Data analysis in R WM-I-S2-E3-ADwR

The aim of the course is to present basic techniques of data analysis including statistical methods. During the course, the student obtains theoretical and practical knowledge regarding the construction, selection and application of an appropriate model in relation to the analysed empirical phenomenon.

(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 2023/24_Z:

(in Polish) E-Learning (pełny kurs)

Term 2024/25_Z:

(in Polish) E-Learning

Term 2022/23_Z:

(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

Term 2022/23_L:

Lecture - estimated student workload: - class participation: 30h - preparation for classes: 5h - consultations with the instructor: 3h - exam preparation: 15h - independent reading: 20h - exam: 2h i.e. 75h equivalent to 3 ECTS. Laboratory - estimated student workload: - class participation: 30h - preparation for classes: 20h - consultations with the instructor: 4h - independent reading: 15h - preparation for credit: 5h - completion of classes: 1h i.e. 75h equivalent to 3 ECTS.

Term 2023/24_Z:

Term 2024/25_Z:

Term 2022/23_Z:

Subject level

intermediate

Learning outcome code/codes

Lecture: I2_W10, I2_U09 Laboratory: I2_W10, I2_U09

Type of subject

Term 2023/24_Z:

optional with unlimited choices

Term 2024/25_Z:

obligatory

Preliminary Requirements

1. Fundamentals of mathematical and descriptive statistics. 2. Knowledge of R package or C++ language.

Course coordinators

Term 2022/23_L:

Piotr Śliwka

Term 2023/24_Z:

Dorota Dąbrowska
Piotr Śliwka

Term 2024/25_Z:

Piotr Śliwka

Term 2022/23_Z:

Piotr Śliwka

Learning outcomes

LECTURE:
W1 - the student knows the theoretical and practical aspects of data analysis (I2_W10).
U1 - the student is able to select an appropriate modelling approach based on theoretical assumptions (I2_U09).

LABORATORY:
W2 - knows the methods of data analysis,
U2 - for selected problems the student is able to build a model and apply it using or modifying advanced R environment packages (I2_U09),
U3 - for selected issues the student is able to build his own package profiled to the issue (I2_U09).

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-E3-ADwR in USOSweb