DA101 - Introduction to Data Science

In this course students receive an introduction to the concepts and procedures in data science. An overview of the data, questions, and techniques and tools that data analysts and data scientists work with are introduced and reviewed. This course provides a conceptual introduction to the ideas behind turning data into actionable knowledge and tools that will be used to analyze this data. The course will focus on the collection, organization, manipulation, assessment and analysis, and communication of data.

Credits: 3

DA102 - Data Analysis

In this course the student will manipulate, process, clean, analyze and visualize data in a programming language. Real world datasets will be utilized. Structured data will be emphasized.

Credits: 3

DA103 - SQL for Data Analysis

In this course students will focus on how to apply the Structured Query Language (SQL) to data analysis tasks. Spreadsheets will be used for the visualization of data. Additionally, basic statistics will be covered. All data will be extracted from relational tables.

Credits: 3

DA104 - Data Mining

This course will provide students with an understanding of fundamental data mining methodologies and the ability to formulate and solve problems with these methodologies. Particular attention will be paid to the process of extracting data, analyzing it from many dimensions or perspectives, then producing a summary of the information in a useful form that identifies relationships within the data. The lectures will be complemented with hands-on experience with data mining software to allow development of execution skills.

Credits: 3

DA105 - Big Data Architecture

This course covers emerging big data architectures that deal with large amounts of unstructured and partially organized data. Focus in on the creation of applications that analyze big data stored in distributed file systems. Topics include file architecture, data retrieval, performance and data analysis.

Credits: 3

DA106 - Problem Solving, Decision-Making, and Computer Applications in Business

This course uses computer applications and critical thinking skills to solve real-world business problems.  Students integrate the use of word processing, spreadsheet, database, presentation, add-in software, and Internet resources to manage data to solve problems. Emphasis is placed on the use of software tools and analysis and modeling techniques to manage and manipulate data sources for decision-making. The course assumes prior successful experience with and knowledge of individual Microsoft Office computer applications programs. 

Credits: 3

DA200 - Statistical Methods in Data Science

Prerequisites DA101 and MA120 or equivalent

Statistical concepts and applications related to data science including advanced exploratory data analysis, nonparametric inference and simulation for larger datasets, logistic regression modeling, statistical programming, and basics of machine learning.

Credits: 3

DA201 - Data Analysis with R

Prerequisite:  MA120 or equivalent

This course is an applied statistics course that introduces students to key topics in data science, including exploration, statistical data analysis and communicating the results of data analyses. Major topics include advanced R programming language concepts, working as a standalone data analyst and within a team, organizing analysis projects, modeling with univariate, bivariate and multivariate data and basic clustering, classification and time series analysis and forecasting.

Credits: 3

DA202 - Data Visualization and Business Intelligence

Prerequisite:  DA102

This course introduces students to key design principles and techniques for interactively visualizing data. Students will be able to tell a story with data, communicating observations in a clear, compelling way that provides meaning and explanation. Students will study how visual representations are used in the analysis and understanding of complex data and acquire data visualization skills including designing effective visualizations, creating interactive visualizations, and drawing and presenting conclusions based on raw data from industry.

Credits: 3

DA203 - Advanced Data Visualization

Prerequisite:  DA202

This is the second course in the data visualization sequence. Students will apply advanced design principles and techniques for interactively visualizing data.  Students will be able to both create complex data visualizations and critique data visualizations designed for multiple audiences from many sources of information. Students will make use of tools like Tableau, Plotly and Quadrigram as they graphically represent analytical patterns. Students will also create and critique different types of dashboards.

Credits: 3

DA204 - Capstone Experience in Data Science

A comprehensive, project-based course where partners in industry, science, and government work with faculty and students providing expertise, guidance, and real data. Course includes topics in data mining, data ethics, and reproducible research.

Credits: 3