Master of Science in Business Analytics
The Degree of the Future

This course develops students’ data extraction, data transformation, data analysis, data interpretation, and data visualization skills. Topics include fraud detection, Benford’s Law, managerial accounting analytics, and financial accounting analytics. This course incorporates a substantial data analytics class project.

Prerequisite: 53:010:505 Non-Credit Financial Accounting Knowledge Seminar (or Undergraduate Level courses 52:010:101 Intro to Financial Accounting and 52:010:305 Intermediate Accounting)

The course provides a comprehensive overview of new financial technologies. Such technologies combine traditional investment practice with the ever-increasing power of computation to facilitate the achievement of highly customized objectives. The course covers the rise of big data analytics (Artificial Intelligence and Machine Learning) as well as the rise of automated investment advisers and algorithmic trading. We also discuss the role of the Blockchain in cryptocurrencies, such as Bitcoin and Etherium, and their implications for investment management practice and financial services.

This course provides a broad and practical introduction to the modern methods of Financial Data Analysis. The course emphasizes the use of modern analytical techniques to extract insights from the most commonly used financial data. Using a hands-on approach, students will develop deep practical intuition into the nature of financial returns, bond valuation, and stock pricing. Using a programming language, such as “R,” students will build financial models using a mix of market and accounting information, build optimally weighted portfolios, learn the basics of risk management, and learn simulation techniques, such as Bootstrap and Resampling.

Prerequisite: Foundation of Finance and an assigned LinkedIn Learning course.

This course provides a broad and practical introduction to the modern methods of Investment Management. The course emphasizes the use of big data and modern analytical techniques of Machine Learning and Artificial Intelligence to improve the performance of investment management. We start with the introduction to Python and financial data. We then apply Python and machine learning algorithms to the fundamental topics on investment management, such as bond valuation, stock pricing, derivative pricing, portfolio construction and optimization, international asset allocation, Monte Carlo simulation, and performance measurement.

Prerequisite: Foundation of Finance and an assigned LinkedIn Learning course.

Information technology (IT) is an important driver and enabler of the dramatic transformation of the business landscape. This course is designed to provide concepts and framework to develop technology strategy for supporting corporate strategy. The course also introduces traditional and agile project management skills for successful selection, planning, and monitoring of projects. Case studies and hands-on assignments reinforce concepts, which students can directly apply in their work environment. 

Use of customer databases to develop classification models with extensive use of the SPSS software package includes a comparison of traditional RFM (regency/frequency/monetary) versus other more advanced approaches, such as logistic regression and decision trees, in maximizing the profitability of marketing campaigns. Students will learn how to build models that predict buyer behavior, as well as assess and improve model performance. Note: Students must purchase SPSS Premium software package from Rutgers software portal (approximately $100).

This course covers development and management of digital marketing strategy, and the uses of digital media technology, including: social, mobile, and web to enhance customer equity, brand value, and ROI within the framework of an organization’s overall marketing strategy.

This course prepares students to develop the analytical skills marketers require to monitor, grow, and sustain competitive advantage. Students will develop abilities in aligning business objectives with metrics; utilizing data visualization, modeling, and text mining techniques; analyzing quantitative and qualitative data; and drawing data-driven consumer insights. The applications will emphasize the use of analytics to help make strategic marketing decisions.

This course explores how to use social media marketing to achieve strategic marketing goals. Using a mix of theoretical and practical exercises, students will learn to deploy social media as a strategic marketing asset. Objectives include learning and applying social media principles and evaluating how an organization’s social media presence adds strategic value. Students will also learn to implement a social media plan, connecting strategic goals to tactical objectives and the social media tools used to listen to and engage with consumers. The course also provides the skills needed to manage and measure social media activity.

The first part of the course focuses on strategic issues as well as learning basics on how to prepare text data for analyses. We will begin by framing text analytics questions, understanding sources of text data, and generating preliminary insights by coding text data. We will learn how to encode and preprocess text data for automated text analyses. Then, the course proceeds to focus on automated text analyses. We will cover explorative text analytics using word analyses (including word frequency analysis, keyword analyses, and text parsing), text visualization, and topic modeling. The last part of the course focuses on text classification and predictions, for example, understanding customer opinions from review comments with sentiment analysis. There are different methods for text classification and predictions, ranging from the very simple to the very sophisticated.  We will cover unsupervised dictionary approach as well as more advanced supervised machine learning approach. 

Prerequisite: 53:716:502 Business Analytics.

This course aims to (1) familiarize students with the major operational issues confronting managers, and (2) provide students with concepts, insights, and tools to deal with these issues. Topics include inventory management, capacity planning, forecasting, quality management, lean systems, supply chain management and logistics. Proficiency requirement: Excel for Business Executives.

This introductory geographic information systems (GIS) course emphasizes application; training primarily uses open-source GIS software. Students will be able to produce maps and conduct basic research using geographical data in any discipline that uses such data, e.g., public policy and administration, sociology, criminology, and public health/epidemiology.

This course illustrates how the field of data analytics can be applied to optimally manage supply chains. Students learn to apply data driven decision making methodology to the field of Supply Chain management. Topics encompass all portions of a supply chain, including sourcing, procuring, buying, making, moving, and selling. Topics include designing and planning supply chains, transportation analysis, facility and warehouse location models, demand and inventory management, and supply chain risk analytics. Case studies and hands-on assignments will introduce students to current business applications and innovative use of these ideas.

Prerequisite: 53 716 502 Business Analytics