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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 301Social Issues of Computing and Professional Practice

1(1-0)

DAS 260Data Integrity and Ethics

1(1-0)

Mathematical Foundations I (8 hours)

 
MTH 132Calculus I

4(4-0)

MTH 133Calculus II

4(4-0)

Mathematical Foundations II (3 hours)

Select one of the following:
MTH 223Linear Algebra and Matrix Theory

3(3-0)

MTH 232Linear Algebra and Differential Equations

3(3-0)

Computational Foundations (9 hours)

CPS 180Principles of Computer Programming

3(3-0)

CPS 285Programming for Data Science

3(3-0)

ITC 341Introduction to Databases and Applications

3(3-0)

Statistical Foundations (9 hours)

STA 382QRElementary Statistical Analysis

3(3-0)

STA 580Applied Statistical Methods I

3

STA 581Probability and Statistics for Data Science

3

Note: any of STA 282QR, STA 392, PSY 211QR, GEO 512, BIO 500, HSC 544 may be counted instead of STA 382QR to satisfy this course requirement.

Data Science Core Courses (15 hours)

DAS 150QRIntroduction to Data Science

3(2-2)

DAS 350Exploratory Data Analytics

3(2-2)

DAS 450Applied Analytics I

3(3-1)

DAS 460Applied Analytics II

3(3-1)

DAS 495Capstone/Practicum

3(3-0)

Data Science Core Courses II (3 hours)

Select three of the following:
DAS 251Data Visualizations and Programming using Tableau

1(1-1)

DAS 252Data Visualization and Programming using R/RStudio

1(1-1)

DAS 253Data Visualization and Programming using SAS

1(1-1)

DAS 254/CPS 254Data Visualization and Programming using Python

1(1-1)

Electives (6 hours)

Select from the following:

CPS 525Introduction to Text Mining

3(3-0)

CPS 580Supervised Machine Learning

3(3-0)

ITC 441Database and Virtual Data Server Administration

3(3-0)

STA 575Statistical Programming for Data Management and Analysis

3(3-0)

STA 582Experimental Designs

3

STA 583Nonparametric Statistics

3

STA 589Time Series Forecasting

3(3-0)

STA 591Data Mining Techniques I

3(3-0)

STA 595Introduction to Bayesian Statistics

3(3-0)

Total: 55 semester hours