Quantitative data analysis

General data

Course ID:	1900-5-ADI
Erasmus code / ISCED:	(unknown) / (unknown)
Course title:	Quantitative data analysis
Name in Polish:	Analiza danych ilościowych
Organizational unit:	Faculty of Geography and Regional Studies
Course groups:	(in Polish) Przedmioty obowiązkowe, dzienne studia I stopnia (kierunek Gospodarka przestrzenna) (in Polish) Przedmioty obowiązkowe, dzienne studia I stopnia (kierunek Gospodarka przestrzenna) - sem. 1
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Main fields of studies for MISMaP:	spatial development
Type of course:	obligatory courses
Prerequisites (description):	(in Polish) -
Mode:	Classroom
Short description:	The aim of the course is to introduce the basic methods of collecting, processing and analyzing quantitative data in social and economic research.
Full description:	The aim of the course is to familiarize students with the basic methods of collecting, processing and analyzing quantitative data in social and economic research. During the course, students will learn the basics of social science methodology. They will learn about quantitative research methods and popular data sources used in local government studies. The course will introduce basic descriptive statistics (measuring central tendency and dispersion), properties of the variable distributions, covariance and correlation, and the principles of index construction. Students also will learn the basics of statistical inference (estimation of the mean and standard error of the mean), preparing them for the application of more advanced quantitative methods. Exercises held weekly in the computer lab supplement the content of the lecture and serve to develop basic skills in working with quantitative data. Students will learn useful functions of the Excel/Google spreadsheet. They will learn to construct simple databases, process data according to the research questions posed, calculate basic statistical parameters, and correctly visualize and interpret the results of their analyses.
Learning outcomes:	(in Polish) Efekty uczenia się: K_W02, K_W08, K_W10 / K_U05 Po zaliczeniu zajęć: Student zna i rozumie (K_W02, K_W08, K_W10): - najważniejsze źródła danych publicznych i ich rolę w analizie zagadnień związanych z gospodarkę przestrzenną. - podstawowe pojęcia z zakresu statystyki opisowej (m.in. rozkład wartości zmiennej, rozkład warunkowy, wybrane parametry pozycyjne i rozproszenia) oraz wnioskowania statystycznego. - podstawy zastosowania programu MS Excel do przetwarzania i analizy danych, ze szczególnym uwzględnieniem danych mogących służyć diagnozie prawidłowości i zjawisk występujących w rozwoju lokalnym i regionalnym. - sposoby wizualizacji danych w postaci wykresów oraz dobre praktyki w tym zakresie Student potrafi (K_U05): - przygotowywać wieloźródłowe zbiory danych przydatne w rozwiązywaniu praktycznych problemów planistycznych i w opracowywaniu analiz społeczno-gospodarczych - prawidłowo przetwarzać dane ilościowe wykorzystywane w badaniach z zakresu gospodarki przestrzennej (np. dane urzędowe, dane sondażowe). - wykonywać poprawny opis statystyczny i wizualizację danych. - wykorzystać teorie i pojęcia z zakresu gospodarki przestrzennej i geografii społeczno-ekonomicznej do interpretacji analizowanych danych
Assessment methods and assessment criteria:	Methods: lecture, individual and group practical tasks, group project Regular attendance is required to pass the exercises. It is permissible to have 1 absence per semester, regardless of the reason. In the case of subsequent absences, the class participant will be given an additional assignment to complete. Attendance at lectures is not mandatory. Final grade will be based upon a colloquium (test + test of practical skills) and graded homework performed during the semester. The colloquium accounts for 70% of the exercise grade, the homework score accounts for 30% of the exercise grade. The final exam of the lecture is in written form (test). The final course grade will be a weighted average of the exercise (60%) and lecture (40%) grades. For both the colloquium and the exam, the following grading scale will be used: 100% - 90% very good 90% - 80% good+ 80% - 70% good 70% - 60% sufficient+ 60% - 50% sufficient < 50% unsatisfactory
Practical placement:	(in Polish) -

This course is not currently offered.

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