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Designed as an introduction to Business Analytics, which considers the extensive use of data, methods and fact-based management to support and improve decision making. Business intelligence focuses on data handling, queries and reports to generate information associated with products, services and customers, business analytics uses data and models to explain business performance and how it can be improved. The class will be built on heavy hands-on coding; it will introduce and subsequently involve extensive use of Python.

Exposes the students to commonly used platforms for statistical and predictive analytics. The class will go into depth of analytics using R before demonstrating the same concepts using SPSS and SAS. Â鶹ÒùÔº will learn to analyze large datasets, including textual analytics such as twitter-stream analysis using R.

This course exposes the students to commonly used platforms for statistical and predictive analytics. The class will go into depth of analytics using Python. Â鶹ÒùÔº will learn to analyze large datasets, including textual analytics such as twitter-stream analysis. The class will focus on predictive analytics.

Explores both the functional and technical environment for the creation, storage and use of the most prevalent source and type of data for business analysis, ERP and related structured data. Â鶹ÒùÔº will learn how to access and leverage information via SQL for analysis, aggregation to visualization, create dashboards, and be source for business intelligence.

Explores the capabilities and challenges of data-driven business decision making and prepares students to lead in analytics-driven organizations. Introduces a set of common predictive and prescriptive analytics tools. Â鶹ÒùÔº apply the analytics tools to important decisions based on practical data sets from various companies. Analytics software packages are used extensively in the course.

Market Intelligence is a decision-oriented course geared toward gathering, analyzing, and interpreting data about markets and customers. Â鶹ÒùÔº learn how to: define the marketing problem and determine what information is needed to make the decision; acquire trustworthy and relevant data and judge its quality; analyze the data and acquire the necessary knowledge to make certain classic types of marketing decisions.

The purpose of the course is to provide students with a comprehensive introduction of the recent development in AI through the coverage of fundamental AI concepts and practical applications of these concepts in business.

Moves the student beyond structured data and sources into business scenarios where data is semi-structured to unstructured such as those from social and web applications. Specific topics include introduction to SQL-on-Hadoop, NoSQL and related distributed processing technologies. Â鶹ÒùÔº will learn practical application and mechanisms for getting this sort of data ready for analytics.

Provides an opportunity to execute a project for a company, integrating course work knowledge in an applied capstone experience. Allows first hand exposure to the business analytics as both an observer and creator of the business analytics process. Â鶹ÒùÔº work closely with an area client company to solve an important business analytics problem under the close supervision of the instructor.

Provides a deep understanding of how to use data on customer behavior and preferences to inform managerial decision making. Introduces methods for causal inference, modeling consumer demand, and modeling firm decisions. Applications include long-run customer management decisions (customer acquisition and retention) and short-run marketing mix (product, price, promotion and distribution) decisions. The R programming language is used for course examples and assignments. Â鶹ÒùÔº are assumed to have a working knowledge of R and linear regression techniques.

Covers both traditional and emerging digital advertising methods, the popular platforms used to execute ads, and the leading analytic tools that can be used to assess advertising performance. Core advertising platforms covered include search, display, social media, native advertising, sponsored content and mobile. This class focuses on best practices and Key Performance Indicators that go with each advertising platform.