Introduction to Data Processing (FSI-SZD-A)

Academic year 2025/2026
Supervisor: doc. Mgr. Zuzana Hübnerová, Ph.D.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: English
Aims of the course unit:

Introduction to concepts and tools for data manipulation. The following main topics will be taught and implemented

  • databases (quering, indexing,..)
  • visualization
  • basic statistics
  • regression analysis and machine learning
  • geographical data
Learning outcomes and competences:
 
Prerequisites:

Foundations of programming.

Foundations of descriptive statistics, probability theory and mathematical statistics.
Course contents:

The course is focused on basic data handling: introduction to databases and its effective design for data manipulation; elementary concepts from statistics - linear regression, machine learning; and visualization, geographical data included. The course is oriented on practical aspects, all main concepts are implmented in programming language python.
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

Students will have to finish two minor projects during the semestr to proceed to the final examination. First is focused on databases, the second one on data presentation (interactive dashboard). The final project should involve more advanced concepts from data analysis. Students will work independently on a topic, which will be discussed (and approved) with the teacher in advance. The final exam and evaluation is based on the individual discussion of that project, which can receive 0 - 100 points.

Evaluation by points: excellent (90 - 100 points), very good (80 - 89 points), good (70 - 79 points), satisfactory (60 - 69 points), sufficient (50 - 59 points), failed (0 - 49 points).

Participation in the exercises is compulsory. During the semester two abstentions are tolerated. Replacement of missed lessons (if there are more of them) is dealt with individually.
Controlled participation in lessons:
 
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture

  1. Introduction to databases

  2. Basic queries and simple commands

  3. Larger instances and database indexing (computational aspects vs. database size)

  4. Project 1: Own Database Project

  5. Descriptive statistics and basic statistical methods

  6. Visualization: introduction to various libraries, different types of graphs

  7. Advanced visualizations and dashboards

  8. GIS + Python: map data and visualizations

  9. Analyses on maps

  10. Project 2: Own Dashboard

  11. Linear regression and logistic regression: basic econometrics

  12. Linear regression II; machine learning: neural networks

  13. Machine learning: boosted trees
    Computer-assisted exercise

  1. Installation of python, sqlite, simple example

  2. Basic queries and simple commands

  3. Larger instances and database indexing (computational aspects vs. database size)

  4. Project 1: Own Database Project

  5. Descriptive statistics and basic statistical methods

  6. Visualization: introduction to various libraries, different types of graphs

  7. Advanced visualizations and dashboards

  8. GIS + Python: map data and visualizations

  9. Analyses on maps

  10. Project 2: Own Dashboard

  11. Linear regression and logistic regression: basic econometrics

  12. Linear regression II; machine learning: neural networks

  13. Machine learning: boosted trees
Literature - fundamental:
1.

Sharpe, NR, Veaux, RDD, Velleman.  PF. Business statistics. Pearson, 2017.
4.

Larsen, R., Marx, M., Introduction to Mathematical Statistics and Its Applications, 6nd ed., 2017. ISBN: 978-01341142178
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-LAN-A full-time study --- no specialisation -- Cr,Ex 6 Compulsory 2 1 W