The seminar provides basic knowledge on a broad spectrum of empirical research methods, both quantitative-statistical and qualitative-hermeneutical. Scientific theoretical backgrounds are also discussed on the basis of relevant texts. The learning objective is the ability to choose appropriate methods in practice and to methodically and systematically obtain, analyse and present data on urban and regional development.

In the practical examination of problems of urban and regional development, a broad spectrum of empirical methods is used to obtain, analyse and present a wide variety of data. In the sense of a preparatory degree for a career, the module provides an overview of different approaches to empirical urban and regional research and their scientific theoretical foundations. Both quantitative-statistical and qualitative-hermeneutic methods are dealt with in the module. Emphasis is placed on the reflection of scientific approaches and the practical work of obtaining and analysing data.

The module consists of a weekly two-hour seminar (2 courses to choose from) and a one-hour exercise with a focus on SPSS, which takes place on four dates in the semester for two hours each (3 courses to choose from). Students must provide themselves with access to use the virtual PC pool of the Department of Geography in advance. Instructions: https://www.geographie.ruhr-uni-bochum.de/studium/virtueller-pc-pool/

Will be provided or announced in the course

The seminar provides basic knowledge on a broad spectrum of empirical research methods, both quantitative-statistical and qualitative-hermeneutical. Scientific theoretical backgrounds are also discussed on the basis of relevant texts. The learning objective is the ability to choose appropriate methods in practice and to methodically and systematically obtain, analyse and present data on urban and regional development.

In the practical examination of problems of urban and regional development, a broad spectrum of empirical methods is used to obtain, analyse and present a wide variety of data. In the sense of a preparatory degree for a career, the module provides an overview of different approaches to empirical urban and regional research and their scientific theoretical foundations. Both quantitative-statistical and qualitative-hermeneutic methods are dealt with in the module. Emphasis is placed on the reflection of scientific approaches and the practical work of obtaining and analysing data.

The module consists of a weekly two-hour seminar (3 courses to choose from) and a one-hour exercise with a focus on SPSS, which takes place on four dates in the semester for two hours each (3 courses to choose from). Students must provide themselves with access to use the virtual PC pool of the Department of Geography in advance. Instructions: https://www.geographie.ruhr-uni-bochum.de/studium/virtueller-pc-pool/

Will be provided or announced in the course

• Basic knowledge about fundamental methods in sciences
• Acquire programming skills in core Python 3.
• Identify and manipulate Python objects.
• Writing effective python scripts that are consistent and easy-to-read.
• Open, read, process, analyze and write data using Python and Pandas.
• Designing clear and effective data visualizations using Python.

This course consists of two parts. The first introduces the students to the fundamentals of the Python language and the second to data analysis and visualization with Python.

First Part:

• Introduction to the fundamentals of the Python language (built-in datatypes and functions, import statements, python environments etc.)
• Flow control using conditional statements and loops.
• Definition of functions in Python.
• Working with datetimes.
• Opening, reading, and writing files using python.

Second Part:

• Analysis of different research datasets from physical geography
• Introduction to the Pandas Data Analysis Library.
• Working with arrays using the NumPy Module.
• Visualizing data using Matplotlib.

The course consists of 12 lectures, 8 of which are dedicated to introducing the fundamentals of Python and 4 to data analysis and visualization. Each lecture includes a set of compulsory assignments. To complete this course the students, must prepare a final project that includes several programming tasks.

1.The Python Tutorial: https://docs.python.org/3/tutorial/
2.Python documentation: https://docs.python.org/3.7/
3.The Coder's Apprentice: Learning programming with Python 3 (Free Ebook): http://www.spronck.net/pythonbook/pythonbook.pdf