The data sciences minor from CBU teaches students how to work with data and make meaningful, data-driven decisions. Knowing these skills will give students an edge in the workforce as data has become the language of the world.

The data sciences minor requires the completion of the following 18 units.

Programming and Statistics Requirements

 

Complete one of the following:

Introduction to computer science. Covers problem solving methods and algorithm development; modern programming methodologies; and fundamentals of high-level block structured language using C++. Prerequisite: EGR 181 or MAT 115. (3 units; Fall, Spring, & Online)

InstructorStart DateDaysTimeLocation
EGR121-B
Shade, Karen S
01/08/2024 MWF 3:45 PM - 4:45 PM Engineering 301
EGR121-C
Shade, Karen S
01/08/2024 MWF 2:30 PM - 3:30 PM Engineering 119
EGR121-A
Shade, Karen S
01/08/2024 MWF 10:45 AM - 11:45 AM Engineering 119
EGR121-D
Shade, Karen S
01/08/2024 MWF 8:15 AM - 9:15 AM Engineering 119
EGR121-A
Shade, Karen S
09/03/2024 MWF 9:30 AM - 10:30 AM Engineering 107
EGR121-B
Shade, Karen S
09/03/2024 MWF 8:15 AM - 9:15 AM Engineering 107
EGR121-C
Shade, Karen S
09/03/2024 MWF 1:15 PM - 2:15 PM Engineering 107
EGR121-B
Shade, Karen S
01/13/2025 TTh 3:45 PM - 5:15 PM Engineering 104
EGR121-A
Shade, Karen S
01/13/2025 TTh 2:00 PM - 3:30 PM Engineering 104

In this course concepts of computer programming languages are presented. Attention is given to the common constructs of programming languages as well as a structured approach to program development. The programming language may change from semester to semester. (3 units; Spring)

InstructorStart DateDaysTimeLocation
CIS268-A
Corso, Anthony J.
01/08/2024 WF 9:30 AM - 10:30 AM Engineering ONLN

 

Complete one of the following:

An introduction to the primary statistical and probabilistic models used in the collection and interpretation of engineering data. The focus is on summary techniques, regression models, and application of the central limit theorem, confidence intervals, hypothesis tests, and analysis of variance. Prerequisite: MAT 245. (3 units; Fall/Spring)

InstructorStart DateDaysTimeLocation
EGR305-A
Kim, Seung-Jae
01/08/2024 TTh 2:00 PM - 3:30 PM Engineering 130
EGR305-C
Zhou, Ziliang
01/08/2024 TTh 10:30 AM - 12:00 PM Engineering 204
EGR305-C
Kim, Seung-Jae
09/03/2024 MWF 1:15 PM - 2:15 PM Engineering 130
EGR305-B
Knisley, Benjamin David
09/03/2024 MWF 3:45 PM - 4:45 PM Instructor OFFC
EGR305-A
Zhou, Ziliang
09/03/2024 TTh 2:00 PM - 3:30 PM Engineering 301
EGR305-A
Kim, Seung-Jae
01/13/2025 TTh 2:00 PM - 3:30 PM Engineering 130
EGR305-B
Zhou, Ziliang
01/13/2025 TTh 10:30 AM - 12:00 PM Engineering 204

Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics. (3 units; Fall, Spring, & Online)

InstructorStart DateDaysTimeLocation
STA144-A
Carothers, Linn E.
01/08/2024 MWF 12:00 PM - 1:00 PM BUS 252
STA144-B
Carothers, Linn E.
01/08/2024 MWF 1:15 PM - 2:15 PM BUS 203
STA144-A
Carothers, Linn E.
05/06/2024 - Online
STA144-C
Nielsen Hernandez, Michelle
09/03/2024 MWF 9:30 AM - 10:30 AM Mission Hall 125
STA144-B
Carothers, Linn E.
09/03/2024 MWF 1:15 PM - 2:15 PM Mission Hall 126
STA144-A
Carothers, Linn E.
09/03/2024 MWF 12:00 PM - 1:00 PM Mission Hall 126
STA144-A
Carothers, Linn E.
01/13/2025 MWF 12:00 PM - 1:00 PM TBA
STA144-B
Carothers, Linn E.
01/13/2025 MWF 1:15 PM - 2:15 PM Park Building 203
STA144-A
Carothers, Linn E.
05/05/2025 - Online
STA144-B
Carothers, Linn E.
06/30/2025 - Online

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations. Prerequisites: MAT 245, and one of the following: EGR 120, 121, or STA 144. (3 units; Fall)

InstructorStart DateDaysTimeLocation
STA310-A
Noh, Heewon Esther_AKA:_Esther_L
09/03/2024 TTh 12:15 PM - 1:45 PM Park Building 125

Upper Division Requirements

 

Complete one of the following:

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures. Prerequisite: EGR 120 or 121. (3 units; Fall)

InstructorStart DateDaysTimeLocation
EGR325-A
Corso, Anthony J.
01/08/2024 TTh 3:45 PM - 5:15 PM Engineering 104
EGR325-B
Clement, Larry W.
01/08/2024 TTh 2:00 PM - 3:30 PM Engineering 104
EGR325-B
Clement, Larry W.
09/03/2024 MWF 2:30 PM - 3:30 PM Engineering 302
EGR325-A
Clement, Larry W.
09/03/2024 MWF 1:15 PM - 2:15 PM Engineering 302

This course is a comprehensive introduction to data management in organizations. It establishes the data management foundation for the computing major. Topics include conceptual and logical data modeling, entity relationship and relational data modeling, and database design and implementation using the SQL programming language. (3 units; Online)

InstructorStart DateDaysTimeLocation

 

Complete one of the following:

The course provides an overview of the theoretical foundation and applied use of Geographic Information Systems (GIS). At the conclusion of the course, students will have working knowledge of GIS and their appropriate application in various disciplines and organizational settings. The historical development of innovative and collaborative geographic information tools that assist with managing the challenges associated with positional data will also be introduced. Students will demonstrate acquired knowledge via application of the fundamental and principal concepts of geographical information systems by completing a series of lab assignments and a culminating research project. Prerequisites: EGR 121 and CIS 265. (3 units; Fall)

InstructorStart DateDaysTimeLocation

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered. Prerequisite: STA 144 or MAT 245. (3 units; Spring, even years)

InstructorStart DateDaysTimeLocation
STA360-A
Noh, Heewon Esther_AKA:_Esther_L
01/08/2024 TTh 8:45 AM - 10:15 AM Yeager Center ONLN

 

Upper Division Elective Requirements

 

Complete 6 additional units from the following:

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistic course. (3 units; Fall)

InstructorStart DateDaysTimeLocation

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Fall)

InstructorStart DateDaysTimeLocation

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Spring)

InstructorStart DateDaysTimeLocation
CSC424-A
Corso, Anthony J.
01/13/2025 WF 8:15 AM - 9:15 AM Engineering ONLN