The Marketing faculty have designed the Advanced Marketing Analytics (AMA) Sequence to allow Marketing Majors to concentrate their coursework in preparation for marketing roles that may require more analytical rigor which has become prevalent. The use of analytics is expected to grow rapidly. Companies will need employees who understand data that is available to them. A study from McKinsey & Co. found that by 2018, the United States will face a shortage of 1.5 million managers who can use data to shape business decisions (WSJ 2011). This sequence will provide skill development, introduction and use of contemporary analytical tools, and context that will be useful for future workers to manage effectively.
Degree offered: B.S. in Marketing students may choose to major with a general Marketing degree or specialize in Integrated Marketing Communication, Professional Sales, or Advanced Marketing Analytics (AMA) Sequence.
As a marketing major, it is strongly recommended that you take MKT 190 (counts as MKT 230) in your sophomore year and declare the Advanced Marketing Analytics (AMA) Sequence by your Junior year. Advanced Marketing Analytics (AMA) courses may be "major blocked". Advanced Marketing Analytics (AMA) Sequence students may be added first, followed by MKT majors. You may not get in these courses if you are not declared AMA Sequence.
The following lists a brief description of the marketing courses offered as part of the Advanced Marketing Analytics (AMA) Sequence. For additional details, please also consult the “ MARKETING Major Checklist for Advanced Marketing Analytics” document.
Introduction to Marketing Analytics MKT 245: This course introduces you to the tools and techniques of data mining and predictive analytics, with the goal of using analytical techniques to derive actionable intelligence from marketing data to drive measureable improvements in marketing performance.
Advanced Marketing Analytics MKT 345: This is a quantitatively oriented course that is intended to explore concepts, tools, and methods of customer relationship management (CRM) in addition to more specialized and important contemporary business tools. Since data analysis is an important first step in developing a CRM program, a focus of the course will be placed upon techniques, and terminology associated with data analysis.
Brand Management & Analytics MKT 339.13: This course will provide students with advanced knowledge and practical skills necessary to make day-to-day and long-term brand related decisions. The course focuses on important issues in planning, implementing, and evaluating brand strategies based on the relevant theories, models, and the latest analytics tools available in brand management.
Marketing and Sales Forecasting MKT 311: This course develops an understanding of forecasting concepts and builds skills in performing a variety of qualitative and quantitative analysis methods. Applications include determining market and sales potential, estimating future sales, determining territory assignments and target markets. This is a highly interactive class, emphasizing actual forecasting applications through real-world exercises and projects.
Introduction to Business Analytics ACC 271 / Introduction to Business Intelligence IT244: This course enables the student to become aware, comprehend, explore and manage significant issues confronting the field of Business Intelligence and Analytics from a multi-disciplinary perspective. Students can expect to be “immersed” in Business Intelligence analyses, research, resources, cases, tools, and techniques from industry and academia alike.
Accounting Information Systems ACC 263: This course provides students with advanced spreadsheet skills and familiarity with an accounting software package.
Advanced Business Data Management ACC 366: This course covers advanced study in computer storage techniques required for business information systems. Techniques are developed for both enterprise and desktop applications.
Data Mining IT 344: This course enables students to discover knowledge from data by applying data mining tools and techniques. It will emphasize preparing data, application of data mining techniques, interpreting the results and devising action plans based on the discovered knowledge. Students will be equipped with theoretical foundations to engage in algorithmic and optimization exercises.
Geographic Information Systems GEO 303: In this course students create effective visual presentations of spatial data. Students obtain datasets for GIS analysis, assess the suitability of the dataset to a given problem, and evaluate the associated uncertainties. Students will also independently analyze spatially referenced data, interpret results, and apply these skills to a decision-making process.