Modern data analysis technologies WM-I-S2-E2-WTAD

The goal of the course is to provide knowledge and skills in basic data analysis and the basics of machine learning. In this class, students will be introduced to basic data analysis methods and machine learning models in R and Python. They will acquire the skills to: load a dataset, identify its flaws and improve them, select an appropriate model and evaluate it. They will also become familiar with basic libraries in R and Python languages for data analysis in science and business.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

information and communication technology

(in Polish) E-Learning

Term 2022/23_L:

(in Polish) E-Learning

Term 2020/21_L:

(in Polish) E-Learning (pełny kurs)

Term 2024/25_L:

(in Polish) E-Learning

Term 2021/22_L:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

Term 2023/24_L:

(in Polish) E-Learning

Term 2019/20_L:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

(in Polish) WYKŁAD Szacunkowy nakład pracy studenta: - uczestnictwo w zajęciach: 30h, - uczestnictwo w egzaminie: 3h, - konsultacje z prowadzącym: 3h, - przygotowanie do zajęć: 5h, - przygotowanie do egzaminu: 35h, razem 76h, co odpowiada 3 ECTS. LABORATORIA: Szacunkowy nakład pracy studenta: - uczestnictwo w zajęciach: 30h, - uczestnictwo w kolokwium: 2h, - konsultacje z prowadzącym: 3h, - przygotowanie do zajęć: 5h, - przygotowanie projektu: 35h, razem 76h, co odpowiada 3 ECTS.

Subject level

intermediate

Learning outcome code/codes

LECTURE I2_W10, I2_U09 LABORATORIES I2_W10, I2_U09

Type of subject

obligatory

Preliminary Requirements

1. knowledge of statistics, statistical tests, integration, theorem proving 2. basics of structured and object-oriented programming

Course coordinators

Term 2022/23_L:

Term 2020/21_L:

Term 2024/25_L:

Term 2021/22_L:

Term 2023/24_L:

Dorota Dąbrowska
Robert Kłopotek

Term 2019/20_L:

Robert Kłopotek

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification:
grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved -with a clear majority of positives -and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WM-I-S2-E2-WTAD in USOSweb