02806 Social data analysis and visualization

2024/2025

Course information
Social data analyse og visualisering
English
5
MSc
Offered as a single course
Elective course (B Eng), IT and Economics
Elective course (B Eng), Computer Engineering
Elective course (B Eng), Software Technology
Programme specific course (MSc), Business Analytics
Programme specific course (MSc), Human-Centered Artificial Intelligence
Programme specific course (MSc), Transport and Logistics
General competence course (MSc), Human-Centered Artificial Intelligence
General competence course (MSc), Business Analytics
Technological specialization course (MSc), see more
Technological specialization course (MSc), Design and Innovation
Technological specialization course (MSc), Human-Centered Artificial Intelligence
Technological specialization course (MSc), Transportation and Logistics
Technological specialization course (MSc), Business Analytics
Spring F3A (Tues 8-12)
Campus Lyngby
Lectures, exercises and final project
13 weeks
Evaluation of exercises/reports
The grading is based on an overall evaluation of exercises (50%) and final project report (50%). Specifically, the grade is based on individualized group-reports.
All Aids - with access to the internet
7 step scale , internal examiner
02822
02822/02467
02101/02100 , The course involves work with high level programming languages (e.g. Python), so practical programming experience is recommended (e.g. in Python/​Java/​JavaScript/​C/​C++)
Sune Lehmann , Ph. (+45) 4525 3904 , sljo@dtu.dk
01 Department of Applied Mathematics and Computer Science
At the Studyplanner
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor http://www.groendyst.dtu.dk/english
General course objectives
The course objective is to enable the students to create visualizations of complex data sets and to apply common strategies for understanding the content of media (e.g. text, music, images, etc).
Learning objectives
A student who has met the objectives of the course will be able to:
  • Access and assess types of available on-line data for data visualization.
  • Use state-of-the-art tools to filter, clean, and organize large, complex datasets
  • Apply standard tools from high-level programming languages (e.g. Python, MatLab, R) to evaluate data visualization methods for exploration of single variable data, including dot and jitter plots, histograms, kernel density estimates, distribution functions, and more.
  • Assess and apply data visualization methods for data exploration of multiple variable data, including estimating functional relationships (e.g. by smoothing noise, visualizing residuals, using log, semilog-plots, and simple regressions).
  • Use visualization techniques to evaluate and identify limitations of summary statistics, based e.g. on Simpson’s paradox, and Anscombe’s quartet.
  • Use basic principles of displaying visual information (e.g. Tufte’s six principles of graphical integrity) to create explanatory visualizations.
  • Apply specialized visualization software (e.g. JavaScrip’s D3 library or Python libraries) in order to build custom visualizations designed to explain insights from a dataset to an audience.
  • Analyze cases of narrative data visualization to extract the underlying principles used to construct this type of visualization.
  • Build a narrative data-visualization.
Content
The course is based on mastering tools for analyzing data sets generated from online social interactions. The course is structured around short lectures combined with exercises, as well as a high degree of independent project work
Last updated
02. maj, 2024