This course seeks to equip students with the key conceptual, analytical and problem solving skills to address marketing problems and decisions. Specifically, it introduces students to various marketing analyses (customer, competitor and company analysis) and marketing strategies. Tools and methods used in planning and implementing the 4Ps (product, price, place and promotion) will be explored. This course integrates theory and practice within the context of organizations operating locally and globally.
Buyer Behavior 3 credits
This course offers an analysis of consumer and organizational purchase behavior. Emphasis is placed on how and why purchase decisions are made and on the psychological, sociocultural and economic underpinnings of different purchase behaviors. Based on these principles, students should be able to predict how buyers (consumers and organizations) will react to various marketing actions.
Marketing Analytics 3 credits
This course is designed to help students understand the development of marketing data in the big data era and the value of such data in marketing decision making. Students will be introduced the broad range of marketing data available to businesses, ways to gain insights from the data and convert insights into profitable customer acquisition, retention and growth efforts. Important marketing data usages (e.g., customer behavior prediction, customer value evaluation, social listening, etc.) are discussed using examples from different industries. Issues of data privacy and implementation challenges in big data marketing will also be explored.
Research Methods 3 credits
This course prepares students to design and conduct academic and applied research in business. It aims to provide essential knowledge and skills to the students for mastering the research process. Major topics include problem identification, literature review, research design, data collection, analytical methods and report writing.
Project Report 6 credits
This Project Report gives the students an opportunity to apply the knowledge obtained from their previous studies in data science and their field of specialization. Students will be required to identify existing practical problems in their field of specialization. To develop solutions for the problems identified, they need to conduct detailed literature review, design appropriate research method, gather relevant data, analyze their data using big data analytic techniques, interpret their findings, and finally write up the entire project report.