Space Data Analysis Course


  1. Introduction (Slides, 15.2.2021), AMON (Slides)
  2. Basic data science packages (Slides, Recording, 22.2.2021)
  3. Copernicus programme I. (Slides 1, Recording, 1.3.2021)
  4. Copernicus programme II. (Slides 2, Recording, 8.3.2021)
  5. Copernicus programme III. (Slides 3, Recording, 15.3.2021)
  6. Copernicus programme – Lab exercises, discussion (Recording, 22.3.2021)
  7. Copernicus programme – Lab exercises, discussion (Recording, 29.3.2021)
  8. Holiday (5.4.2021)
  9. Astrometry – Gaia (Slides, Recording, 12.4.2021)
  10. Astrometry – Gaia (Slides, Recording, 19.4.2021)
  11. Astronomy – Herschel, Hubble space telescope, TESS (Slides, Recording, 26.4.2021)
  12. Cosmic ray physics (Slides, Recording, 3.5.2021)
  13. Heliophysics (Slides), presentation of student projects and time allocated for catching-up in case of missed or problematic topics


  1. Introduction, AMON (16.2.2021)
  2. Basic data science packages (Slides, Recording, 23.2.2021)
  3. Copernicus programme I. (Recording, 2.3.2021)
  4. Copernicus programme II. (Recording, 9.3.2021)
  5. Copernicus programme III. (Recording, 16.3.2021)
  6. Copernicus programme – Lab exercises, discussion (Recording, 23.3.2021)
  7. Copernicus programme – Lab exercises, discussion (Recording, 30.3.2021)
  8. Astrometry – Gaia (Slides, Recording, 6.4.2021)
  9. Astrometry – Gaia (Slides, Recording, 13.4.2021)
  10. Astronomy (Slides, Recording, 20.4.2021)
  11. Astronomy – Herschel, Hubble space telescope, TESS (Slides, Recording, 27.4.2021)
  12. Cosmic ray physics (Slides), Heliophysics (Slides, Recording, 4.5.2021)
  13. Presentation of student projects, consultations related to semester projects, time allocated for catching up in case of missed or problematic topics.

Lectures skipped due to time constraints

If there are time allotment issues, one or two topics above might be dropped.
However, the plan is at least to show few examples related to the dropped topics during the lectures.

Course schedule

  • Lectures: 7:30-9:00 AM on Monday
  • Labs: 7:30-9:00 AM on Tuesday
  • Students can also receive individual consultations after an agreement with the lecturer.


Course completion requirements

Overall assessment consists of “Continuous assessment” (40%) and “Final assessment” (60%).

  • “Continuous assessment” is based on student activity during the labs (20%) and the semester project (20%).
    • Weekly/occasional submissions (push) of lab work to the faculty’s Git repository. Completed lab work will be graded by points (20 divided by a number of labs with prepared activity). 
    • The semester project will be graded based on weekly activity and the final result of the project. Students have 10 weeks to finish their projects, 1 point is given for each weekly report about the work, and the remaining 10 points grade the final result.
      April 20th update: students will have two additional weeks (week 14, 15) to finish their projects.
  • “Final assessment” consists of an exam in which students will be required to practically process a dataset, which is a similar activity as done during the labs. The assessment will also include an oral examination of a student about topics covered in the course.

Deadlines for LAB activity submission

March 5th, 2021AMON3✔️
April 4th, 2021Copernicus programme and Earth Observation 6✔️
April 27th, 2021Astrometry – Gaia5✔️
May 5th, 2021Astronomy3✔️
May 16th, 2021Geomagnetosphere – SWARM3*
May 16th, 2021Heliophysics3*
May 16th, 2021Cosmic ray physics (recommended)3*
Deadlines for LAB activity submission

The total number of points is 20. The points are divided by the complexity of the tasks. This might change slightly, by point up or down.

*: At least one of the optional topics is mandatory. Students can choose the topic they prefer.

Activities that do not require writing a source code should be documented by committing a few screenshots inside the git repository. The screenshots should demonstrate that the student tried to work on the given activity.

Semester projects

General organizational notes:

  • There is a private Gitlab group for each project. Project members should have access to it.
  • Students should create at least one Gitlab project (git repository) inside the group mentioned above. It should be the main git repository of the project.
  • The main repository of the project should contain file. The changelog should report on changes to the code but also any activity related to the project. There should be at least one report per week. Read about changelogs.
  • If students are working on a review of the state of art as a part of the project activities, the review can be in any format. It does not need to be saved in the repository, but then there needs to be provided URL address of this review in the changelog and maybe also in
  • Each team should prepare a presentation of their project – something to say, show, a few supporting slides. This will be presented during the last week of the course.

Besides these topics, four students working on a diploma theses related to the analysis of space data:

  • Mapping of cloud and meteorological measurements into observations of Mini-EUSO detector
  • Development of autonomous operation of a full-sky airglow camera
  • Detection of meteors in measurements of Mini-EUSO experiment using machine learning methods
  • Processing of data from EUSO experiments using neural networks

Each semester topic has associated a webpage, which provides additional information, and it will be updated during the semester as students raise questions or some other issues arise. 

Each project is assigned a Mattermost “channel,” which will serve as a forum for discussions and consultations regarding the semester project.

Course objectives

The course objectives are to make the students familiar with the basics of accessing and processing data from space missions. The course familizes students with selected missions and access to their data.  These are selected to represent a variety of data produced by scientific and applicational space missions. The students will gain practical experience with data management, visualization, and processing. The topics covered in the course should prepare students for further work in ESA projects, for instance as a trainee in the Young Graduate Trainee programme.

Lectures are focused on the presentation of various space missions and approaches for data analysis. Labs are focused on practically applying the topics discussed in the lectures. One of the important tasks realized by students within the course is work on a semester project assigned individually to each student or a group of students. The projects are focused on expanding the topics examined in the lectures and exercises. 

Lecture topics are selected to cover a variety of mission types explored by ESA. Explored fields focus on astronomy,  geophysics, remote sensing, planetary science. The labs follow lecture topics with practical examples of data analysis.

The lecture plan includes few lectures dedicated to giving an opportunity to finish lessons and discussions started during the previous weeks, to discuss semester projects, and, if necessary, topics that students find challenging.

Topics covered in the course


AMON-net is a network of AMON detectors, which are one pixel detectors of the UV background of the atmosphere. The project is managed and developed by the Institute of Experimental Physics of Slovak Academy of Sciences. The project is also supported by two ESA PECS projects. The aim of the lesson and seminar is to familiarize students with popular libraries for data analysis.  The aim is to discuss topics related to data filtration, joining of datasets, visualization, and machine learning (prediction of background variation based on observed data).  The AMON-related topics will also be covered in semester projects.

Astrometry – Gaia

The Gaia is one of the ESA’s large projects, therefore it was considered  as imperative to cover this topic within this course.

The aim of the topic is to familiarize students with Gaia mission, astronomical terms, work with astronomical catalogues, and usage of such data. The attention will be given to usage of the Gaia archive – topics will include TAP, ADQL, Datalink, HEALPix, discussion of the Gaia data model, and visualization of the data. The students will familiarize themselves with astropy, matplotlib, numpy, healpy and other python. Activities will also include work with web-based Gaia Archive, and TOPCAT application. 

Astronomy – Herschel, Hubble Space Telescope

The topic of astronomy is closely related to astrometry, and it is covered as a continuation of the topics examined within the Gaia-related lecture and seminar. Herschel and Hubble Space telescopes will be used to demonstrate concepts related to processing of the astronomical data.

The covered topics include archives of the space telescope data, working with FITS files, HiPS and MOC formats.The students will work with ESASky and Aladin applications.

Students will continue working with the Astropy library. Activities will also include work with the HIPE (Herschel Interactive Processing Environment) application.

Copernicus programme

The Copernicus is the European Union’s flagship Earth Observation and Monitoring programme, in which the ESA is one of the key stakeholders.  

The aim of this topic in the course is to introduce students into the area of Earth monitoring. 

Data access is demonstrated for satellite data and for copernicus services through GUIs and through API. Students should gain experience with handling typical data formats such as NetCDF and GRIB. The activities include programming with popular python libraries such as rasterio, geopandas, cartopy, and also applications in ESA’s SNAP package (Sentinel application platform). The tasks include visualization, classification, and application of models.

Geomagnetic field

The SWARM mission collects data about the Earth’s magnetic field. The geomagnetosphere topic is also investigated by the Institute of Experimental Physics, which makes it more likely to be investigated within diploma students’ theses. The activities include the usage of the magnetospheric models, downloading and visualization of the actual data, and comparison between models and reality.