Business Administration

This course is an introductory study of contemporary organizations and their management. It explores the types of purposes of organizations, their stakeholders and changing environments together with their key managerial processes – entrepreneurship, organizational structure, leading, strategic planning and change. The focus throughout is on helping students achieve a critical and reflective approach, and learning to apply relevant concepts, tools, and models. The course provides a platform for later study by encouraging skills in critical thinking, academic writing, concept acquisition, and research. 

This intermediate international business course solves a challenge related to the internationalization of companies. As a learning outcome, the class develops skills to understand the processes required to conduct business in Mexico using various input methods while grasping the processes involved and, of course, understanding how cultural differences must be analyzed when doing business in an international context.

Course topics include:

• History of Mexico and the political environment
• Mexican economy
• Mexican infrastructure
• Trade agreements
• Cultural patterns and business protocol
• Management styles
• Work and labor conditions in Mexico

This course addresses the business and social issues related to the use of modern biotechnology. Topics include business development in the industry, operations in running a bio-business, manufacturing practice, drug development process, regulation and policy, intellectual property issues, environmental aspects of biotechnology, ethical and public concerns.

This course examines the impact of Artificial Intelligence (AI) on work processes in organizational contexts, on the professions and on expertise. It starts with defining what AI is, examining how it exactly works, how it is used in professional contexts, and how its uses should be regulated. The course uses a number of case studies from across finance, health organizations, or urban planning to investigate how AI changes the professions, expertise, and organizations. 

This course provides students with a broad understanding of international business, including the fundamental abilities and knowledge essential to the doing of business internationally: both in theory and practice. The course offers a foundation in international business. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by permission of the instructor. This course focuses on the main data mining methods used in knowledge discovery in business employing internal and external data. With an emphasis on data analysis and on the use of a software, special attention is devoted to techniques that help to single out the relationships of interdependence and patterns in business and market research phenomena. Students learn, hands-on, how to organize and analyze market research data. In particular, at the end of the course students are able to: independently run a complete data mining process (from data pre-processing to the interpretation of obtained results); choose the best suited statistical methodology for the problem at hand; to critically interpret empirical results.

The course content is divided as follows:

1. INTRODUCTION: data-analytic thinking, overview of Data Mining, from business problems to Data Mining tasks, the Data Mining process; real-world business challenges.

2. DATA EXPLORATION AND PREPARATION: data objects and attributes type, data matrices and their transformations, data cleaning.

3. STATISTICAL AND DATA MINING SOFTWARE: introduction to SAS; SAS LAB tutorial on data organization  and data preprocessing using real datasets.

4. MULTIDIMENSIONAL DATA ANALYSIS & DIMENSIONALITY REDUCTION: Principal component analysis and its variants (e.g., PCA of ranks); Multiple Correspondence Analysis - categorical pattern detection. Theory and practice with SAS.

5. PROXIMITY MEASURES: distance and similarity for mixed data.

6. CLUSTERING: hierarchical, partitional and hybrid clustering. Understanding the Results of Clustering.

7. PROFILING: deriving typical behavioral segments. 

8. CO-OCCURRENCES AND ASSOCIATIONS: Finding items that go together. Theory and application of main association rules algorithms in SAS.

9. Data Mining SCORING: Theory and practice.

10. Causal ML and Advanced Lab:  causal inference fundamentals; application of causal ML algorithms in the context of business analytics for decision support; evaluate a marketing campaign using causal ML in SAS; targeting and interpreting causal results.

This course examines the core theories and concepts of managing human behavior in organizations. It covers a variety of theories and concepts to provide a foundational understanding of the attributes of individual behavior in organizations, including personality, motivation, decision-making, as well as interpersonal behaviors, including teamwork, power and influence, leadership, and communication. 

Designed for STEM students, this course systematically cultivates career planning capabilities. Beginning with self-assessment, students develop core competencies in time management, leadership, and teamwork through occupational exploration, goal-setting exercises, and decision-making training – enhanced by field visits, alumni panels, and workplace simulations. The curriculum focuses on guiding school-to-workplace transition, strengthening career adaptability, and targeted development of innovation literacy. Integrating theoretical instruction with hands-on practice, it empowers personalized career exploration and planning.

This course addresses the definition of business models and the understanding on how to identify and build existing and innovative business models. The objectives of the course are for students to understand the fundamentals of business models, recognize the strategic importance of business models, identify and differentiate various types of business models, analyze hybrid business models, and examine the relationship between business models and industry lifecycles.