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This course provides an overview of the field of data science, with a focus on data models, data mining processes, tools and technologies, algorithms, and statistical analyses used to glean insight from data for use across a variety of domains. Students will also examine various computer information systems and programming languages used to process, store, integrate, and analyze data.

This course focuses on the design and administration of database management systems (DBMS) with an emphasis on relational databases, data integration and cleaning, data warehousing, cloud computing, Structured Query Language (SQL), and NoSQL.

This course introduces students to a variety of data analytic solutions. Students will learn how to retrieve real business data and create reports, support business decisions with visual representations of data, manage and improve a business process based on quantitative analysis, analyze long-term trends and patterns in the data, and develop actionable results that drive the business decision-making process.

This course introduces principles of artificial intelligence, machine learning, and deep learning with a focus on applied techniques. Students will examine software, programming languages, and algorithms used to parse and analyze data and automate computer decision-making processes.

This course provides an overview of data modeling with an emphasis on data model types, database models, common business requirements, and the structure, relationships, properties, and rules used to organize and store data.

This course provides an overview of data mapping, integration, and transformation, which involves the combination of data from heterogeneous database systems to provide a coherent, unified view for business intelligence purposes. Topics include big data, data warehousing, data lakes, metadata, ETL (extract, transform, load) mapping, and Electronic Data Interchange (EDI).

Data mining involves the discovery, extraction, and analysis of patterns of data from integrated data sets. This course examines the use of machine learning and statistical models in data mining processes.

This course provides a comprehensive review of customer relationship management (CRM), which is a major component of organizational business intelligence (BI). This course covers the domain of CRM and is not tied to any particular perspective and covers a number of holistic models. Students will learn three types of CRM - strategic, operational and analytical. This course emphasizes a managerial perspective on CRM while maintaining a balanced view of the technology.

This course explores various tools and communication strategies used in data visualization and decision making. Topics business intelligence and presentation software, dashboards, exploratory data analysis, and effective presentation and storytelling techniques.

Business intelligence involves the use of data analysis to guide business planning, inform operational decisions, improve performance, and add business value. This course provides an overview of complex event processing (CEP), benchmarking and key performance indicators (KPI), dashboard development, and predictive and prescriptive modeling used to assess strategic business opportunities.

This course provides an overview of data governance standards and processes with a focus on information architecture, data management, data governance tools, regulatory requirements, data ethics, data integrity and security, internal auditing, and data reporting.

The data science capstone provides students with an opportunity to integrate content from throughout the program through the completion of an applied data science project. Students will develop a data strategy to address a business challenge with a focus on measurable goals and objectives, performance metrics, roles and responsibilities, business intelligence tools, data collection and analysis, and data visualization and communication.