Data Science and Analytics

  • Data Science and Analytics

    What is Data Science and Analytics?

    The Bachelor of Science with a major in Data Science and Analytics will provide a student with foundational mathematical, statistical, and computational knowledge, skills, and methodologies within the context of the ethical and professional standards of Data Science. A student will also complete at least 16 hours of courses in either a domain of expertise in data science and analytics or a minor to provide them a context in which to apply their data science abilities. Thus, the degree will enable the student to either begin a career in industry, government, or community and non-profit organizations in a range of domains, or pursue graduate study. 

    Students will begin the program by building a foundation in mathematics, statistics, computer programming, and algorithmic techniques.  They will then take 38 credit hours of data science core courses covering the fundamentals of data science, programming, machine learning, data mining, data science ethics, and communication. After completing the core, students will complete 6 credit hours of elective courses in data science and statistical learning. Students will also be required to take at least 16 hours in a suitable domain knowledge concentration to begin exploring an expert area of application.  The program will conclude with a required data science capstone course, in which the student will demonstrate overall knowledge of the discipline by completing a data science project, incorporating all the knowledge learned in the courses.  

    College of Computing and Software Engineering

    4-Year Suggested Program Map

    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

    • 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)

    • 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. 

    • 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.

    • 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.

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