1. Understand management control systems as a component of the management model of the organization;
2. Develop critical reasoning ability about the models and management control tools and their adequacy to the organizations and stakeholders? needs;
3. Identify management control tools and structure them into a management and company performance driven system;
4. Be able to integrate management information into information systems

I - Management control framework
II - Financial performance for management control
III - Strategic and operational control

1) Periodical evaluation: Instruments: case solving/Work, in group/individual (40%) and an individual written final test (60%). Requires a minimum grade of 7,5 points in each element (or group of elements), attendance to classes of at least 2/3, and a minimum of 10 points in the final classification. 2) Exam (1st sitting): written test (100%), requiring minimum 10 points to get approval. 3) Exam (2nd sitting): written test (100%), requiring minimum 10 points to get approval. Scale: 0-20 points

O1 Knowledge and comprehension of data science.
O2: Understand the history of machine learning and the different types of machine learning.
O3: Acquire a working knowledge of Python.
O4: Understand the concepts that permit performing an Exploratory Data Analysis (EDA).
O5: Acquire knowledge and understanding of Data Wrangling mechanisms,.
O6. Learn and be familiar with Data Visualization mechanisms.
O7. Know and comprehend the supervised algorithms: decision trees, linear and logistic regression, support vector machines (SVM), and naive bayes classification.
O8. Know how to use continuous and categorical variables in machine learning algorithms; differentiate between classification and regression problems.
O9. Comprehend reinforcement-based Q-learning algorithms.
O10. Comprehension of Artificial Neural Networks (ANN).
O11. Comprehension of Recurrent Neural Networks (RNR), Convolutional Neural Networks (CNN) and Computer Vision techniques.
O12. omprehend of time series applications.

CP1. Introduction to Data Science
CP2. Introduction to Machine Learning: History, fundamentals, and basic concepts
CP3. Introduction to the Python programming language
CP4. Exploratory Data Analysis (EDA): Part 1 - Data Wrangling with Pandas
CP5. Exploratory Data Analysis (EDA): Part 2 - Data Visualization with Matplotlib / Seaborn
CP6. Supervised Learning: SVM, Decision Trees, Linear and Logistic Regressions, Random Forests
CP7. Unsupervised Learning: K-means clustering; Reinforcement Learning: Q-Learning
CP8. Classification and Regression Problems; Numeric / Continuous and Categorical / Discrete Variables
CP9. Artificial Neural Networks
CP10. Deep Learning: Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs); Computer Vision
CP11. Time Series

Given the practical nature of the contents, the assessment will encompass a project. Its subject should be aligned with all or part of the syllabus.
Project subject proposal (5%).
Project (95%, including teamwork (report and software) ? 40%, and oral exam ? 55% ).
All components of the project - proposal, report, software and oral exam, are mandatory. The minimal classification for each component is 10 on a scale of 0 to 20.
There will be a unique deadline for submitting the project, except for students accepted to the special period of assessment, that will be allowed to submit during that period.
Presence in class is not mandatory.
There is no final exam.
Students aiming to improve their classification can submit a new project in the following scholar year.

OA1. Deepen the mastery of the most used frameworks for IT governance.

OA2. Acquire critical thinking about practical constraints regarding business/IT alignment.

OA3. Realize the impact that a good / bad IT investment can bring in relation to success for the business.

OA4. Gain awareness of the various mechanisms for IT governance and how these can be used to achieve a better strategic alignment between business and IT.

OA5. Understand how risk and compliance management is directly related to governance and what influence does it has on its success.

OA6. Understand the role of IT in innovation and how IT can help the business to differentiate itself in the marketplace.

OA7. Understand the importance of planning properly and analyzing the benefits.

CP1 [Principles and concepts]
Main principles and concepts on IT governance.

CP2 [IT governance vs management]
Main differences between governing and managing IT.

CP3 [IT governance mechanisms]
Mechanisms for IT governance, as well as their functionalities.

CP4 [IT strategy management]
Clarify the importance of good IT planning and keep your business strategy and IT aligned.

CP5 [IT value]
Understand the cost / benefit of IT investments.

CP6 [IT performance]
Application and relevance of a Balance ScoreCard in IT.

CP7 [IT risk management]
Importance of risk management and how it can be applied.

CP8 [IT compliance management]
The importance of keeping in line with the various external and internal policies.

CP9 [Innovation]
How to use IT to empower business.

CP10 [Frameworks for IT governance]
Introduction to the main frameworks in the market for guiding/supporting IT governance.

Given the nature of the contents taught, the evaluation will encompass an individual assignment. Its subject should be aligned with part of the syllabus. Although not mandatory, the contextualization of this assignment in the reality of companies will be encouraged.

First delivery - 30%
Final delivery - 50%
Presentation - 20% (Public session)

The presentation is the only assessment to be performed during the first season

Students still have second season and special season to deliver the project weighing each 100%.

This UC does not have exam.

O1. To understand basic ideas of management;

O2.To understand the historical approach of management;

O3.To understand the complexity of globalization;

O4.To understand the basic ideas of knowledge management.

O5.To understand the learning organization as knowledge discipline;

O6. To understand how important is the organizational culture to improve the organizational performance.

1. Basic foundations of management
2. Pioneering ideas of management
3. Globalization phenomenon
4. Knowledge management
5. Organizational learning
6. Organizational culture
7. Knowledge management and innovation.

OPTION 1:
Assessment throughout the term:
1.Involvement in class activities - 20%.
-Levels of attendance and punctuality.
-Participation in class.
-Answer of questions in class.
2. Test - 80%.
Passing grade is 10 points, with at least 7,50 points (out of 20) in the test.

OPTION 2:
End-of-term exam - 100%.
A positive evaluation means a grade of 10 or above (over 20).

LO1. Get an overview of the process modeling application domains and of languages currently used in it, as well as in the near future.
LO2. Be able to synthesize process models from a natural language requirements specification.
LO3. Understand and acquire critical thinking regarding complex pre-existing process models, being able to identify good and bad modeling practices.
LO4. Be able to identify the requirements for an interactive process modeling environment.
LO5. Understand how a model can be used to simulate and/or understand the execution of a process.
LO6. Be able to retrieve a process model from the execution events of the same and understand the limitations of this mining.
LO7. Understand the role of process models in the "low-code" development paradigm.

PC1 [Introduction and Motivation]
The role of process modeling. Comparison of process modeling languages.
PC2 [Synthesis of models with BPMN]
Syntax and semantics of BPMN modeling constructs.
PC3 [Quality of models]
Catalog of workflow patterns that convey best practices in process modeling. Defects detection.
PC4 [Modeling environments]
Overview of existing modeling tools, cooperative modeling and process model interoperability formats.
PC5 [Process simulation]
Practice of process simulation to evaluate alternative scenarios.
PC6 [Process mining]
Reverse engineering of process models from events generated during their execution, using object-centric approaches and the support of LLMs.
PC7 [Low-code development]
Process model-based low-code development platforms.
PC8 [Decision management]
The DMN (Decision Model and Notation) standard for decision management.

According to the Regulation of ISCTE-IUL no. 436/2014, two modalities are contemplated:

i) Periodic evaluation - Group assignment with 2 deliveries (roughly at the middle and end of the semester) and weight of 40% (group of 3 students) or 50% (group of 2 students). The remaining 60% or 50% of the final classification will be obtained through an individual final test during the 1st Exam Season. A minimum score of 9/20 should be obtained in each of the components (group assignment and individual test).

ii) Final exam with a weight of 100% - This modality can be chosen in the 1st Season by those who did not take the periodic evaluation and is the only one available in the 2nd Season and in the Special Season.

LG1. Understand why agile methodologies are important for project management specially for ITSM

LG2. Understand why ITSM is important and how complex is to apply it.

LG3. Learn about the main standards and frameworks of ITSM. The main focus should rely on ITIL.

LG4. Understand how DEVOPS works as well as how it links with ITIL and ITSM.

LG5. Learn about SCRUM methodology. Which are the main advantages and how one can apply it in practice to better manage ITSM projects.

LG6. Teach students the main roles and responsibilities of SCRUM, ITIL and DEVOPS as well as how these methodologies can works all together to promote IT service improvement and increase organizational productivity.

This UC has the following program contents (CPs):

CP1 [Frame and Motivation]
Global vision of ITSM

CP2 [ITIL & ITSM]
Introduce the main ITSM framework: ITIL

CP3 [DEVOPS]
Introduce DEVOPS methodology and respective practices

CP4 [ITIL & DEVOPS]
Explain how DEVOPS and ITIL can work together to improve the ITSM

CP5 [SCRUM]
Introduce and explain SCRUM. Detail its content and elements as well as their meaning and field of application.

CP6 [SCRUM & ITSM]
Explain how SCRUM and ITSM relate. Plus, detail the advantages of using SCRUM in ITSM projects.

CP7 [Applicational Architecture]
Explain how the approached methodologies can work all together aiming to both improve ITSM efficiency and improve ITSM project success.

This CU does not have an exam. Its practical approach is assessed by a project.

Periodical Evaluation
Group assignment
Part1 (P1) - 40% of the grade
Part2 (P2) - 40% (first season)
Individual presentation (IP) - 20% (first season)

Formula::
Final grade = (P1*0,4+P2*0,4 + IP*0,2)

The students can:
Second season: improve their IA or deliver a new project for 100% of the grade (indiviual assignment without oral discussion)
Special season: deliver a new project for 100% of the grade (indiviual assignment without oral discussion)

At the end of this course, students should:

O1. Know the general sequence of procedures that take place in an investigation.
O2. Know how to identify / confirm the existence of open problems, through a systematized literature review.
O3. Know the rudiments of various methodological approaches to research and understand their applicability conditions.
O4. Have had contact with various techniques and tools used by researchers.
O5. Learn how to select the processes of data collection, organization, processing and analysis.

1st SEMESTER
P1: State of the art and research questions
It involves a critical literature review, guided by a protocol defining the research scope and inclusion and exclusion criteria. The characterization of the state of the art should justify the choice of the topic and the relevance of the research questions.
P2: Training of writing and presentation skills for scientific works
Encompasses participating in seminars, cooperative workshops (e.g. posters session), mini courses and attending public defenses.

2nd SEMESTER
P3: Implementation and validation of proposed contributions
The validation of contributions must be made by demonstrating the compliance with the steps of the adopted scientific method(s), and/or by comparing the results obtained with the state of the art, and/or through their dissemination in peer-reviewed scientific venues.
P4: Dissertation writing
Must comply with the graphic presentation standards in force.

I) PERIODIC EVALUATION
- 100% Group project (first season)

The student can use the 2nd season or special season (individual project and different topic).

This course includes the following learning objectives:
LG1 | work on analytical dimensions (composite variables) using principal components analysis
LG2. Conduct and interpret a Principal Components Analysis.
LG3. Analyse the reliability of the new composite variables
LG4. Build the new composite variables.
LG5. Develop and deepen the knowledge in multiple linear regression (MLR)
LG6. Apply, analyze and statistically interpret results from a MLR
LG7. Report the results in a thesis/article

1. Discussion of papers to illustrate the main topics of the program
1.1. Dimension reduction
1.2. Contextualization of the hypotheses in the research context.
2. Principal Component Analysis (PCA)
2.1. Introduction
2.2. Definition of principal components
2.3. Eigenvalues and communalities
2.4. Criterions to extract principal components
2.5. Interpreting the principal components (via loadings)
2.6. Rotation of the principal components
2.7. Computing and interpreting factor scores
2.8. Reliability analysis
2.9. Creating summated scales
3. Multiple Linear Regression (MLR)
3.1. Objetives
3.2. Parameter Estimation
3.3. Evaluating the quality of the model (R-square)
3.4. Inference on the model (F test)
3.5. Inference on the parameters (t-test)
3.6. Effect size of the predictors
3.7. MLR assumptions
4. Report PCA and MLR in a thesis/paper

Individual study, based on the suggested bibliography, is oriented and supported by lessons of different types:
1. Theory practice lessons: conceptual approach of methods. Different examples are also presented and discussed (papers);
2. Laboratory lessons: these sessions allow students to apply the different statistical methods, and also to interact with statistical software.
In this practical lessons students also prepare the main results to be reported in thesis/paper format.

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The periodic evaluation includes:
1. Individual test (65%)
2. Group report (35%).

Students must obtain a minimum grade of 8.0 on the individual test and 10.0 on the group report.

The evaluation can be made through a final examination with the same components as the periodic evaluation:
1. Individual test (65%)
2. Group (or individual) report (35%).

OA1: Know how to systematically review the relevant literature in a given scientific domain, to draw a picture of the state of the art and to identify research questions and expected contributions.
OA2: Have applied one or more research methods to answer research questions, resulting in one or more technical-scientific contributions.
OA3: Know how to validate contributions and identify the corresponding validity threats.
OA4: Have learned how to prepare a master's thesis with quality, both in form and content.
OA5: Being able to present a scientific work and to argue about the validity of its contributions.

1st SEMESTER

P1: State of the art and research questions
It involves a critical literature review, guided by a protocol defining the research scope and inclusion and exclusion criteria. The characterization of the state of the art should justify the choice of the topic and the relevance of the research questions.

P2: Training of writing and presentation skills for scientific works
Encompasses participating in seminars, cooperative workshops (e.g. posters session), mini courses and attending public defenses.


2nd SEMESTER

P3: Implementation and validation of proposed contributions
The validation of contributions must be made by demonstrating the compliance with the steps of the adopted scientific method(s), and/or by comparing the results obtained with the state of the art, and/or through their dissemination in peer-reviewed scientific venues.

P4: Dissertation writing
Must comply with the graphic presentation standards in force.

The assessment at the end of 1st semester include: research proposal, introduction, literature review, the plan for the next sections, and a presentation.
The final assesment will take into consideration the previous assessment, the quality of the investigation (document), and the quality of the presentation and public discussion.

OA1: Know how to systematically review relevant literature in a given technical-scientific field, including technical reports, standards, white papers or tutorials, to substantiate a problem and propose a solution.

OA2: Have selected one or more methodological approaches to achieve the project?s objectives, resulting in one or more technical-scientific contributions.

OA3: Know how to validate the artifacts that constitute the solution to the chosen problem and identify the corresponding validity threats.

OA4: Have learned how to prepare a master's project with quality, both in form and content.

OA5: To be able to present a technical-scientific problem and its motivation, to describe the project carried out to produce a solution for the same and to argue about the validity of the same.

1st SEMESTER
P1:Motivation to the problem and preliminary design of the solution
It involves the review of technical-scientific literature, guided by a protocol and its conclusions should be confirmed by domain experts. This step should clarify the problem relevance and the preliminary design of its solution
P2:Training of writing and presentation skills for technical-scientific works
Encompasses participating in seminars, cooperative workshops (e.g. posters session), mini courses and attending public defenses
2nd SEMESTER
P3: Implementation and validation of the proposed solution
The implementation implies the refinement of the design. Validation implies compliance with the steps of the adopted methodological approach(es) and comparing the proposed solution with the state of the art and/or their dissemination in technical-scientific venue(s) with peer review (e.g. tool demo session in conference)
P4: Project?s report writing
Must comply with the graphic presentation standards in force

[10% during 1st sem.]
Assiduity in participation in seminars,mini-courses and attending defenses (to announce).
[25%, final 1st sem.]
Assessment of project's report chapters 1 (problem motivation) and 2 (preliminary design of solution) in the official format and the presentation and discussion of a poster on an ISTAR-IUL workshop
[65%, final 2nd sem.]
Project's public defense, which will evaluate the delivered report, its presentation, argumentation capabilities,autonomy and possible dissemination.