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
The course consists of the lecture and exercises. The exercises are not mandatory, though highly recommended.
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, association rules (Apriori and FP Growth).
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 20, Nuremberg, lecture hall 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 18, 2024.
Please do not send an email for registration. We have limited capacity so not all students who have registered can be accepted.
To gain access to the course material (video lectures, slides, forum) please register on StudOn. Registration starts on March 01 and ends April 14.
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