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.


  • 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.

Course Sessions and NEWS

Update 27.06.2023: The initial 2023 website content has been uploaded; text still under revision


Tuesdays from 05.09.2023 15:15 - 17:00, Location: KBG - ATLAS
Thursdays 07.09.2023 09:00 - 10:45, Location: KBG - PANGEA
Thursdays from 14.09.2023 09:00 - 10:45, Location: RUPPERT - 040

Tutorials/Assigments/Labs (werkcollege)

Mondays from 11.09.2023 17:15 - 19:00, Location: DALTON 500. Room: See below.

  • TA Alister: 5.08.
  • TA Elio: 5.09.
  • TA Kalee: 5.27.
  • TA Sacha: 6.06.
  • TA Vincent: 6.08.

Office Hours: To be announced (Office hours)

Office hours are posted here.

Lecture Resources:
Discussion forum on MS Teams (Discussion Channel)
Materials and grades also on MS Teams (General -> Files)


7.5 ECTS-Credits for lecture, tutorials, labs, and homeworks; Representing in total 210 hours, split into

  • 50 hours course of study with attendance
  • 160 hours of self-study time

Instructor and Head TF

Michael Behrisch (Instructor)
Alister Machado dos Reis (Head TF)

Teaching Fellows

Alister Machado dos Reis

Elio Verhoef

Kalee Said

Sacha Vermeer

Vincent Haverhoek

COVID-19 Rules for this Class

We are following the Utrecht University COVID-19 Rules. Generally, we will keep the work as remote as possible, while still trying to foster community building aspect.

Lectures will be held ON-SITE; The Labs/Werkcolleges are currently planned to be ON-SITE.
Please be aware that this information can change rapidly.

Previous Years (Archive)

INFOB2DA 2020 Website

INFOB2DA 2021 Website

INFOB2DA 2022 Website