This course discusses the process of business analytics by developing a business intelligence solution, including problem definition, data preparation, descriptive and predictive analyses, evaluation of results, implementation and deployment. Data-oriented methods using spreadsheet and structured query language (SQL) are emphasized for business transaction capturing, data aggregation and online analytic processing (OLAP). Students will employ a variety of software tools in the development of a data warehouse, including ETL (extraction, transformation and loading) and visual data representations (e.g., data cubes).
Novel problems require innovative solutions - this course introduces students to the power and flexibility of programming and scripting languages such as R and Python, applied to problems in business analytics. Students will learn how to acquire and deploy software packages relevant to their problem, then use them together with tools such as SQL to collect and prepare data, customized analyses according to specific needs, and create outputs which effectively communicate the results.
This course introduces students to the concept of social media analytics and techniques used to analyze social media data such as texts, networks, and actions. Students will learn how to extract data from popular social media platforms and analyze such data using software tools such as R to identify trends, sentiment, opinion leaders and communities.
This course introduces students to data visualization and dash-boarding. Students will learn best practices in data visualization, data retrieval using structured query language (SQL), polish analytical skills, and learn how to design dashboards to support managerial decision. Student will have the opportunity to gain hands-on experience in data retrieval and visualization. Students will use Tableau as their main tool for data visualization and dash-boarding but will develop transferable skills which can apply to most common software packages in the field.
This course provides students with the knowledge, skills and techniques for unsupervised machine learning, deep learning, and artificial Intelligence for Business Analytics. Several techniques will be applied to diverse business applications. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent.
An introduction to statistical methodology to include probability concepts, inference techniques, analysis of variance, regression analysis, chi square and other non-parametric analyses. This course focuses on the use of the computer in performing statistical analysis.
This course addresses real-world business applications of machine learning and artificial intelligence for strategic decision-making. Students will implement AI and machine learning solutions to unlock valuable insights from complex business data. The course enables students to drive business transformation through AI-powered analytics and data-driven business intelligence. Prerequisites: QUMT 6303 or QUMT 3341 or equivalent
This course introduces the principles and techniques of prescriptive analytics. These provide business entities and policy makers with fundamental rationality in evaluating performance, making decisions, designing strategies, and managing risk. Students will learn how to use analytical models to evaluate uncertainty that is prevalent in many business decisions. Since business problems often have alternative solutions, students will learn how to use analytical models to assess various business solutions and identify the best course of action. This course involves a hands-on learning experience with spreadsheet modeling and other analytical packages. The emphasis is on how to employ these analytical methods to facilitate managerial decision-making in diverse industries and functional areas.
