The students are taught elementary theoretical knowledge and get first practical experience in the data analysis domain.
They obtain the ability to assess requirements and parameters for the application of fundamental analysis algorithms. Beyond that, students will practically apply and assess the results in an autonomous way.
In the visualization area, they are taught appropriate visual mappings for varying data types, and will apply them to form useful interactive visualization systems.
The students will be enabled to judge design decisions considering properties of human perception and to develop and assess visualizations solutions.
After completion of the course you will be able to:
- Evaluate different Data Analysis (DA) processes and their differentiating key aspects.
- Apply selected techniques and algorithms to a data set from a task-oriented perspective.
- Analyze semi-structured and unstructured data, for example using text analysis.
- Use external data sources in analyses to derive new insights.
- Relate the potential negative impact of data quality problems.
- Use principles of human perception and cognition in visualization design
- Conceptualize ideas and interaction techniques using sketching and prototyping
- Apply methods for visualization of data from a variety of fields
- Create web-based interactive visualizations using D3 and Dash
- Work constructively as a member of a team to carry out a complex project
- Lectures (attendance mandatory)
- Practical Group Assignments (60%)
- Final Exam (40%)
- Fundamental Data Mining Methods
- Data Preparation and Preprocessing
- Common Analysis Algorithms and Methods
- Principles of Information Visualization
- Human Perception and Visualization Design
- Data Visualization Techniques for Particular Data Types
The lecture is separated in three parts. Part one deals with the principal data understanding methods, the second and main focus lies on automatic data preprocessing, cluster & outlier analysis techniques, classification and association rules. Subject of the third part are the basics of information visualization, the foundations of human perception and user interface design.
INFOB2DA features a dedicated course website that should be used as a single source of truth: https://infob2da.gitlab.io/
This website contains:
- a schedule with lecture topics, assignment timings, and office hour information
- the course syllabus with course requirements, learning objectives, and grading information
- information on resources and related work pointers (text books)
- practical assignment information
- contact information
- and more...
Lectures (mandatory), practicals, assignments.
- Data Mining part: Han J., Kamber M., "Data Mining: Concepts and Techniques", third edition, 2011, Morgan Kaufmann Publishers
- Visual Analysis part: Ward, Grinstein, and Keim , "Interactive Data Visualization: Foundations, Techniques, and Application" 2010, A.K. Peters, Ltd, ISBN: 978-1-56881-473-5, http//www.idvbook.com
Competenties-Ingangseisen |Verplicht materiaal|
|Peng, Roger D., and Elizabeth Matsui. 2016. The Art of Data Science: A Guide for Anyone Who Works with Data. Lulu.com. https://leanpub.com/artofdatascience.|
|James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning. Springer Texts in Statistics. Springer. https://doi.org/10.1007/978-1-4614-7138-7.|
|Chapman et al. (2000). “CRISP-DM 1.0: Step-by-Step Data Mining Guide.” Technical report. The CRISP-DM consortium. ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf|