Course catalogue
Create your own master’s programme by choosing between the different specializations of our partner universities.
Master SERP+ Programme - cohort 2020-2025
Introduction to Data Sciences (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
- Transferable skills: Scientific writing, French courses - 5ECTS
- 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)
Hours: (Lecture / Tutorial / Practical courses)
Lectures (10 h)
- Introduction to Data Science, data collection, processing, analysis and archiving
- API (Application Programming Interface) – a powerful data mining tool
- Reproducibility of published experimental results, Open Science and FAIR
- Big Data - brief overview of issues and challenges
Practical laboratory exercises (12 h)
- Hands-on use of databases and experimental data repositories
- Examples of shared data applications in chemistry and structural biology
- Practical use of the programmatic API implemented in various databases and web-servers
- Applications of big data in practice, machine learning in chemistry and structural biology
- Reprocessing and re-use of raw experimental data
The covered topics include the characteristics and operations associated with the creation, gathering, and use of research data. The following issues will be presented:
- How to handle raw data obtained from experiments (e.g. synchrotron X-ray diffraction, NMR spectroscopy, cryo-electron microscopy)?
- Required procedures to get a working dataset, the first stage of interpretation
- Overview of methods for analyzing experimental data
- Storing, retrieving and sharing data
- Reproducibility, validation and re-using data (Open Science and FAIR initiatives)
Students will learn in practice the methods and techniques that are necessary to exploit vast amounts of research data and to extract information from large heterogeneous datasets or use them in machine learning protocols. During hands-on laboratory exercises, students will be introduced to selected real scientific problems. They will prepare dedicated software queries to mine data from repositories and databases, learn how to process and archive experimental data, and how to improve search efficiency and precision.
Pre-requisites:
- Familiarity with the basic math and statistic concepts.
- Curiosity about research and playing with data.
Hours Lectures 12 h Laboratory 9 h
Teaching Staff: Miroslaw Gilski
Hours: 22 hours
Grading system in % (homework, oral presentation, lab training, final exam)
Recommended books & articles
- Introduction to Data Science, Jeffrey S. Saltz and Jeffrey M. Stanton, Sage Publ. (2017).
- An Introduction to Data, Everything You Need to Know About AI, Big Data and Data Science. Francesco Corea, Springer Nature, (2019).
- A public database of macromolecular diffraction experiments, M. Grabowski, K.M. Langner, M. Cymborowski, P.J. Porebski, P. Sroka, H. Zheng, D.R. Cooper, M.D. Zimmerman, M. Elsliger, S.K. Burleyd and W. Minor, Acta Cryst. D72, 1181-1193, (2016).
- Data sharing in structural biology: Advances and challenges, M. Grabowski et al., in Data Sharing: Recent Progress and Remaining Challenges-Computer Science, Technology and Applications (Nova Science Publishers), pp. 29–68, (2019).