Business Administration

This course covers the fundamentals of innovation and entrepreneurship and how they can help students become more future proof in their careers. It focuses on how innovation can be developed and enhanced and then looks at the world of entrepreneurship and how it can be relevant for each and every one of the participants. Throughout, the course introduces and practices the Lean Startup Model, focusing on how to identify real problems for people and then finding solutions for those problems. As an online course, students watch short videos, use interactive applications, answer online quizzes, and develop an idea for a venture (business, social, or design). The course ends with a project that presents a model for a real world venture.

This course presents financial statement analysis from the point of view of the primary users of financial statements: company managers, creditors, and investors. The course provides students with tools to enable them to analyze financial statements and draw inferences about the performance and the value of a firm. The course is structured in two broad parts. Financial analysis forms the first part, focusing on past and present performance evaluation to generate expectations about future performance (prospective analysis), credit rating and distress prediction. The second part, security valuation, focuses on market- and accounting-based models to derive the value of a firm. All analyses are conducted within the context of a firm’s industry and strategy. This is an applied and practical course. 

A decision support system (DSS) is a model-based or knowledge-based system intended to support managerial decision-making in semi-structured or unstructured situations. A DSS is not meant to replace a decision-maker, but to extend his/her decision-making capabilities. It uses data, provides a clear user interface, and can incorporate the decision-maker’s own insights. This course reviews decision support systems, their use, and important components which leads to a group designed and developed DSS that facilitates decision-making through specific selection criteria and constraints.  Students learn how to code in the programming language Excel VBA, develop and improve skills in algorithmic thinking, create userforms, and use tools provided by the toolbox. Essential programming techniques and constructs such as loops, subs, functions and macros are taught. Prerequisites for the course include a basic mastery of Excel, or an alternative spreadsheet application: cell referencing, building formulas, and use of logical functions. This does not require mastery of VBA for Excel, training in VBA programming is part of the course.

In this course, students gain an in-depth understanding of what makes consumers buy some products and not others, how various psychological characteristics influence our consumer behaviors, how companies can best try to meet consumers' wants and needs, among other topics. Building on a general understanding of marketing, this course develops a useful, conceptual understanding of psychological theories relevant to the study of consumer behavior.

Topics in this marketing and sales management course include: today's client; contemporary sales; CRM, sales technology and analysis; market research and messaging; negotiation and closing; territory organization; recruitment, selection, and training; motivation of sellers; remuneration and evaluation of sales; international sales prospects.

Business administration studies economic problems within the firm and relates to problems in the fields of marketing and logistics, finance, accounting, information management, and organization and strategy. Business administration aims to provide an integrated view of all the various (sub) disciplines. This course introduces basic topics that are related to business administration. The course centers around a real-life management simulation: Market Place live.

This course addresses human resource topics from a strategic perspective, considering how human resource management might aid in developing competitive advantage and what can be done to fulfill this potential. 

This course provides students with a thorough understanding of major topical areas in financial management, including an overview of the most recent developments and the ways they are integrated into corporate practice. Students learn how to evaluate major strategic corporate and investment decisions and to understand capital markets and institutions from a financial perspective.  Topic coverage includes asset valuation, real options, capital structure, cost of capital, Hybrid Financing, corporate liabilities, Initial Public Offerings (IPOs), Mergers and Acquisitions (M&As), and Sustainable Finance. Throughout the course, practical applications of financial concepts and techniques are carried out with the use of cases. Building on earlier, more fundamental courses in corporate finance and investments, the scope of the Sustainable Financial Management and Policy course drifts away from the simple technical analysis of corporate financial decisions to a high-level discussion of their strategic implications for the profitability and sustainability of the company. Prerequisites include a second-year course in finance.

This course discusses machine learning and its uses in business decision-making. Topics include: data extraction and exploration; basic models for classification and regression; training, hyper-parameter tuning, model evaluation, pre-processing; feature selection and generation; advanced models for classification and regression; unsupervised learning.

This course examines the main elements of natural language processing (NLP), text analytics, and text mining, providing students with a foundation in collecting, managing, and analyzing textual data with financial and economic applications in mind, such as FinTech. Examples of potential applications include understanding and responding to sentiment in financial newspapers and social media, using social media to improve performance in asset/investment management, due diligence, Fed watching, monitoring of company events, and detecting insider trading. Although students write their own computer programs in this course, they are not required to implement most algorithms from scratch. Instead, the focus of this course is on how to use existing state-of-the-art open-source software libraries and how to apply them in a financial context. This course consists of three parts. In the first part, we work with real-world textual data sets to obtain proficiency in collecting, importing, organizing, and cleaning textual data from sources related to finance and economics. Among others, we cover web scraping, textual corpora, text processing, tokenization, stemming, and stop word removal. In the second part we delve into a more detailed analysis of NLP, text analytics, and machine learning with a particular focus on FinTech. For instance, we examine bag-of-words, word weighting schemes, document classification, document clustering, sentiment analysis, and topic models. The third part consists of summarizing, displaying, and visualizing results obtained from NLP and text analytics for applications in finance and economics.