Masters Data Science

• What will I learn in this course?

  What will I learn in this course?

  Data Science integrates the multidisciplinary paradigms and methods necessary to address the complex challenges of our hyperconnected information ecosystem. Every day, vast amounts of data are generated from numerous sources such as commercial transactions, social media interactions, medical records, sensor readings, and IoT devices. Through the integrative analytics in Data Science pipelines, we can derive crucial inferences that enable us to extract knowledge from data and support informed decision-making. The Master's in Data Science at Universidade Lusófona de Lisboa (MCiD) is the ideal educational complement for candidates who, after completing a degree in Computer Engineering, Statistics, Physics, Mathematics, Management, Economic and Financial Sciences, or related areas, wish to pursue a graduate degree. This academic path aims to achieve a higher level of specialisation and provide access to more prominent professional positions. The MCiD offers a comprehensive introduction to Data Science, providing solid training both in foundational principles and cutting-edge techniques for data analysis and engineering. Additionally, it addresses the necessary knowledge in privacy, security, and ethics, essential for handling data in compliance with current legislation. Candidates are evaluated on the basis of their curriculum vitae.

• Entry requirements and conditions

  Entry requirements and conditions

  Candidates with a background in Computer Science, Statistics, Physics, Mathematics, Management, Economics and Financial Sciences or related fields may apply for the cycle of studies leading to a master's degree: a) holders of a bachelor's degree or legal equivalent; b) holders of a foreign academic degree awarded following a 1st cycle of studies organised in accordance with the principles of the Bologna process by a State adhering to that process; c) holders of a foreign higher academic degree that is recognised as satisfying the objectives of a bachelor's degree by the statutorily competent body; d) holders of an academic, scientific or professional curriculum that is recognised by the competent statutory body of Centro Universitário Lisboa as attesting to their ability to complete this cycle of studies.

• Seriation Criteria

  Seriation Criteria

  • 100% - Mestrado - Apreciação Curricular

  Candidatos sem classificação da habilitação anterior

  • 100% - Mestrado - Apreciação Curricular

• Profile of the candidates for whom the course is intended

  Profile of the candidates for whom the course is intended

  This graduate programme is intended for candidates with a background in areas such as Computer Science, Statistics, Physics, Mathematics, Management, Economic and Financial Sciences, or related fields, who wish to acquire foundational competencies in Data Science.

• Technical and professional skills that I will acquire

  Technical and professional skills that I will acquire

  Throughout the programme, students acquire a comprehensive set of essential skills and competencies in Data Science. These abilities equip them to tackle current and future challenges in data analysis and management. Technical Skills: 1 - Advanced Data Science programming in languages such as Python and/or R, utilising specific libraries and 2 - tools for data manipulation and analysis. 3 - Statistical analysis and applied mathematics including regression analysis, hypothesis testing, and probabilistic reasoning. 4 - Creation of Machine Learning models to produce explanations and prediction using algorithms trained with extensive dataset. 5 - Database management and data engineering through gaining familiarity with structured (SQL) and semi-structured (JSON, XML) databases, and techniques for handling unstructured data. 6 - Creation of effective visualisations to communicate complex insights clearly and accessibly. Transferable Skills: 1 - Creative problem-solving with innovative approaches to analysing data and deriving relevant inferences within multidisciplinary scenarios. 2 - Effective communication to explain complex technical concepts to non-specialist audiences. 3 - Adaptive learning to rapidly acquire knowledge in new data-generating domains such as healthcare, social networks, industry, and others. 4 - Ethical awareness and ability to contextualise data science legally through a deep understanding of the ethical and legal aspects related to data privacy, security, and usage, ensuring regulatory compliance. 5 - Collaborative work within multidisciplinary teams, integrating diverse perspectives and expertise.

• Study plan and curriculum structure

  Study plan and curriculum structure

  • 1º Ano / Common Core
    • [1º Semestre]
    • Applied Programming for Data Science 7 ects
    • Fundamentals of Statistics for Data Science 7 ects
    • Introduction to Data Science 7 ects
    • Orientation Seminar 2 ects
    • Privacy, Security and Ethics in Data Science 7 ects

    • [2º Semestre]
    • Advanced Data Science 7 ects
    • Information Visualization 7 ects
    • Introduction to Social Networks 7 ects
    • Topics in Data Engineering for Data Science 7 ects
    • Tutoring Seminar 2 ects
  • 2º Ano / Common Core
    • [1º Semestre]
    • Fundamentals of Natural Language Processing 7 ects
    • Project Seminar 2 ects
    • Topics on Machine Learning and its Applications 7 ects
    • Option I
    • Option II

    • [2º Semestre]
    • Dissertation Seminar or Project Work

• Options

  Options

  • Dissertation Seminar or Project Work
    • Dissertation Seminar (Dissertation Seminar or Project Work) 30 ects
    • Project Work (Dissertation Seminar or Project Work) 30 ects
  • Option I
    • Advanced Information Visualization (Option I) 7 ects
    • Fundamentals of Recommendation Systems (Option I) 7 ects
  • Option II
    • Fundamental Methods for Recommendation Systems (Option II) 7 ects
    • Fundamentals of Process Mining (Option II) 7 ects

• Course Team

  Course Team

  Bruno Saraiva

  Bruno Cipriano

  Carlos Fernandes

  Iolanda Velho

  José Paredes

  João Caldeira

  João Moreira

  Lucio Ferreira

  Manuel Pita

  Paulo Guedes

  Thiago Paiva

  Zuil Filho

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• Career opportunities and career prospects

  Career opportunities and career prospects

  Graduates of the Master¿s in Data Science at Universidade Lusófona are uniquely equipped to work in contexts requiring advanced competencies in data engineering, machine learning, model building, and the applied interpretation of complex data phenomena. Thanks to the programme¿s balanced coverage of data engineering, applied statistics, visualisation, machine learning, and ethical responsibility, graduates may assume roles across a wide range of sectors including healthcare, finance, retail, creative industries, telecommunications, public administration, and academic research. Example career paths include: - Data Scientist - Data Engineer - Machine Learning Engineer - Data Analyst - Data Visualisation Specialist - Social Network and Audience Analyst - Health Data Analyst - Financial Data Analyst - Data Ethics and Privacy Consultant - IoT Data Integration Specialist - Public Policy Data Analyst - Scientific Researcher in Data Science or interdisciplinary areas

• Scientific areas of the course

  Scientific areas of the course

  Ciências informáticas

• Observations

  Observations

  Schedule: Daytime, Evening.

Department of Computer Engineering and Information Systems

• DEISI Website