Accreditations
The master degree in Information Systems Management lasts for two years, and is comprised of 120 ECTS credits, divided between: 48 in compulsory curricular units, 24 in optional courses, 6 in free electives and 42 for the dissertation or project.
The indicative electives will be made available in a sprecific timetable so that the student can choose the recommended course units, or others, that can be adjusted to their schedule.
Note:
Please note that the Master's programme in Informatics and Management is currently being restructured. In this context, there is a possibility that the 24/25 academic year will start with a new programme. Until information to the contrary is made available, it should be assumed that the start will take place with the old programme, which is described in detail here. Admitted candidates will be notified if the new plan is implemented as early as 24/25. Please note that the final decision depends on organisations outside ISCTE. If the new plan is adopted, the curricular units will be:
- Leading Digital Transformation and Innovation
- Digital Transformation
- Disruptive Technologies
- Fundamentals of Information Technology Governance
- Artificial Intelligence for Business
- Business Process Management
- Agile Project Management
- Agility in the Digital Age
- Introduction to Research in Computer Science and Management
- Master's Dissertation or Project Work
- Optional
- Optional
- Optional
- Optional
Programme Structure for 2024/2025
| Curricular Courses | Credits | |
|---|---|---|
| 1st Year | ||
|
Management Control Systems Design
6.0 ECTS
|
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|
Data Science Fundamentals
6.0 ECTS
|
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|
It Governance Fundamentals
6.0 ECTS
|
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|
Management of Organizational Knowledge and Culture
6.0 ECTS
|
||
|
Process Modeling and Deployment
6.0 ECTS
|
||
|
Agile Project Management
6.0 ECTS
|
||
|
Introduction to Research in Computer Science and Business Management
6.0 ECTS
|
||
|
Advanced Data Analysis Methods
6.0 ECTS
|
||
| 2nd Year | ||
|
Master Dissertation in Computer Science and Business Management
42.0 ECTS
|
||
|
Master Project in Computer Science and Business Management
42.0 ECTS
|
Management Control Systems Design
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 indicators
III - Strategic and operational control
1) Assessment throughout the semester: 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), 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
Title: Drury, C. (2024). Management and Cost Accounting. 12th Edition. Cengage Learning.
Jordan, H., Neves, J.C., e Rodrigues, J. A. (2021). O Controlo de Gestão - ao Serviço da Estratégia e dos Gestores. 11ª edição. Áreas Editora.
Major, M. e Vieira, R. (2017). Contabilidade e Controlo de Gestão: Teoria, Metodologia e Prática. 2nd Edition. Escolar Editora.
Merchant, K. A. e Van der Stede, W. A. (2017). Management Control Systems. Performance Measurement, Evaluation and Incentives. 4th Edition. Pearson.
Simons, R. (2013). Performance Measurement and Control Systems for Implementing Strategy Text and Cases. 1st Edition. Pearson.
Authors:
Reference: null
Year:
Title: Bhimani, A. (2006). Contemporary Issues in Management Accounting. Oxford University Press.
Bhimani, A., Horngren, C.T., Datar, S.M. and Rajan, M. (2023). Management and Cost Accounting. 8th Edition. Pearson.
Chapman, C. S., Hopwood, A. G. and Shields, M. D. (2009). Handbook of Management Accounting Research. Elsevier. Volumes 1, 2, 3.
Garrison, R., Noreen, E. and Brewer, P. (2024). Managerial Accounting. 18th Edition, McGraw-Hill.
Hopper, T., Scapens, R. W. and Northcott, D (2007). Issues in Management Accounting. 3rd edition. Prentice Hall.
Humphrey, C., and Lee, B. (2007). The Real-Life Guide to Accounting Research: A Behind the Scenes View of Using Qualitative Research Methods. CIMA Publishing
Authors:
Reference: null
Year:
Data Science Fundamentals
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.
Title: Mueller, J. P. (2019). Python for Data Science for Dummies, 2nd Edition (2.a ed.). John Wiley & Sons.
Grus, J. (2019). Data science from scratch: First principles with python (2.a ed.). O?Reilly Media.
Raschka, S. & Mirjalili, V. (2019). Python Machine Learning : Machine Learning and Deep Learning With Python, Scikit-Learn, And Tensorflow. Birmingham: Packt Publishing, Limited.
Avila, J. (2017). Scikit-Learn Cookbook - Second Edition. Birmingham: Packt Publishing.
Theobald, O. (2017). Machine Learning for Absolute Beginners: A Plain English Introduction. United States.
Ller, A. & Guido, S. (2017). Introduction To Machine Learning with Python: A Guide for Data Scientists. Sebastopol, CA: O'Reilly Media, Inc.
VanderPlas, J. (2016). Python Data Science Handbook. O?Reilly Media.
Authors:
Reference: null
Year:
Title: McKinney, W. (2022). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter (3.a ed.). O?Reilly Media.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, And Tensorflow : Concepts, Tools, And Techniques to Build Intelligent Systems. Sebastopol, CA: O'Reilly Media, Inc.
Authors:
Reference: null
Year:
It Governance Fundamentals
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.
Title: ?Slides de Fundamentos de Governação das TI, Rúben Pereira, disponíveis na plataforma de e-learning (à medida que os temas forem introduzidos), 2017/2018
?Enterprise Governance of Information Technology: Achieving Strategic Alignment and Value, Van Grembergen and Steven de Haes, 2009th Edition, Springer, 2009.
?IT Governance: How Top Performers Manage IT Decision Rights for Superior Results, Peter Weil and Jeanne Ross, Harvard Business School, 2004
Authors:
Reference: null
Year:
Title: ?Artigos científicos que serão explicitamente indicados na plataforma de e-learning
?IT Governance: Policies and Procedures, Michael Wallace and Larry Webber, 2017 Edition, Wolters Kluwer, 2016
?Implementing World Class IT Strategy: How IT Can Drive Organizational Innovation, Peter A. High, 1st Edition, Jossey-Bass, 2014
?Governance, Risk Management, and Compliance: It Can't Happen to Us--Avoiding Corporate Disaster While Driving Success, Richard M. Steinberg, Wiley, 2011
?Adventures of an IT Leader, Robbert D. Austin and Richard L. Nolan, Harvard Business School, 2009
Authors:
Reference: null
Year:
Management of Organizational Knowledge and Culture
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).
Title: Schein, E. H. (2004). Organizational culture and leadership. London: The Jossey-Bass Business.
Nonaka, I.; Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
Hofstede, G. (1991). Culture and Organizations: Software of the Mind. London: McGraw-Hill.
Fernandes, A. (2007). Tipologia da aprendizagem organizacional: Teorias e Práticas. Lisboa: Livros Horizonte.
Davenport, T.; Prusak, L. (1998). Working Knowledge. Cambridge, MA: Harvard Business School Press.
Chesbrough, H., (2003). Open Innovation - The New imperative for creating and profiting from technology. MA: Harvard Business School Press
Bartol, K. e Martin, D. (1998). Management (3ª Ed.). Boston, MA: McGraw-Hill.
Argyris, C.; Schon (1978). Organizational Learning: A Theory of Action Perspective. Reading: Addison-Wesley.
Authors:
Reference: null
Year:
Title: Nonaka, Ikujiro, & Hirotaka Takeuchi. 1995. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York, NY: Oxford University Press.
Hofstede, Geert. (2001) Culture's Consequences : Comparing Values, Behaviors, Institutions, and Organizations across Nations. 2nd ed. Thousand Oaks, CA: Sage Publications.
Iskandar, K., Jambar, M., Kosala, R. & Prabowo, H. (2017). Current Issue of Knowledge Management System for Future Research: A Systematic Literature Review. Procedia Computer Science 116 (2017) 68-80
Ferreira, M. P., Santos, J. C., Reis, N. & Marques, T., (2010) Gestão Empresarial. Lisboa: Editora LIDEL.
Donnely, J. (2000) Administração: Princípios de Gestão Empresarial. 10ª Ed., Lisboa: McGraw Hill.
Davenport, Thomas H., & Lawrence Prusak (1998) Working Knowledge: How Organizations Manage What They Know. Cambridge, MA: Harvard Business School Press.
workers . Boston, MA : Harvard Business School Press .
Davenport , T. (2005) Thinking for a living, how to get better performance and results from knowledge
Dalkir, K. (2011) Knowledge Management in Theory and Practice. Cambridge, Massachusetts: the MIT Press.
Authors:
Reference: null
Year:
Process Modeling and Deployment
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.
Title: Freund, J. and Rücker, B., Real-life BPMN: Using BPMN 2.0 to analyze, improve, and automate processes in your company (4th edition), 2019, Camunda,
Volker Stiehl, Process-Driven Applications with BPMN. Springer., 2016, Springer,
Russell, Nick, Wil van der Aalst, and Arthur Hofstede, Workflow patterns: the definitive guide. MIT Press., 2016, MIT Press,
White, S. A., & Bock, C., BPMN 2.0 Handbook Second Edition: Methods, Concepts, Case Studies and Standards in Business Process Management Notation, 2011, Future Strategies Inc.,
Authors:
Reference: null
Year:
Title: Laliwala, Z. and Mansuri, I., Activiti 5. x Business Process Management Beginner's Guide, 2014, Packt Publishing Ltd.,
Tijs Rademakers, Activiti in Action - Executable Business Processes in BPMN 2.0, 2012, Manning Publications Co.,
Nelson, M., & Williams, T., Oracle BPM Suite 11g: Advanced BPMN Topics: Master Advanced BPMN for Oracle BPM Suite Including Inter-process Communication, Handling Arrays, and Exception Management, 2012, Packt Publishing Ltd.,
Silver, Bruce, BPMN method and style, 2nd edition, with BPMN Implementer?s Guide, 2011, Cody-Cassidy Press,
Authors:
Reference: null
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Agile Project Management
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 curriculum unit does not have an exam. Its practical approach is assessed by a project.
Evaluation throughout the semester:
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 (individual assignment without oral discussion)
Special season: deliver a new project for 100% of the grade (individual assignment without oral discussion)
Title: Slides de GPA, Rúben Pereira, disponíveis na plataforma e-learning
The Unicorn Project; Gene Kim; 2019
The Phoenix Project; Kevin Behr, George Spafford and Gene Kim; 2013
DevOps for Dummies; Emily Freedom; 2019
ITIL Foundation: itil4; Stationery Office; 2019
The SCRUM Guide; Ken Schwaber and Jeff Sutherland; 2013
Authors:
Reference: null
Year:
Introduction to Research in Computer Science and Business Management
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) EVALUATION during the semester
- 25% first individual delivery (during semester)
- 75% Individual project (first season)
The student can use the 2nd season or special season (individual project and different topic).
Title: Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications (4th edition)
Berndtsson, M., Hansson, J., Olsson, B., & Lundell, B. (2008). Thesis projects: a guide for students in computer science and information systems. Springer Science & Business Media (2nd edition).
Saunders, Mark et al (2016), Research Methods for Business Students, England, Pearson Education Limited (7th edition).
Authors:
Reference: null
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Title: Artigos selecionados ilustrando a aplicação de vários métodos de investigação
Rúben Pereira. Várias coleções de slides sobre vários métodos, técnicas e ferramentas de investigação (a disponibilizar progressivamente na plataforma de e-learning)
Authors:
Reference: null
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Advanced Data Analysis Methods
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
Assessment during the semester includes:
1. Individual assessment - Written test (65%) - with a minimum mark of 8.5
2. Group assessment - Work (35%) - with a minimum mark of 10.
Assessment by exam includes two tasks:
1. Written test (65%) with a minimum mark of 8.5
2. Practical assignment (35%) handed in on the day of the exam. Minimum mark of 10.
Title: Nota: Materiais disponibilizados no e-learning e que apoiam aulas TP e aulas PL.
Tabachnick, B. and Fidell, L. (2013). Using Multivariate Statistics, USA, Person International Edition, 6ª ed.
Reis, E. (2001). Estatística Multivariada Aplicada, 2ªed, Lisboa, Edições Sílabo.
Maroco, J. (2010). Análise Estatística com o PASW Statistics (ex-SPSS), Pero Pinheiro.
Maqbool, R., Sudong, Y., Manzoor, N. and Rashid, Y. (2017). The Impact of Emotional Intelligence, Project Managers? Competencies, and Transformational Leadership on Project Success: An Empirical Perspective. Project Management Journal, vol. 48, 3.
Hair, J., Black, W., Babin, B. and Anderson, R. (2019). Multivariate Data Analysis. Pearson New International Edition (8th ed).
Authors:
Reference: null
Year:
Title: Kline, R. B., (2011). Principles and practice of structural equation modeling. 3rd ed. New York: Guilford Press.
Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), doi.org/ 10.1037/0033-2909.112.1.155.
Bryman, A. (2015). Social Research Methods, Oxford, OUP.
Authors:
Reference: null
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Master Dissertation in Computer Science and Business Management
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.
Title: A bibliografia desta unidade curricular será determinada pela UC Introdução à investigação em informática e gestão. Assim como, pelo contexto de investigação de cada aluno.
Authors:
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Recommended optative
The current study plan of the Master allows students to graduate with a specialization in one of two possible areas: Digital Transformation Technologies or Data Science.
In order for a student to have one of these specializations, he will have to register, using the optional curricular units (UC), of the 4 specific UC. Only if you successfully complete the 4 UC in that area you can complete the specialization.
The offering of Courses and Minors is subject to their selection by a required minimum number of students. It is also important to consider that there are restrictions on the number of enrollments per course.
Digital Transformation Technologies:
03691 | Blockchain
03557 | Business Process Management
02674 | Cloud Techonologies and Systems
04401 | Disruptive Technologies
04412 | Digital Transformation
03579 | Iot for Smart Cities
03746 | Internet of Things Laboratory
Data Science:
03363 | Computational Intelligence and Optimization
02864 | Algorithms for Big Data
02870 | Text Mining
03209 | Data Science Fundamentals
Objectives
The course aims to equip participants with integrated abilities in organizational Information Systems, using the most appropriate and current methodologies, technologies and management principles. It aims to provide students with scientific and methodological skills corresponding to the 2nd cycle in the technological and management fields.
With this master's degree students will have the possibility to deepen knowledge about the main topics required and used by the labour market with regard to the governance and management of information technologies for the benefit of business. Agile, scrum, DevOps, project management, IT governance, IT service management, BPMN, process optimization, data science, ITIL, COBIT, will be some of the main approached topics.
It offers a subsequent cycle of studies to 1st cycle graduates in Information and Business Management, as well as to the graduates of other institutions. It also intends to affirm itself as an alternative for graduates who have followed a 3-year plan and who are looking for a training and learning process that follows standards of a high level of demand and result-orientation.
Students should be able to: communicate effectively in writing and orally; think critically; demonstrate high technical knowledge in the essential areas of information technology and management, relevant to management information systems; demonstrate specific skills for diagnostic, synthesis and research work.
These learning objectives are operationalised through the specific objectives of each course unit, duly specified in the respective FUC, and with a direct correspondence to at least one of the course's learning objectives.
The degree of fulfilment is measured in each course unit, in the respective FUC, which contains the assessment methodologies used for each specific objective.
Thesis / Final work
The program intends for students to develop and demonstrate abilities of independent work, planning, investigating, systematizing, developing, writing (in the form of a dissertation) and presenting their introductory work of scientific activity around a well-defined topic. The work will be guided by a supervisor or recognized specialist that, among other things, will help the candidates to choose the most appropriate research methodology. If the theme chosen is multidisciplinary, it is possible to have a co-supervisor in order to adequately cover the scientific areas involved. This curricular unit ends with the defense of the dissertation made before a jury appointed for this purpose in a public examination.
Iscte strongly encourages dissertations to be written in English, not only because this promotes their international dissemination by making them available in the institutional repository, but also because it facilitates the production of scientific articles based on the results of the research described in the dissertation. The acceptance for publication of such an article in an international event or journal with scientific peer review, prior to the defence, is a factor that will positively influence the final grade obtained in this UC. Please note that the dissertation grade counts for almost half of the final master's degree average, as this average is calculated on the basis of the credit-weighted grades of all the UCs taken within the scope of the MIG.
Although the "dissertation" option is favoured, as it provides a good introduction to the world of research, which is the main forge of knowledge throughout the world, the master's student may opt to carry out a "project work" of an innovative nature, totalling the same credits as a dissertation, whose preparation must be anchored in a well-identified development methodology and whose report, which will also be subject to a public defence before a jury, must meet the same quality requirements as a dissertation. Project work will typically be of a more applied nature than a dissertation and may be carried out within the scope of a research centre linked to ISTA, under the supervision of one of its researchers, or conducted in a company, typically under the supervision of a professor from DCTI or IBS and co-supervised by a specialist of recognised merit from that same company.