Master of Science in Business Analytics

This course explores the expertise, perspectives and the statistical foundation that data scientists apply to projects during four phases of data science: problem foundation, data acquisition, statistical modeling and analysis, and presentation of results. This course intends to provide students with a comprehensive introduction to all the major aspects of data science. Credits: 4

This course offers students an introduction to R and Python object-oriented programming. More specifically, students will learn data programming concepts, reproducible research and version control, which will serve as a foundation to the rest of the program. The course is meant to offer a foundation for the programming skills necessary for modern data professionals. Credits: 4

This course offers students an introduction to the basic concepts and applications of predictive analytics, which is a broad domain of data analytical practice focused on developing forward-looking statistical estimates. The course begins with an overview of predictive and prescriptive analytics, followed by immersion in statistical prediction-related multivariate statistical concepts, including an in-depth discussion of commonly used types of multivariate predictive models, all focused on strengthening students’ comprehension of core statistical notions, including variable types, statistical learning, statistical regression and classification, non-linear modeling, and tree-based estimation. The second part of the course focuses on hands-on applications of model fitting and evaluation using programming applications in R or Python. Credits: 4

Financial statement analysis is essential to evaluate an organization’s performance and provide insights into its operations. Designed to build and strengthen a student’s ability to correctly interpret financial statements, this course is a deep dive into income statements, balance sheets, and cash flow statements, where students learn how to assess a company’s financial health and performance. The course will review how financial statements are generated, followed by the application and analysis of a range of financial statement analysis tools, such as ratios, common-size financials and rate-of-change financials. Students gain valuable hands-on experience analyzing financial and business information included in the Annual Report, Form 10K, Form 8K and Form 10Q. Credits: 4

This course introduces students to mathematical and statistical concepts, integrating financial modeling applications across various finance domains. Quantitative methods and financial modeling are employed to conduct financial analysis, thereby facilitating managerial decision-making. The utilization of quantitative aspects in financial topics and the practical use of tools, including but not limited to Excel, blend data interpretation with technology, enhancing and improving overall analytical skill sets. Credits: 4

This course focuses on how technology innovation is creating profound changes in the financial services industry. Financial technology (FinTech) is revolutionary and is challenging long-held practices, systems and processes in the financial industry, and even forcing us to rethink the underlying concept of money. Students will learn about these new technologies and trends that are creating opportunities for visionaries. This course provides an introduction to the future of finance, including numerous FinTech applications, cryptocurrencies (e.g., Bitcoin and Ethereum), payment systems, AI, and machine learning (robo-advising). Students are expected to develop a broad understanding of recent FinTech developments, the evolving ethical landscape, changes in regulations and their broad impact on the financial industry. Students will also have hands-on and problem-solving experiences that can be useful in FinTech applications and innovation. Credits: 4

Marketing is continually evolving. This course represents the leading edge of marketing by covering the most innovative and modern marketing practices. Topics include branding, experiential marketing, interactive marketing and holistic marketing. This course not only covers contemporary marketing practices but also reveals the psychological mechanisms that drive their success. Students will use a mix of theory and brainstorming within synchronous sessions to develop an optimal brand strategy. By the course conclusion, students will have a rich understanding of the most innovative marketing practices and how to leverage them through the creation and development of a brand. Credits: 2

Digital Marketing Implementation & Optimization (DMIO) is the heart of marketing and communication between companies and consumers in the new digital era. With the prevalence of online marketing and various media promotion strategies such as email marketing, E-commerce sites, social media marketing, and podcast and live streaming, knowledge of DMIO best practices has become essential for business success. Specifically, students will gain exposure to concepts and practices of digital content creation, digital and social media marketing, inbound and outbound marketing, CRMs and marketing terms. Students will learn how to measure SEO, KPIs and ROI, affiliate and sponsored marketing, E-commerce via website and APPs, and digital privacy. These DMIO concepts and skills are helpful for students who: (1) plan to pursue a marketing and communication-related career, (2) want to create content and conduct digital marketing for their own startups or (3) have the desire to enjoy the process of developing marketing content and thinking creatively. Credits: 2

Marketing analytics skills and experiences have become one of the increasing demands by employers for both entry-level and senior-level positions. In this course, students will be exposed to and become familiar with basic analytical tools (e.g., Excel, Google Analytics, Tableau) and commonly used analytical methods in the industry to gain data insights and drive business success in a marketing campaign setting (i.e., collect, clean, interpret, present and reapply business data in each of the decision-making processes). Students will also collaborate in teams to develop their data soft skills (e.g., developing data sense, brainstorming, communication, project management, data translation, data presentation) along the way to mimic a real industrial analytics team project. Although data soft skills are developed throughout the class, this class is heavily technical and applied to help maximize students’ future success in a marketing or data analytics-related career. Credits: 4

This course deals with concepts, methods and applications of decision modeling to address various marketing issues: segmentation, targeting, positioning, new product design, advertising and promotional planning, and strategic planning. Topics include connecting with customers, building strong brands, creating value, supply chains and distribution, and integrated marketing communications. Students gain practical experience conducting market research and use substantiated facts and inferences to present recommendations and make managerial decisions in a client-based atmosphere. Credits: 4