Special programs, facilities, or equipment:
Programs of Emphasis:
Individualized Programs of Emphasis:
DS-110 Intro to Data Science (Fall; Yearly; 3.00 Credits; N) This course introduces the student to the emerging field of data science through the presentation of basic math and statistics principles, an introduction to the computer tools and software commonly used to perform the data analytics, and a general overview of the machine learning techniques commonly applied to datasets for knowledge discovery. The students will identify a dataset for a final project that will require them to perform preparation, cleaning, simple visualization and analysis of the data with such tools as Excel and R. Understanding the varied nature of data, their acquisition and preliminary analysis provides the requisite skills to succeed in further study and application of the data science field. Prerequisite: comfort with pre-calculus topics and use of computers.
DS-210 Data Acquisition (Fall & Spring; All Years; 3.00 Credits; N) Students will understand how to access various data types and sources, from flat file formats to databases to big storage data architecture. Students will perform transformations, cleaning, and merging of datasets in preparation for data mining and analysis. PRE-REQ: CS 110 and DS 110.
DS-352 Machine Learning (Fall; Variable; 3.00 Credits; N) This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in datasets from the simple tables through complex and big data sets. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Matlab, Weka and others. Simple visualization and data exploration will be covered in support of the DM. Software techniques implemented the emerging storage and hardware structures are introduced for handling big data. Prerequisite: CS 110, DS 110, and an approved statistics course: MA 220, BI 305, PY 214 or EB 211.
DS-500 Data Science Fundamentals (Fall & Spring; Yearly; 4.00 Credits) A graduate level introduction to data science through a focus on the language R. Support tools and libraries such as Rstudio and the tidyverse will be emphasized. Students will complete the data science boot camp(a weekend in person intensive or online equivalent) at the start of this online course.
DS-510 Computer Science Fundamentals (Variable; Variable; 4.00 Credits) A graduate-level introduction to Computer Science Fundamentals through a focus on the Python language. Students will complete the data science boot camp (a weekend in-person intensive or online equivalent) at the start of this online course.
DS-516 Mathematics Fundamentals (Variable; Yearly; 3.00 Credits) Selected topics of discrete mathematics and linear algebra related to data science analysis techniques and algorithms.
DS-520 Statistics Fundamentals (Variable; Yearly; 3.00 Credits) Overview of basic statistical techniques including descriptive statistics, hypothesis testing, and regression.
DS-525 Data Acquisition & Visualization (Variable; Yearly; 3.00 Credits) A graduate-level introduction to retrieving, cleaning, and visualizing data from widely varied sources and formats. The student will use common data science languages and tools for extraction, transformation, loading and visualizing data sets. Project presentations will have an emphasis on communication skills. Tableau visualization tools and Python libraries are used.
DS-570 Database Systems (Variable; Yearly; 3.00 Credits) This course focuses on database design and relational structures, data warehousing and access through SQL. Students will use SQL to create and pull data from database systems. NoSQL and data warehousing are also covered to give students the necessary background in database systems.