Urban Data Analytics

Urban Data Analytics 1

Course introduction

This course will teach students how to harness the power of quantitative urban data by mastering the way they are prepared, visualised and analysed. The course begins with introducing students to quantitative data analysis (compared to qualitative data analysis), and continues with lectures on descriptive statistics and data visualisation. The focus is, besides understanding, on working with real data and practicing how to conduct data analyses, which students learn in workshops and with exercises. Students will also learn how to present descriptive statistics and data visualisation in academic studies.

Course objectives

The aim of this course is to equip participants with a basic tool set that will capacitate them to work with data and interpret results for testing and refining theory as well as supporting evidence-based policy making.

More specifically, after following UDA students should have achieved the following learning objectives:

• Understand what quantitative data analysis entails and how it differs from qualitative data analysis.
• Identify situations in which quantitative data analysis provides useful information in an urban context.
• Be able to use and explain the main tools of descriptive data analysis and visualisation and apply these in practice using statistical software.
• Comprehend the power of random sampling and how to use that information for hypothesis testing.
• Understand the basics of regression analysis using the ordinary-least-squares method and apply the knowledge in practice using statistical software.
• Understand how quantitative data analysis output is interpreted, find meaning in the data, and learn how findings can be translated into policy recommendations and/or reflection on theory.

Course content

The course introduces participants to basic data analytics and entails the following seven sessions:

1. Introduction to quantitative data analysis
2. Descriptive statistics (measures of central tendency, measures of spread, correlation)
3. Data visualisation
4. Probability and estimation
5. Hypothesis testing
6. Simple regression analysis
7. Multiple regression analysis

The statistical software that will be used in this course is STATA 16.

Course information

Urban Data Analytics 2

Course introduction

For the first time in history, more than half of the world’s population lives in urban areas. This does not only mean that a majority of people worldwide live in cities, but also that cities are increasingly becoming larger and more complex. In this context, collecting and making sense of in-depth qualitative data on complex urban issues is crucial for urban managers to understand and respond to how urban complexity is constructed, maintained, experienced and contested.

Course objectives

The objective of the course is to help students gain methodological skills to design and conduct qualitative research.  At the end of the course, the students will be able to

At the end of the course, the students will be able to 

• Understand theoretical and practical aspects of conducting qualitative urban research.
• Apply methodological skills on building qualitative research design, data collection and analysis of qualitative data.
• Critically reflect on qualitative research findings.
• Apply ethical concerns, as well as concerns for validity and reliability in qualitative research.

Course content

Urban Data Analytics (UDA) Qualitative course introduces urban qualitative research and focuses on qualitative data collection and analysis. Covering both theoretical and practical dimensions of conducting qualitative research, it helps the students to gain methodological skills to design and conduct qualitative research in urban settings. The course is structured around the following themes:

1. Introduction to Qualitative Urban Research (Qualitative vs. Quantitative Research, Sampling);
2. Qualitative Data Collection Tools (Interviews, Focus Groups, Observations, Online and Offline Qualitative Data Collection);
3. Qualitative Data Analysis (Types, Data Preparation and Coding, Presentation of Findings, Using Atlas TI).

Together with UDA Quantitative and Research Design courses, this course provides students a practical guide for the design and implementation of their thesis research. 

Course information