Course detail

Introduction to Data Processing

FSI-SZD-AAcad. year: 2025/2026

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.

Language of instruction

English

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

doc. Mgr. Zuzana Hübnerová, Ph.D.

Department

Institute of Mathematics (ÚM)

Entry knowledge

Foundations of programming.

Foundations of descriptive statistics, probability theory and mathematical statistics.

Rules for evaluation and completion of the course

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.

Aims

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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Larsen, R., Marx, M., Introduction to Mathematical Statistics and Its Applications, 6nd ed., 2017. ISBN: 978-01341142178
Sharpe, NR, Veaux, RDD, Velleman.  PF. Business statistics. Pearson, 2017. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme N-LAN-A Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Mgr. Zuzana Hübnerová, Ph.D.

Syllabus

  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

26 hod., compulsory

Teacher / Lecturer

doc. Mgr. Zuzana Hübnerová, Ph.D.

Syllabus

  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