(in Polish) Analiza danych w R - zajęcia fakultatywne WM-MA-S2-E1-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) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

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.

Subject level

intermediate

Learning outcome code/codes

Lecture: MA2_W04, MA2_W08 Laboratory: MA2_U19, MA2_K01, MA2_K02

Type of subject

obligatory

Preliminary Requirements

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

Course coordinators

Piotr Śliwka

Learning outcomes

LECTURE:

W1 - Discusses theoretical and practical aspects of data analysis, considering their applications and limitations (MA2_W04),
U1 - Selects and justifies appropriate mathematical models and modeling methods based on analysis of theoretical assumptions (MA2_U16).

LABORATORY:

W2 - Evaluates and classifies data analysis methods in terms of their effectiveness and applications using advanced computational techniques (MA2_W08),
W3 - Demonstrates a thorough knowledge of at least one software package for statistical data processing (MA2_W12),
U2 - Constructs and applies models that solve selected problems in specific advanced mathematical applications (MA2_U16).

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-MA-S2-E1-ADwR in USOSweb