Team Takes Top Spots in International Data Mining Competition
Chemnitz mathematics students went up against 162 teams from 35 countries
A group of students from Chemnitz University of Technology in the Master’s program Data Science asserted itself at the 21st edition of the Data Mining Cup (DMC), reaching 2nd and 5th placec. To do so, they competed against 162 teams from 126 universities from 35 different countries. "We are proud of our outstanding performance. This is the result of good teamwork, which everyone has done in their spare time despite studies, student jobs and the current COVID-19 situation. Our effective time planning as well as the team composition of both competitive new and experienced students is recommendable for future participation in the DMC," says Manuel Richter, who studies Data Science at Chemnitz University of Technology.
Data Mining is about collecting large data sets (Big Data) and deriving trends and new developments from them. It is thus a way to test knowledge from the lecture hall on a practice-oriented data mining task. "Participation in the competition enabled the students to link the content taught at Chemnitz University of Technology with a real and practical case. The teams developed a common understanding of the data set to be analysed and applied methods of 'machine learning' to predict demand," explains Prof. Dr. Martin Stoll, Dean of Studies for the Data Science program and holder of the Professorship of Scientific Computing at Chemnitz University of Technology.
Mixed team prepared online for the challenge
The university’s team of eight people included students with competition experience from the previous year, first-year students and a student from the TU Darmstadt. During the preparation phase, they met weekly online to prepare for the challenge of international competition.
The task for all participants in the competition was to forecast the purchasing demand for over 10,000 of a retail company’s products for the next 14 days. For retailers, this has many advantages. Storage areas can be reduced in order to provide a more attractive shopping experience with larger and more open sales areas. In addition, optimized inventory planning increases product availability, allowing customers to benefit from reduced waiting and delivery times. As a result, customer satisfaction and sales increase for the company. Here, the initiative of the Chemnitz students was rewarded with 2nd and 5th places.
Not only the students but also the professors are happy about these excellent rankings. "The result is a great success for our students, who have organized themselves so fantastically in this time of corona and have undoubtedly proven their creativity in solving a real, data-based problem. Congratulations," says Martin Stoll.
Background: Data Science and AI
Data science and artificial intelligence are currently on everyone's lips. The analysis of data plays an increasingly important role in both university research and industrial applications. It is more important than ever to better understand the algorithms underlying data-based decisions. Mathematics plays a key role here, since a fundamental understanding of the underlying structures can lead to the development of new and improved methods that will allow us to tackle the challenges of the future.
The Faculty of Mathematics at Chemnitz University of Technology plays a pioneering role in Saxony in this area. "Through the Data Science course (https://www.tu-chemnitz.de/mathematik/ds/ ), which was established in 2018 and was jointly launched by the Faculties of Mathematics and Computer Science, our university offers a unique environment for acquiring sound knowledge in all areas. Students learn how to use mathematical tools to master the challenges of the 21st century with modern machine learning techniques and the use of programming languages such as Python," explains Prof. Dr. Martin Stoll.
Questions about the course of study are answered by Prof. Dr. Martin Stoll: firstname.lastname@example.org
Further information about the Master's program is available online.
(Author: Matthias Fejes/Translation: Chelsea Burris)