and DAS 150QR as early as possible. The University requires that students have a signed major at 56 credit hours. To declare a major, students must have completed MTH 132, CPS 180 and an Introductory Statistics course, such as STA 382QR, with an average GPA of these three courses at 2.7 (B-) or better.
Students majoring in Data Science may choose one of the following options: 1) another major, 2) a minor different from the following analytics minors, or 3) an analytics minor in an application discipline described below. The option a student chooses should be signed with the corresponding department during the first or second year in the program.
These analytics minors along with the corresponding department are:
Program Requirements
Responsibility and Ethics (2 hours)
| CPS 301 | Social Issues of Computing and Professional Practice | 1(1-0) |
| DAS 260 | Data Integrity and Ethics | 1(1-0) |
Mathematical Foundations I (8 hours)
Mathematical Foundations II (3 hours)
Select one of the following:
| MTH 223 | Linear Algebra and Matrix Theory | 3(3-0) |
| MTH 232 | Linear Algebra and Differential Equations | 3(3-0) |
Computational Foundations (9 hours)
| CPS 180 | Principles of Computer Programming | 3(3-0) |
| CPS 285 | Programming for Data Science | 3(3-0) |
| ITC 341 | Introduction to Databases and Applications | 3(3-0) |
Statistical Foundations (9 hours)
| STA 382QR | Elementary Statistical Analysis | 3(3-0) |
| STA 580 | Applied Statistical Methods I | 3 |
| STA 581 | Probability and Statistics for Data Science | 3 |
Data Science Core Courses (15 hours)
Data Science Core Courses II (3 hours)
Select three of the following:
| DAS 251 | Data Visualizations and Programming using Tableau | 1(1-1) |
| DAS 252 | Data Visualization and Programming using R/RStudio | 1(1-1) |
| DAS 253 | Data Visualization and Programming using SAS | 1(1-1) |
| DAS 254/CPS 254 | Data Visualization and Programming using Python | 1(1-1) |
Electives (6 hours)
Select from the following:
| CPS 525 | Introduction to Text Mining | 3(3-0) |
| CPS 580 | Supervised Machine Learning | 3(3-0) |
| ITC 441 | Database and Virtual Data Server Administration | 3(3-0) |
| STA 575 | Statistical Programming for Data Management and Analysis | 3(3-0) |
| STA 582 | Experimental Designs | 3 |
| STA 583 | Nonparametric Statistics | 3 |
| STA 589 | Time Series Forecasting | 3(3-0) |
| STA 591 | Data Mining Techniques I | 3(3-0) |
| STA 595 | Introduction to Bayesian Statistics | 3(3-0) |
Total: 55 semester hours