ECTSCodeAvailability
5.0MGDI_EGOEnglish*

Syllabus:

General Objectives:
1. To understand organization?s benefits of a strategic focus culture;
2. To monitor the evolutionary perspectives of strategic management;
3. To know the fundamental theories of management and administration;
4. To understand the management process based on its explanatory theories;
5. To structure critical thinking about the management process associated.

SPECIFIC OBJECTIVES: Upon completion of this Course, the student should be able to:
1. To apply the appropriate concepts to develop and implement a strategy at the level of a business;
2. To apply industry analysis techniques, environmental analysis and competition analysis;
3. To select and implement a strategy and specify barriers typically underlying these practices.
4. To link the theoretical concepts and ideas that allow long run risks minimization.
5. Critically evaluate the fundamentals of management based on students? theory and research

CONTENTS

Part I: Basics of Management
1. Introduction to Strategy
2. Analysis of Strategic Positioning
3. Strategy Choice and Formulation
4. Information and Strategy
5. Strategic Management and Integrated Management Systems

Part II: Management Theories
1. Resource-based Theory
2. Theory of Transaction Costs;
3. Agency theory
4. Governance Theory
5. Knowledge-based theory

*English* The courses available in English are depending on the number of attending students

ECTSCodeAvailability
5.0MGDI_MIEnglish*

Syllabus:

The aim is to establish a foundation of theoretical and practical knowledge, equipping students with the main methodological tools of scientific research. This will enable them to prepare for conducting research and analyzing qualitative data, in order to define a research problem, identify and synthesize the most relevant literature, select the appropriate research strategy, and apply suitable techniques for data collection and analysis.
SPECIFIC OBJECTIVES: Upon completion of this course unit, the student should be able to:
? SO1. Understand the process of scientific research;
? SO2. Determine appropriate methodological approaches for different research problems;
? SO3. Demonstrate critical capacity in the interpretation and discussion of results;
? SO4. Conduct qualitative research.

CONTENTS

1 – Research Design and Strategy
2. Research Ethics
3. The Research Process
3.1. Research Sources: Information Gathering and Databases
3.2. The Process of Academic Reading and Writing
3.3. Data Collection ? Qualitative vs. Quantitative Data
3.4. Bibliographic References ? Mendeley
4. Qualitative Methodologies
4.1. Qualitative Research Methods
4.2. Data Coding
4.3. Bibliometric Analysis
4.4. Content Analysis
5. The Integration of Quantitative and Qualitative Research
5.1. Bibliometric Analysis

*English* The courses available in English are depending on the number of attending students

ECTSCodeAvailability
5.0MGDI_IMSEnglish*

Syllabus:

GENERAL OBJECTIVES:
GO1 – To use mathematical models, techniques and methods suitable for the analysis of the real situation, its interpretation and simplification
GO2 – To select and use the most appropriate methods and software to solve industrial optimization problems
GO3 – To analyze, interpret and evaluate industrial processes through simulation

SPECIFIC OBJECTIVES: Upon completion of this course, the student should be able to:
O1 – To model industrial problems using Integer Programming (IP) and Mixed Integer Programming (MIP) formulations
O2 – To solve IP and MIP problems, using the appropriate techniques and tools
O3 – To know information systems and simulation technologies
O4 – To formulate and solve problems through industrial simulation

CONTENTS

C1 – Integer Programming
Introduction and applications of Integer Programming
Modeling a simple Integer Program
Branch-and-bound method
Preprocessing: tightening bounds, redundant constraints, ?
C2 Formulation of IP and MIP Models
Differences between linear, integer, and mixed programming
Assumptions of Mixed Integer Programming
Modeling frameworks
Modeling with binary, integer, and continuous variables: fixed cost, logical and conditional constraints, implications and disjunctive conditions, covering, packing, and partitioning constraints
Modeling practical problems: knapsack, assignment, and location problems
Current challenges: scalability; integration with AI and ML
C3 Industrial Simulation
Types of simulation models and software.
Statistical aspects of the simulation
Verification and validation of simulation models
Modelling of systems (goods and services) with a professional simulation tool
Analysis of results and iterative process of generating alternative scenarios

*English* The courses available in English are depending on the number of attending students

ECTSCodeAvailability
30.0MGDI_TIGTutorial

Syllabus:

GENERAL OBJECTIVES:

1. Examine and criticize themes present in the current theoretical and empirical debate on the management of organizations
2. Assess / criticize the role of management in organizations
3. Develop and support a management research program suggesting feasible research avenues

SPECIFIC OBJECTIVES: Upon completion of this Course, the student should be able to:
1. Prepare, present and discuss a dissertation/project proposal on a relevant topic in management.
2. Develop an article of a scientific nature applied in the area of management

The objectives of the course are mainly aligned with SDGs 4, 8, 9, and 12, as they promote quality education, foster responsible management practices, encourage organizational innovation, and contribute to sustainable consumption and production.

CONTENTS

– Preparation of a scientific article for the ESTG Masters event.

– Review of articles submitted to ESTG Masters by classmates.

– Public presentation of the article at the event.

– Preparation of the dissertation/project proposal.

BUSINESS SCIENCES
ECTSCodeAvailability
5.0MGDI_ADITutorial

Syllabus:

GENERAL OBJECTIVES
Provide students with skills to:
OG1- Acquiring habits of critical reflection and developing research skills, synthesis, structuring and presentation of information from data processing;
OG2- Perspect statistics as a tool for decision making;
OG3- Handle statistical software.
SPECIFIC OBJECTIVES
Upon completion of this course unit, the student should be able to:
SO1- Recognize and identify the different statistical methodologies, their potentialities and limitations;
SO2- Acquire knowledge of probabilistic nature;
SO3- Correctly identify a probabilistic model and apply it in practical cases;
SO4- Know how to use inferential statistics in concrete problems;
OE5- Identify, define and apply the most appropriate parametric and nonparametric hypothesis test;
SO6- Use free software for problem solving, analysis and interpretation involving data analysis.

CONTENTS

C1- Introduction to the foundations of statistics: Descriptive statistics; data pre-processing; outliers detection; Detection of relevant information and leverage values; variables association.
C2- Elements of sampling theory: Data collection technique; Definition of samples;
C3- Main theoretical distributions of random variables: discrete theoretical distributions; continuous theoretical distribution; data fit models
C4- Statistical inference: Hypothesis tests, parametric and nonparametric;
C5- Use of free statistical software and de Free visualization tools.

ECTSCodeAvailability
6.0MGDI_MQEnglish*

Syllabus:

O1 – Identify and select statistical and data science tools applicable to diverse industrial contexts.
O2 – Apply multivariate methods and modeling techniques for decision support.
O3 – Integrate data from multiple sources (industrial, simulated, or public) to generate actionable insights.
O4 – Critically interpret results and propose data-driven solutions, considering economic and sustainability impacts.

The course contributes to SDGs 4, 8, 9, and 12 by developing skills in data analysis, decision support, industrial innovation, and sustainable solutions.

CONTENTS

C1. Factor analysis and dimensionality reduction (PCA, t-SNE).
C2 – Multiple linear regression and regularization (Ridge, Lasso).
C3 – Discriminant analysis and supervised classification (LDA, QDA, KNN).
C4 – Cluster analysis: hierarchical, K-means, and DBSCAN.
C5 – Logistic and multinomial regression, advanced classification techniques.
C6 – Survival models.
C7 – Introduction to machine learning and neural networks for industrial data.

*English* The courses available in English are depending on the number of attending students

ECTSCodeAvailability
30.0MGDI_D/APTutorial

Syllabus:

1. Apply multidisciplinary and interdisciplinary knowledge acquired throughout the formative process;
2. Prepare a technical / scientific text;
3. Develop a thesis / advanced project with a view to obtaining the Master degree.

The objectives of applying transversal and interdisciplinary knowledge, producing technical-scientific texts, and developing advanced dissertations or projects contribute to the SDGs by promoting quality education (SDG 4), innovation and sustainable infrastructure (SDG 9), and applied knowledge for sustainable development (SDG 17).