Business Intelligence

Goals

The students …

  • Know and can describe important business intelligence and data science concepts, tools, and algorithms
  • Lern how to structure and conduct a project of advanced data analysis
  • Work on a practical exercise and apply the learned algorithms to a real-world dataset
  • Are able to evaluate a machine learning model and decide on its goodness of fit

Course Structure

The course consists of the lecture and exercises. The exercises are not mandatory, though highly recommended.

Content

The interest in data analytics has increased tremendously in the last few years, and it is part of almost every business or organization we can think of. There has been a tremendous development in the field since we all heard the term Big Data for the first time close to a decade ago. The demand for skilled practitioners has also increased significantly and is projected to keep increasing in the next years. At the same time, a qualified data scientist or data analyst is expected to have knowledge in different areas like statistics, data mining, data visualization or programming, to name a few. It is often challenging to decide where to start if one has interest in this career path.

In this lecture, we introduce a variety of topics which will give you a kick start in the field of data science and will help you to continue the learning path in other, more advanced courses. We teach the whole data science process (based on the industry-wide accepted CRISP model) from the business and data understanding to the deployment and management steps. Students get familiar with terms like data science, machine learning, and artificial intelligence, as well as available tools and technologies. You will learn what is behind the technology that powers everything from your shopping suggestions on Amazon to automatic systems like chatbots and self-driving cars. We teach you the most used machine learning algorithms right now: decision trees, neural networks, support vector machines, association rules (Apriori and FP Growth), clustering algorithms (k-Means, DBSCAN).

In the end of the lecture, you will know the difference between machine learning and artificial intelligence, understand how the most popular algorithms work, and how they can be applied in practice.
The lecture is intended for students with no prior knowledge in data analytics. After familiarizing with the relevant theory, students also have the chance to apply their knowledge on a given data set. This will be done with a data science tool that does not require any programming skills.

Location and Date

Lecture: Thursdays 13:15 to 14:45 hrs in Lange Gasse H6.

Exercise: self-study, online tutorial for student support available weekly during the semester.

More details will be given during the first lecture on April 28, 2022. 

Course registration

Please do not send an email for registration. 

To gain access to the course material (video lectures, slides, forum) please register on StudOn

Grading and Exam ID

Written exam (5 ECTS), 90 min

70414 (written exam)

70415 (written exam)

Lecture Notes and LoDs

Lecture notes and Lecture-On-Demand packages are available on StudOn