Online Applied Business Analytics Graduate Certificate

Prereq: AD100 Pre-Analytics Laboratory and ADR100 Introduction to R
This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [4 credits]

Prereq: METAD571
The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course. [4 credits]

Prereq: METAD571
Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores approaches and techniques to support the managerial decision-making process and skills in using state-of-the- art statistical and analytics tools. Students will have an opportunity to gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions. Python, R, SQL, and Power BI software are used in this course. [4 credits]

Prereq AD100, ADR100, AD571
Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. R, SQL, and Power BI software are used in this course. [4 credits]

Prereqs: AD100,ADR100,AD571
Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal to convert the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. R, SQL, and Power BI software are used in this course. [4 credits]