Master in City & Technology 2019/21 – Term I
Seminar Name: Big Data Analytics
Total Hours: 20 hours
Faculty: Diego Pajarito

Syllabus

Big data sets and high-performance computing are turning into commodities for the digital world and so, for lots of disciplines including advanced architecture. Big data analytics remains as a field in which many disciplines converge. From mathematics and statistics to geography and computer sciences, all contribute with tools and methods theoretically applicable to any other field. However, turning problems, data and computer programs into data analytics is closer to a tailor-made solution rather than a one-fits-all recipe.
This course provides students with a general structure to assemble big data analytics and prepare datasets for multiple analytics techniques but especially those related to artificial intelligence. The course aims to encourage master students to gather data for their individual research and later, assemble a pile of standardised descriptive statistics and analysis techniques to gain insights from these data sets. Students will be guided across the three main phases of big data analytics and later they will replicate them for the individual research.
During the course sessions, students will perform descriptive and analytic methods to gain insights from multiple datasets. Through discussion with peers and the tutor, students will define the most convenient path to collect and analyse big data. Each session will demand individual efforts to translate generic tools into tailored solutions for students’ individual projects. This structure aims to emulate real case scenarios faced by professionals developing tech-based projects for cities.
Students will share the tools created for the course through a GitHub repository with a compilation of source code and graphic resources generated. Students will also prepare an academic poster in which they summarise both the workflow and main insights gained from the course. The course aims to support students during the initial phases of their thesis research while setting the ground for the coming actions within the Master in City and Technology.

Faculty

Diego Pajarito got his PhD in Geoinformatics as part of a Marie Curie ITN Action – Joint doctorate between the Universities of Münster, Universitat Jaume I and Universidade Nova de Lisboa (2018), and the MSc in Information and Communication Sciences from Universidad Distrital de Bogotá (2014). He has performed research for data analytics and spatio-temporal analysis of sustainable development, smart cities and urban systems. Also, Diego has developed data collection techniques through mobile devices and crowdsourced data collection. Diego’s interests are the simplification of data collection and analysis for non-expert audiences when it comes to the analysis of spatial distributions. He has been a lecturer of courses on spatial analysis, big data and spatial databases in Spain, IAAC (2019-2020), Colombia, Universidad Distrital (2010-2015) and Universidad Autonoma de Bucaramanga (2014). He has also been a consultant for geospatial analysis and high-performance computing for different agencies in Colombia such as the Ministry of Agriculture (2010-2015), Institute of Environmental, meteorological affairs (2010, 2012, 2014), Ministry of Justice (2014), Geographical Institute (2007-2010), among others. Diego is a postdoctoral research fellow at the Institute for Global Sustainable Development at the University of Warwick and seminar faculty of IAAC’s MaCT program.