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: 53:390:506 Financial Management or 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: 53:390:506 Financial Management or an assigned LinkedIn Learning course.

This course is designed to help students acquire foundational knowledge and skills related to understanding, predicting, and changing human behavior and processes in and around organizations. Students will become more aware of their strengths and weaknesses as individuals and team members. They will learn how to apply theoretical concepts to master real-world challenges. Topical coverage includes, but is not limited to, leadership models and theories, job attitudes, employee motivation, group/team dynamics, decision-making, organizational climate and culture, stress, diversity, and individual differences, such as perceptions, attitudes, and personality.

This course examines major concepts, theories, processes, and practices associated with conflict analysis, resolution, and negotiation. Within an interactive learning environment, emphasis is placed on preparing for and analyzing conflict and negotiation situations, developing various goals of conflict management and negotiation, learning principles of constructive and destructive communication, assessing biases and barriers to effective conflict resolution and negotiation, and reflecting on these processes for managerial growth and development. This course is designed to address a broad spectrum of conflict resolution and negotiation problems that are faced by managers and professionals. Moreover, we will combine the theoretical concepts from class with applications, so you can understand why and how things work in context. Successful completion of this course will enable you to recognize, understand, and analyze essential concepts in conflict resolution and negotiation.

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 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.

Analysis, understanding, and interpretation of financial statements; simple skills and methods for making common sense of the elaborate financial statements and financial reports prepared according to existing accounting standards and conventions. Skills relevant to credit analyses, lending decisions, security analyses, investment decisions, and other decisions that rely on financial data.

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)

Analytic competency is becoming tremendously important in the business world and is often the factor that distinguishes leading firms in any industry. This course is intended to provide an introductory overview of how firms implement data-driven decision-making. Students will learn statistical concepts, use spreadsheet modeling, and learn through a mix of lectures, cases, and class discussion. Students are required to have a functioning computer with Microsoft Excel installed. Within Excel, you must have DATA ANALYSIS and SOLVER functionality. The course’s primary goal is to coach students on “fact-based decision making” and enable them to carefully plan and run “business experiments” to make informed managerial decisions.

The focus of the course will be to introduce basic concepts in machine learning and data-analytic thinking to students, with an applied business orientation. Students will understand how to use data to competitive advantage and to build and evaluate models for decision-making. Companies today have access to vast amounts of data from their business operations. Data Science is the craft of extracting patterns from this data and using available information for competitive advantage. This course represents an introduction to data science and data analytic thinking. Students will learn to leverage data to answer business questions relating to classification tasks (e.g., will this credit card prospect default or not?), prediction (e.g., how much will this customer spend/year?) and similarity profiling (what do my most profitable customers look like?).

Note: Students must be comfortable installing packages independently and navigating in a computing environment. Important: The course assumes the student already has some basic familiarity with the Python programming language as well as a working knowledge of Jupyter notebooks.