Course catalogue
Create your own master’s programme by choosing between the different specializations of our partner universities.
Master SERP+ Programme - cohort 2020-2025
Data Science Basics (3 ECTS)
All courses during this semester
All courses during this semester
- Transferable skills: Polish course, Summer School in Entrepreneurship (6 ECTS)
- The molecules of life: from structure to chemical function (3 ECTS)
- Selected in silico and in vitro methods in thermodynamics and soft matter (6 ECTS)
- Organic chemistry (3 ECTS)
- Introduction to solid state (6 ECTS)
- Dynamics of photochemical reactions in chemistry, biology and medicine (6 ECTS)
- Transferable skills: Portuguese course, Summer School in Entrepreneurship (6 ECTS)
- Solid State Physics (6 ECTS)
- Molecular Energetics (3 ECTS)
- Laboratory of Materials and Surface Analysis (6 ECTS)
- Interfacial Electrochemistry (3 ECTS)
- Interfaces, Colloids and Self-Assembly (6 ECTS)
- Transferable skills: Summer School in Entrepreneurship (3 ECTS)
- Organic Photochemistry (3 ECTS)
- Italian Courses (3 ECTS)
- Introduction to Solid State (6 ECTS)
- Inorganic Functional Materials (3 ECTS)
- Electrochemical systems for energy conversion and storage (6 ECTS)
- Chemistry and Technology of Catalysis and Laboratory (6 ECTS)
All courses during this semester
- Nanosciences (6 ECTS)
- Nanoparticles and Advanced radiation therapies (6 ECTS)
- Fundamentals in data science and machine learning (3 ECTS)
- Femtochemistry (3 ECTS)
- Chemistry for renewable energy: from advanced research to industrial applications (6 ECTS)
- Transferable skills: Scientific writing, Polish courses (6 ECTS)
- Lanthanide luminescence: Application in chemistry and biology (6 ECTS)
- Introduction to Data Sciences (3 ECTS)
- Environmental photochemistry (3 ECTS)
- Computational and quantum photochemistry (6 ECTS)
- Applied photochemistry and luminescence spectroscopy (6 ECTS)
- Scientific Writing and Career Objectives (3 ECTS)
- Portuguese course (3 ECTS)
- Nanotechnologies, Micro and Nano-fabrication (6 ECTS)
- Materials Properties and Applications (6 ECTS)
- Electrochemical Technology (6 ECTS)
- Data Science Basics (3 ECTS)
- Bionanotechnology (3 ECTS)
- Transferable skills: Scientific Writing Industrial Seminars (3 ECTS)
- Surface Science and Nanostructuring at Surfaces (6 ECTS)
- Polymers for electronics and energy harvesting (6 ECTS)
- Laboratory on device building (3 ECTS)
- Italian Courses (3 ECTS)
- Data Science and Applications to Chemistry (3 ECTS)
- Composite materials for biomedical applications (6 ECTS)
Content
- The Cross-industry standard process for data mining (CRISP-DM) model.
- Data Collection and Business Understanding.
- Modelling
- Exploratory Data Analysis;
- Predictive Analytics.
- Evaluation methodologies.
Aims
The syllabus contents defined for this course unit are intended to provide the student with knowledge of the essential steps to a Data Science project. The goal is that the student will be able to define the problem, collect data, apply and evaluate some of the main modeling techniques and interpret the obtained results.
Recommended Books
- L. Torgo. Data Mining with R, learning with case studies, second edition, 2017. Chapman and Hall/CRC . ISBN: 9781482234893
- J. Han , M. Kamber and J. Pei. Data Mining - Concepts and Techniques (3rd edition), 2011. ISBN: 9780123814791.
Teaching Staff
Rita Ribeiro (responsible)
Hours
21 h (lectures + theoretical-practical classes)
Grading System
During classes topics will be exposed with the help of practical examples.
Evaluation will be carried out in a distribution form together with a final exam.
This course uses distributed evaluation formed by two (2) theoretical tests during the semester (or alternatively a final exam), and one (1) practical assignment at the end of the semester.
The final grade will be calculated as the weighted average of the practical and theoretical grades using the following formula:
NF = 0.60 * NTh + 0.40 * NPra
where, NTh is the average of the grades in the two tests or the grade in the final exam, and NPra is the grade of the practical assignment.
