Data Science (DSCI)
The course provides students with a survey of both theoretical and practical aspects in the field of data science. Course topics include an overview of the data science field, data manipulation and flow, artificial intelligence and machine learning, testing, sorting, preparing and cleaning data sets, and cross-validation. Students will develop skills in relevant programming and scripting languages such as R and Python and be able to make inferences using results from data summaries. Course fee.
This course provides the student with the fundamental concepts and methods of statistical analysis while employing programming and scripting skills. Course topics include graphical and numerical representations of data, probability and data distributions, parameter estimation, and hypothesis testing. R programming language will be used to collect, prepare, and organize data throughout the semester. Students cannot earn credit for both MATH 216 and DSCI 102.
This course provides a foundation of database concepts. Topics include definitions and operations related to database systems as well as processes of database design. Students will be able to develop tables, forms, reports, and queries from a database. Entity-relationship (ER) diagrams and database normalization are also explored. Course Fee.
Prerequisite(s): DSCI 101
This course provides students with a study of the graphical representation of data and how to use visualization to aid understanding of big data for fields such as science, engineering, medicine, and the humanities. Students will learn how to design, build, and evaluate visualizations for different types of data, disciplines, and domains. The course emphasizes design and practical applications of data visualization. Course Fee: $20.
Prerequisite(s): DSCI 102