Data Science and Analytics
Learn More About Data Science and Analytics
Admission Requirements
None.
This program does not have specific admission requirements. Only admission to Kennesaw State University is required to declare this major.
General Education Core IMPACTS Curriculum Requirements Specific to This Major
M: MATH 1113 or higher.
T: MATH 1190 or higher in Area D1.
T: Select two course pairs from the following (8 Credit Hours): CHEM 1211/L, CHEM 1212/L,
PHYS 1111/L*, PHYS 1112/L*, PHYS 2211/L*, PHYS 2212/L*, BIOL 1107/L, or BIOL 1108/L.
* Students cannot take both PHYS 1111/L and PHYS 2211/L nor PHYS 1112/L and PHYS 2212/L.
Sample Classes
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DATA 3010: Computer Applications of Statistics
This is an intermediate survey course of computer-based statistical software applications in the analysis and interpretation of data. Topics include developing a proficiency in coding in multiple languages through quantitative applications. Software packages include the most in-demand statistical languages and packages in the marketplace. (e.g. Python, SAS, R)
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DATA 3300 | Data Science Ethics
As the field of data science and artificial intelligence continues to rapidly grow, so does the need for strong ethical guidelines. Throughout this course, students will learn the foundational ethical theories and frameworks, and the origins of ethics within data science. Students will use case studies to learn about the ethical dilemmas around the collection, management, and use of data, the use of models and algorithms, and the future of artificial intelligence and machine learning. Topics include Privacy, Informed Consent, Ownership, Security, Bias, Misinformation, Data Governance and Codes of Ethics.
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DATA 4330 | Applied Binary Classification
Common applications of binary classification include credit worthiness and the associated development of a credit risk score, fraud detection, and the presence of a disease. Students will learn to use logistic regression, odds, ROC curves, and maximization functions to apply binary classification concepts to real-world datasets. This course utilizes statistical coding software and students are expected to have an advanced knowledge of this software.
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STAT 4120 | Applied Experimental Design
Methods for constructing and analyzing designed experiments are the focus of this course. The concepts of experimental unit, randomization, blocking, replication, error reduction, and treatment structure are introduced. The design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial, and fractional factorial designs will be covered. Statistical software will be utilized.