This course introduces the concepts from differential, integral, and multivariate calculus essential for the study of data science. Elements of linear algebra, such as vectors, planes, and matrices, also included. Emphasis on computation and application.
This course introduces concepts from probability theory essential for the study of data science. Topics include probability spaces, Bayes’ Theorem, random variables, discrete and continuous distributions, specifically the normal distribution, and the Central Limit Theorem. Emphasis on computation and application.
This course is about mining knowledge from data in order to gain useful insights and predictions. From theory to practice, the course investigates all stages of the knowledge discovery process, which includes data preprocessing, exploratory data analysis, prediction and discovery through regression and classification, clustering, association analysis, anomaly detection, and postprocessing.
A second semester of data mining introducing tools and techniques necessary for mining large scale data sources. Prerequisite:DSCI 501
An introduction to basic scientific and statistical research methods when dealing with measurements of human and corporate activity. Students read and evaluate current research and translate their ideas into viable research projects. Topics include scholarly writing and presentation, descriptive research methods, quasi-experimental and experimental design, ethical issues, and analytical methods.
Thesis credit may be earned for significant work toward the writing of a master’s thesis. This thesis may be used to fulfill the culminating project requirement.
The practicum is an opportunity to directly experience the work of a data scientist or data analytics professional. It consists of project-based learning on a significant and contributory business objective in conjunction with practicing professionals in one of many appropriate industries. May be repeated up to 6 credits.