Marketing Analytics Graduate Certificate

This course employs extensive use of data and statistical methods to support and improve organizational decision-making. Topics include data visualizations, descriptive statistics, probability distributions, sampling, confidence intervals, hypothesis testing, regression analysis, and the use of computer software for statistical applications. The primary focus will be on the flexibility of the analysis, and the ability to present the results in an intuitive and understandable manner for greater organizational impact. The ethical issues related to big data will also be discussed.

This course introduces fundamentals about data and the standards, technologies and methods for organizing, managing, curating, preserving, and using data. The course will teach students the use of software such as Python for data manipulation, analysis and visualization. The course also incorparates broader issues surrounding data, including technologies, behaviors, organizations, policies, and society. Special attention will be given to ethical issues surrounding data, soical and historical perspectives on data with ethics and policies to help students develop a workable understanding of current ethical issues in data science. Finally, the ethical issues will be addressed that arises throughout the lifecycle of data - from collection to storage to analysis and application.

To compete in a data-driven world, data analytic skills and database skills are key. Before data is analyzed, correct data first needs to be chosen and pulled from a database within your organization or your client's organization. While the term big data is influenced by the rise of unstructured data (no-SQL database), structured data (SQL/relational database) remains a large and important component because structured data is driven by business processes and workflows. This course mainly focuses on process-driven/structured data and a relational database. This course is not designed to develop database building skills. A large focus of this course is placed on an understanding of database schema (or how business data is collected in relation to other business data) and SQL coding techniques for selecting the right data for the purpose of further analysis.

Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. This course is an introduction to the principles and techniques for data visualization. In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from data sets across categories, space, and time.
Prerequisite: Take BUAN-651

Big Data Analytics is about harnessing the power of data for new insights. The course covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry.

This course explores the tools and techniques used by marketers to analyze customer behaviors. It examines databases, analytics, metrics, software, and techniques applied by marketers to transform data into useful formats for the strategic decision-making process. Contents focus on technology tools for segmentation, target marketing and positioning, media selection, market share and estimation, sales forecasting, and other analyses. This course explores the use of machine learning algorithms in the developing of marketing models related to consumer segmentation, market basket analysis, customer lifetime value, predictive marketing, consumer choice, and pricing.
Prerequisite: Take MK-661, MK-670

This course explores how companies assess marketing performance. It is a survey course covering a variety of return on investment metrics for marketing investments. The course introduces formulas and ratios used to gauge customer profitability, product portfolio mix, and advertising and web spending effectiveness. Attention is drawn to links between finance and marketing.

This course will present a practical approach to the process of decision-making using big datasets as a result of acquired or aggregated data.
Prerequisite: Take MK-661, MK-670