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.

Content

  • 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 two parts. The content of the first one are principal Data Mining methods whereas the main focus lies on Data Preprocessing, Cluster & Outlier Analysis, Classification and Association Rules. Subject of the second part are the basics of Information Visualization. Foundations of Human Perception and Design Decisions are followed by examples of visualizations of different data sources (Non-Spatial, Temporal, Geo-Spatial and 3D Spatial Data).

Course Sessions

Lectures:
Tuesday 11:00 - 12:45, Location: Online
Thursday 11:00 - 12:45, Location: Online

Tutorials/Assigments (werkcollege):
Locations to be announced (per group)

Group 1: Thursday 15:15 - 17:00, Location: On SITE TobeDetermined, all groups individually
Group 2: Thursday 15:15 - 17:00, Location: On SITE TobeDetermined, all groups individually
Group 3: Thursday 15:15 - 17:00, Location: On SITE TobeDetermined, all groups individually Group 4: Thursday 15:15 - 17:00, Location: On SITE TobeDetermined, all groups individually

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)

Workload:

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)
Saba Gholizadeh, PhD Candidate, Software Technology (Head TF)

Teaching Fellows

  • Group 1 Diede van der Hoorn
  • Group 2 Hessel Laman
  • Group 3 Anneloes Meijer
  • Group 4 Simardeep Singh

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 remotely, but the Labs will be on-site. Please be aware that this information can change rapidly.