Advanced Excel for Business Analytics
This course will develop intermediate to advanced Excel skills using an applied focus on different types of decisions one may analyze using spreadsheet capabilities. The student will develop knowledge of how to evaluate a business process. Additionally, the art of modeling and the process of structuring and analyzing problems so as to develop a rational course of action will be discussed. The course includes the use of pivot tables to slice and dice data, and graphs and charts to communicate complex analytics visually. In addition, the course integrates advanced topics in business statistics such as linear and multiple regression and forecasting, linear programming, and simulation.
Prerequisite(s): CTA 326
Advanced Statistics for Business Analytics
Students will acquire knowledge to build statistical models and implement regression analysis in real-world problems from business, economics, and marketing research and consumer behavior. Topics include multiple regression models (including first-order, second-order and interaction models with quantitative and qualitative variables), regression pitfalls, and residual analysis. Students will acquire skills not only in the mechanics of regression analysis (often by means of a statistical software package) but also in deciding on appropriate models, interpreting results, and diagnosing problems.
Forecasting for Business Analytics
This course provides knowledge of how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice.
Simulation for Business Analytics
This course provides knowledge of how to develop, implement and use simulation methods for business decision making. Students will build simulation models to answer practical questions that are motivated by operational business decisions such as determining optimal inventory policies, and deciding staffing levels for an organization. The course will utilize Microsoft Excel as well as Excel add-ins as modeling tools.
In this course students will acquire a comprehensive understanding of how an organization can use its customer data to maximize the value of customer relationships. Businesses now have a wide array of tools to convert raw customer transactional data into usable marketing intelligence. Companies can identify, profile, analyze, and interact with both current and prospective customers on a personal basis. Topics covered include upselling and cross-selling, customer lifetime value, customer segmentation, predictive modeling, RFM analysis, customer loyalty and reward programs, and churn management.
This introductory course to data mining will explore various statistical approaches used for data mining analyses. The preparation of data suitable for analysis from an enterprise data warehouses using SQL and the documentation of results is also covered. Building predictive analytics (e.g., SEMMA, KDD); exposure to logistic regression, machine learning and decision tree methods; Understanding lift factors, ROC curves; hands-on use of mining software; business case studies. A simple data mining analysis project using SPSS will be used to reinforce the concepts.
Big Data and Visualization
This course provides knowledge of the data sources, tools, and techniques used in the exploration and analysis of big data such as: text and stream mining, social media and big data, Hadoop, NoSQL, fundamentals of big data programming, cloud-based solutions, and visualization of big data using Tableau and GIS software. The course will utilize business case studies for students to understand big data solutions in the business environment.
Web and Social Media Analytics
Students will gain knowledge of the most effective strategies for analyzing web and social media data generated by online activity. The course will examine social media analytical tools that enable organizations to understand what consumers and bloggers are saying about them, their products, and their competitors. Students will gain knowledge of web analytics to track and analyze the behavior of customers and browsers. Topics include extracting conclusions from abandoned shopping carts, RFM analysis, site usage, domains and URLs, keywords, and search engine placement.
Insights developed during the modeling, simulation and data analysis process must ultimately be visualized and communicated in a compelling way in order to recommend specific paths of action and support decision-making and strategic planning functions within an organization. Students will gain knowledge of data visualization techniques using Tableau advanced visualization software, GIS software, and the native graphics capabilities generally available to working professionals (e.g., PowerPoint, Excel, Prezi, etc.).
Capstone Business Analytics
This capstone course provides students with the opportunity to demonstrate competencies in the key domains of business analytics. Students will develop a comprehensive project that integrates content learned throughout the duration of the program including database management, systems analysis, enterprise infrastructure, and decision support.