MS in Business Analytics Curriculum

This course is designed to help you develop and polish essential career and professional development pieces. The idea is that, whether you are looking for a new job, aiming for a promotion, or happily working in your current position, you need certain items in your career toolkit. Those items include resumes, cover letters, elevator pitches, LinkedIn profiles and behavioral interviewing techniques. We'll use assigned readings, instructional videos, and other guidelines to establish criteria for meeting today's standards. Rubrics establish criteria for meeting expectations of each assignment and provide a framework for instructor feedback. Discussions are optional. Successful completion of the course is based on quality submission of assignments. (1 credit) 

This course teaches students how to maximize spreadsheets as a data management tool using advanced functions and formulas. Students will learn how to efficiently manipulate, format, and automate data, and use spreadsheets to build customized reports, dashboards, amazing charts, pivot tables and macros. This course is applicable to all business disciplines. Prerequisites: basic spreadsheet knowledge. (3 Credits)

This course provides an introduction to Business Intelligence, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data storage and retrieval, thus this course will review logical data models for both database management systems and data warehouses. Students will learn to extract and manipulate data from these systems and assess security-related issues. Data mining, visualization, and statistical analysis along with reporting options such as management dashboards and balanced scorecards will be covered. (3 Credits)

This course provides an introduction to programming with a focus on business and analytics applications. The curriculum supports the development of skills in data extraction and manipulation as well as automating data analysis tasks. Students will become adept in the use of important libraries and will be able to use trusted open-source resources to support continued development of programming skills. No prior programming experience expected. (3 Credits)

This course provides an overview of statistical analysis and inference. The language and methods of statistics are used throughout the MBA curriculum, both in the classroom and in assigned readings. In addition, the language and methods of statistical analysis have permeated much of academic and professional writing as well as media reporting, both inside and beyond the business community. The goal is to present a broad introduction to statistical thinking, concepts, methods, and vocabulary. The course has an emphasis on business applications. (3 Credits)

This course provides an introduction to topics in managerial data mining, and related data retrieval and manipulation content. Models in regression, clustering, neural nets, classification, and association rule mining are applied to business data sets. In this managerially-focused course, students will learn how to use such models and interpret output. Students will also be introduced to a programming language for extraction and manipulation of data. Experiential exercises will be assigned using leading industry applications. (3 Credits)

This graduate course provides comprehensive coverage of project management, focusing on the methods and competencies needed to effectively manage projects. Upon successful completion of this course, students will be able to understand different project management techniques, terminology and trends; define project success measures and methods; improve quality, cost and efficiency with the project management process; and gain basic competency using project management software. (3 Credits)

An overview of the concepts and language of financial and managerial accounting that covers how accounting information can be used as an effective tool for communication, monitoring, and resource allocation. Topics include the principles and methodologies underlying financial statements and the inherent limitations of that information. Additional topics include behavior, cost analysis, and tools used to motivate and coordinate business activities. (4 Credits)

Basic concepts, principles, and practices involved in financial budgeting, planning for the cost of capital, and managing financial aspects of organizational sustainability and growth. Both theory and techniques applicable to financial problem solving. (3 credits)

This course provides an executive viewpoint of marketing concepts, such as branding and segmentation, for strategic and organizational decision-making. There is an emphasis on tools available for analysis and control of marketing activities, including the use of new media. Topics also include industrial life cycles, customer experience, and pricing strategy. (3 credits)

Provides an overview of different perspectives and approaches to leadership practice and related organizational behavior topics. Relying on a participative multi-learning approach, the course goes beyond traditional leadership practices to examine empowering leadership approaches for addressing contemporary organizational behavior challenges such as leading teams. The overall objective is to increase students' understanding of and ability to apply contemporary leadership and organizational behavior knowledge in work situations. (3 credits)

This course covers essential concepts and methods to foster data-driven decision-making to motivate business insight and innovation. Lessons cover topics in big data, algorithmic thinking, machine learning, data visualization, and communication. Projects spanning several weeks focus on business problems that can be solved through the application of cutting-edge Data Science techniques. Students develop proficiencies in the use of Python, R, and Tableau by completing regular exercises and projects using real data. No prior programming experience is expected. (3 credits)

Data Science for Business is a required course, plus complete 9 credits of electives from the courses listed below:

Cybersecurity risk continues to evolve at a rapid pace. It is essential for accounting professionals to have a broad understanding of cybersecurity and how cyber risks affect an organization. It is equally imperative to be able to identify, assess, and evaluate the necessary controls to mitigate the risk of a compromise. This introductory course will teach students how to leverage various types of cybersecurity risk management frameworks to accurately assess an organization's overall cybersecurity maturity. (3 credits)

This course explores marketing research and analytics for decision making. Course content includes marketing strategy, research design, sampling, survey design, data collection, website analytics, analytic techniques for developing insights, and effective storytelling/presentations/reports. Industry tools to be used include Google Analytics, Qualtrics, and Excel. (3 credits)

This course will cover an overview of financial technology and its various applications. The specific topics include fintech and financial service, cryptocurrencies, high-frequency trading, peer-to-peer lending, etc. The course will help students learn fintech industry through case studies. It also provides students with a unique opportunity to delve deep into one aspect of financial technology and write an extensive paper/report on this topic. Topics include (but are not limited to): Data-driven credit models, Cryptocurrencies, Digital wallets, Blockchain and its applications, Financial cloud services, Robo advising, Crowd-funding, Peer-to-Peer lending, High-frequency trading, Payments, Artificial Intelligence, Digital banking, Defi (Decentralized Finance), NFT, etc. (3 credits)

This course explores issues in emerging best practices for operating a business in the online environments of today. We will review various digital marketing efforts which generate web traffic (SEO and organic search vs. paid advertising venues, i.e. Google Adwords).  Specifically, this course will explore the collection, measurement and analysis of Internet information for the purposes of improving the customer online experience.  Use of a demo Google Analytics (GA) account will provide a practical and applied learning environment.   Students will work through the analytics reports available in GA and create user specific dashboards. Students will be able to plan, execute and manage a web analytics strategy, including key performance indicators and a measurement plan. No previous programming or technical skills are required. (3 credits)

Supply chain constitutes a core competency, spanning most business functions required for the delivery of products and services to consumers. Advances in information technology and analytics facilitate continued improvement in supply chain infrastructure and operations efficiency. This course will introduce fundamental concepts in supply chain management, IT-enabled supply chain operations, procurement management, production planning, inventory management, and logistics and transportation. The course involves the use of Excel to support decision making, the study of selected business case studies, and the use of a team-based supply chain simulation game. (3 credits)

Salesforce is a hugely popular-and powerful-customer relationship management (CRM) platform that provides a variety of tools for managing marketing workflows, including digital marketing and paid advertising. Through this 1-credit course you will be introduced to Salesforce and its applications including managing the lead life cycle within a Salesforce account, tracking vendor expenses, communicating with sales teams, visualizing marketing project success, email marketing, and tools such as Marketing Cloud. (1 credit)

As people are increasingly sharing their daily lives on social media, organizations and celebrities are using it as a channel to directly engage with followers. A lot of data is being generated in these interactions. This course seeks to provide students with frameworks and tools to gather insights from this data. For an organization this might allow them to gain insights into their market structure and their customers' perceptions of the brand. Other areas of interest may be studying the changing perceptions about the brands in response to the exchanges amongst netzines or the organization's marketing campaigns. This course will have several hands-on exercises based on the R language (No previous experience with R is required). Students will work with some near real-time datasets of tweets, US stock market data for select ticker symbols, and some popular RSS news feeds from the instructor's research projects in this field. Students will work in groups on a project that will use these datasets.  While there are no prerequisites for this course, a few skills would be helpful for anyone intending to take this course and these are; a basic understanding of descriptive statistics, some experience with spreadsheets, and any experience writing spreadsheet macros, database queries or some basic computer programming. (1 credit)

In this course, students will learn the fundamentals of Artificial Intelligence, Machine Learning, and Big data and how to advance business strategy through the use of these technological advancements.  Artificial intelligence applications covered will include blockchain, 3D printing, virtual and augmented reality, quantum computing, robotics, and IoT. The course will explore these topics both in breadth and depth.  From a breadth perspective, students will explore the current status of tools, solutions and business uses of artificial intelligence.  From a depth perspective, students will learn basic skills with machine learning. (3 credits)

This course provides an introduction as well as hands-on experience in data visualization. Students will learn how to leverage data visualization for exploration and analysis. Students will be introduced to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. Explore storytelling through a variety of mediums, including presentations and dashboards. (3 credits)

The course provides an introduction to business analytics within the context of the sport industry. It first examines the range of cross-disciplinary concepts and techniques that form the foundation for the field of business analytics. It then applies these concepts and techniques to both the administrative side of the sport business - like pricing decisions, stadium management, etc. - and to the on-field side. With the latter, topics include player performance measurement, in-game decision making, and player selection/team building. (3 credits)

This course is designed to teach students the skills necessary to develop, implement, analyze, and interpret survey data. Specifically, the course covers types of surveys and how to design a questionnaire or survey with different levels of measurements and scales. By the end of the course, students should be able to design and conduct an original survey using online platforms (e.g., Qualtrics, SurveyMonkey), and descriptive analysis of data using such programs as SPSS, JMP,  Excel. (1 credit)