Program Overview

Data Science and Analytics, B.S.

The Bachelor of Science with a major in Data Science and Analytics provides 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.

Program Overview

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.

Program Structure

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

The College of Computing and Software Engineering offers the Data Science and Analytics, B.S. program. The college provides students with a comprehensive education in computing and software engineering, preparing them for careers in industry, government, and academia.

Admission Requirements

There are no specific admission requirements for this program. Only admission to Kennesaw State University is required to declare this major.

Recommended IMPACTS Courses

• Students should take MATH 1113 or higher.
• Students should take MATH 1179 or higher.
• Students should take two four-hour laboratory sciences in the Natural Sciences. Students may choose from:
  • CHEM 1211/1211L
  • CHEM 1212/1212L
  • PHYS 1111/1111L
  • PHYS 1112/1112L
  • PHYS 2211/2211L
  • PHYS 2212/2212L
  • BIOL 1107/1107L
  • BIOL 1108/1108L
  • Note: Students cannot take both PHYS 1111/L and PHYS 2211/L nor PHYS 1112/L and PHYS 2212/L.

Sample Classes

• 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).
• 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
    • Codes of Ethics
• 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.
• 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.