Applied data analytics is a multidisciplinary field where you will learn insights needed to make sense of data, research, and observations from everyday life.
You will learn how to apply a data-driven approach to problem-solving, but will not only learn about tools, methods, and techniques, or the latest trends, but also more generic insights: why do certain approaches work, why the field is so popular, what common mistakes are made.
The lectures will provide the theoretical background of how a data analytics process should be performed.
Furthermore, we discuss an overview of popular data analytics and visualization techniques to help match techniques with information needs, including applications of text mining and data enrichment.

After completion of the course, you will be able to:

• evaluate different Data Analysis (DA) processes and their differentiating key aspects
• scrutinize and apply DA techniques and algorithms to a data set given a data analysis task
• analyze semi-structured and unstructured data, for example using cluster or 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
• scrutinize and apply data visualization methods for the purpose of demonstrating and discussing (intermediate) results
• work constructively as a member of a team to carry out a complex project

The course grade comprises:

- Practical Assignments/Group Work (60%)
- Final Exam (40%)

Any concerns about grading errors must be noted in writing and submitted to your TA/TF within one week of receiving the grade.

Each of the above grading components (practical assignment and final exam) is graded separately.

Practical assignment: Practical assignments are graded in points; reaching 50% of the points represents a 5.5 grade for this grading component. You are required to score at least 50% of the assignment points to be eligible for the final exam. Practical assignments do not follow the repair-schema. Scoring less than 50% points in the practical assignments leads to failing the course.

Exams: The written (final and retake) exams’ minimum grade is a 5.5; Failing this grading component (exam grade < 5.5) leads to a fail independent of the practical assignment grade; Students who scored at least an a 4.0 in the final exam and handed in at least three of the four practical assignments are allowed to do a retake exam. Final Grade: The final grade is the weighted average of these two grades (60% PA grade + 40% exam grade = final grade).

In this course we will study

• 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 course 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 will be regularly updated with the latest information: 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...

Course format
Lectures (mandatory), self-study, practical assignments, group presentations.

Our experience taught us that genuine active participation is needed to pass the course.
The practical group assignments reflect the practical nature of real-world data analysis scenarios: Students are asked to implement end-to-end data analytics solutions, reflect on their design choices, evaluate the pipeline, and comment on the strengths and weaknesses of the observed results.
A dedicated rubric will be handed out for all practical assignments together with the assignment description.

Literature
The course has no compulsory textbook. However, the following books are strongly recommended as optional reading material, as they give additional details on the material discussed in the course:

• 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

We will detail which chapters from the above books are relevant during the course.

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen

Er is geen informatie over verplichte ingangseisen bekend.

Voorkennis

Er is geen informatie over benodigde voorkennis bekend.

• Engels

• 2026 - 2027
• 2025 - 2026
• 2024 - 2025
• 2023 - 2024
• 2022 - 2023

• Applied Data Science
• Applied Data Science (for students KI)
• Informatiekunde vanaf 2015-2016
• Informatiekunde vanaf 2019-2020
• Informatiekunde vanaf 2020-2021
• Informatiekunde vanaf 2023-2024
• Informatiekunde vanaf 2024-2025
• Informatiekunde voor 2015-2016
• Minor Informatiekunde
• Wiskunde en toepassingen vanaf 2022-2023
• Wiskunde en toepassingen vanaf 2024-2025