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Data Analytics for Business and Economics

Informacje ogólne

Kod przedmiotu: 2400-SU2TS57 Kod Erasmus / ISCED: 14.3 / (0311) Ekonomia
Nazwa przedmiotu: Data Analytics for Business and Economics
Jednostka: Wydział Nauk Ekonomicznych
Grupy: Seminaria magisterskie dla II roku programów anglojęzycznych
Punkty ECTS i inne: 3.00
zobacz reguły punktacji
Język prowadzenia: polski
Rodzaj przedmiotu:

seminaria magisterskie

Skrócony opis:

This seminar is to support the MA thesis in the data analytics, used in a practice and a theory of business and economics.

There are mainly three thematic areas on focus: 1) unsupervised learning methods (k-means, PAM, CLARA, PCA, MDS, association rules etc.), 2) spatial analysis (for geo-located data), 3) Monte Carlo simulation models and bootstrapping. These methods can be addressed in an empirical and as well as theoretical approach. In three semesters time span students are to review the literature, develop own study and complete the thesis. Seminar is conducted as a set of individual regular consultations.

Pełny opis:

This seminar is to support the MA thesis in the data analytics, used in a practice and a theory of business and economics.

There are mainly three thematic areas on focus: 1) unsupervised learning methods (k-means, PAM, CLARA, PCA, MDS, association rules etc.), 2) spatial analysis (for geo-located data), 3) Monte Carlo simulation models and bootstrapping. These methods can be addressed in an empirical and as well as theoretical approach.

In three semesters time span students are to review the literature, develop own study and complete the thesis. Seminar is conducted as a set of individual regular consultations. Programming is in R.

A goal of this seminar is to develop, validate and revise the quantitative methodology and models. Both applied and theoretical works will be supported. Students will refer mainly to current journal literature of the topic.

Potential types of thesis:

- Comparison of the methods on theoretical and /or empirical data to test similarity and sensitivity of methods, as well as its content capacity

- Development of the existing studies by refreshing the results on another datasets and by complementing the conclusions on the results and literature/methodology.

- Theoretical features of methods for different datasets, distributions, applications etc.

- Case studies for business applications

The very desired outcome of the works is a publishable paper.

Literatura:

Selected by tutor for the topic.

Efekty uczenia się:

Students can build the quantitative models, analyse the data and draw the conclusions from the conducted research.

Students have a knowledge in R programming and advanced methods of data analysis.

Students can design and develop a project by themselves, are dedicated to work and independent on their research path.

KW01, KW02, KW03, KU01, KU02, KU03, KK01, KK02, KK03

Metody i kryteria oceniania:

After first (out of 3) semester students have an outline of the thesis prepared, data is collected and hypothesis is prepared.

After second semester (out of 3) literature overview is completed and majority of modelling work done.

After third semester (out of 3) thesis is ready for the defense.

Zajęcia w cyklu "Semestr zimowy 2019/20" (zakończony)

Okres: 2019-10-01 - 2020-01-27
Wybrany podział planu:


powiększ
zobacz plan zajęć
Typ zajęć: Seminarium magisterskie, 30 godzin więcej informacji
Koordynatorzy: Katarzyna Kopczewska
Prowadzący grup: Katarzyna Kopczewska
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie
Seminarium magisterskie - Zaliczenie

Zajęcia w cyklu "Semestr letni 2019/20" (w trakcie)

Okres: 2020-02-17 - 2020-08-02
Wybrany podział planu:


powiększ
zobacz plan zajęć
Typ zajęć: Seminarium magisterskie, 30 godzin więcej informacji
Koordynatorzy: Katarzyna Kopczewska
Prowadzący grup: Katarzyna Kopczewska
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Zaliczenie
Seminarium magisterskie - Zaliczenie
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Warszawski.