Details - Tematika

Data Analysis (GAINBAN-ADATELEM-1)

Basic data

Name and type of the study programme

Computer Science Engineering, undergraduate program

Curriculum

2026

Classes / consultation hours

2 + 0 + 2 (L+S+Labs)

Credits

5 credits

Theory – Practice

Theory: 50%, Practice: 50%

Recommended semester

Semester 5

Study mode

full-time

Prerequisites

Evaluation type

Mid-term evaluation

Course category

Compulsory elective in the specialization

Language

English

Instructors

Responsible instructor

Dr. Pap-Szigeti Róbert

Responsible department

Department of Information Technologies

Instructor(s)

Dr. Pap-Szigeti Róbert

Checked by

Kovács Márk

Course objectives

Mastering statistical concepts, preparing and interpreting descriptive statistics. Understanding the theoretical background of hypothesis testing, conducting and interpreting hypothesis tests. Conducting multivariate analyses and interpreting the results. Performing data visualization.

Course content

Lectures

Methods of data preparation and data cleaning, handling missing data, and detecting outliers. Calculation and graphical representation of descriptive statistical parameters in a Python environment (Pandas, Matplotlib, Seaborn) and using integrated statistical software (Jamovi, SPSS). Conducting and interpreting statistical hypothesis tests. Correlation analysis and calculation of the correlation coefficient. Application of multivariate regression analysis and interpretation of its results. Examination of the structure of a set of variables using factor and cluster analysis. Data visualization techniques and their practical application.

Labs

Methods of data preparation and data cleaning, handling missing data, and detecting outliers. Calculation and graphical representation of descriptive statistical parameters in a Python environment (Pandas, Matplotlib, Seaborn) and using integrated statistical software (Jamovi). Conducting and interpreting statistical hypothesis tests. Correlation analysis and calculation of the correlation coefficient. Application of multivariate regression analysis and interpretation of its results. Examination of the structure of a set of variables using factor and cluster analysis. Data visualization techniques and their practical application.

Acquired competences

Knowledge

The graduate: - possesses the English language proficiency required to understand professional literature, communicate in a professional context, and support continuous self-development - has knowledge of the fundamental principles and methods of natural sciences necessary for the field (e.g. mathematics, physics) - has knowledge covering: database systems

Skills

The graduate is able to: - analyze problems and apply engineering methods to solve them - perform programming and software development tasks using modern tools and technologies - work both independently and as part of a team - prepare technical documentation and interpret system specifications - adapt to new technologies and apply them effectively

Attitude

- strives for quality, precision, and reliability in professional work - is committed to continuous professional development and lifelong learning

Autonomy and responsibilities

- is capable of independent professional work within their field - complies with professional, legal, and ethical standards

Additional professional competences

- is able to formulate and test hypotheses while taking data analysis criteria into account - is able to interpret the results of studies

Requirements, evaluation and grading

Mid-term study requirements

- At the beginning of each of the 3rd through 7th practice sessions, students must complete a short theoretical assignment based on the lecture material, worth 2 points per session for a total of 10 points. These assignments cannot be made up. - During the 8th practice week, students must complete a practical exam worth 50 points by applying the analytical methods covered in class. The use of GMI or other aids is not permitted during the test. - In weeks 11–12, students must present their own analysis via a presentation and a document outlining the analyses and results, worth 40 points. During weeks 9–10, the instructor will assist students in conducting the analysis through office hours. The documented use of GMI tools is permitted for selecting analytical methods (the documentation must include the prompts used and the GMI’s responses). The use of GMI tools is not permitted during the interpretation and presentation of the analyses. During the presentation, the instructor will ask questions regarding the analyses and their interpretation. Requirements for earning the practical grade: - earning at least 5 points on the theoretical questions, and - earning at least 25 points on the practical exam, and - earning at least 20 points on the analysis and presentation The practical exam may be retaken in Week 10. The presentation of the analysis may be rescheduled for Week 13. Upon fulfillment of these requirements, the total points earned will be converted into a grade in accordance with the current Academic Regulations.

Generative AI usage

Use of GAI tools is permitted in a limited manner (e.g., for literature search support or specific tools). In this case, the course instructor is responsible for defining where and how GAI tools may be used in assignments. The course description must specify in detail how GAI tools may be used during the course.

Study aids, laboratory background

The theoretical course material is available to students in electronic form. The software required for the lab exercises is available on the university’s computer network; students can access the software free of charge for practice at home. We provide students with the lab materials and solutions to the assigned exercises in electronic form.

Readings

Compulsory readings

[1] McKinney, W. (2022): Python for Data Analysis (3rd ed.) Data Wrangling with pandas, NumPy, and Jupyter. O'Reilly, Beijing, Boston, Farnham, Sebastopol, Tokyo. ISBN 978-1-098-10403-0 https://www.ebooks.com/en-hu/book/210644288/python-for-data-analysis/wes-mckinney [2] Downey, A. B. (2024): Think Stats (3rd ed.) Exploratory Data Analysis. O'Reilly, Beijing, Boston, Farnham, Sebastopol, Tokyo. ISBN 978-1-098-10403-0 https://allendowney.github.io/ThinkStats/ [3] Lecture slides in the Teams group; digital course materials on Moodle: https://e-learning.nje.hu/