Our goal is to support project-based learning in data science education. We scaffold project solutions for four common data science problems: binary classification, multi-class classification, regression and clustering, using template solutions implemented as Jupyter notebooks that can be run on any dataset provided by a student in line with formatting guidelines. For data-specific steps (like feature engineering), we extend our templates using a custom Python library for integrating an LLM with a Jupyter notebook. The library works both for open-source models and paid APIs, and allows a chatbot to be integrated in any cell of a Jupyter notebook. Together, these form an interactive data science AI tutor (DSAIT). We study the interaction patterns of 24 data science students from two universities who attempted to solve a Kaggle challenge using DSAIT's assistance, and conduct a temporal analysis of how these patterns evolved during the task. We provide the templates and Python library used to implement DSAIT to the computer science education community.

Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that, at the same time, defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe FSMSet, an algorithm that employs the new support definition, and demonstrate its performance on field data from the automotive business.

The task of information filtering is to classify documents from a stream as either relevant or non-relevant according to a particular user interest with the objective to reduce information load. When using an information filter in an environment that is changing with time, methods for adapting the filter should be considered in order to retain classification accuracy. We favor a methodology that attempts to detect changes and adapts the information filter only if inevitable in order to minimize the amount of user feedback for providing new training data. Yet, detecting changes may require costly user feedback as well. This paper describes two methods for detecting changes without user feedback. The first method is based on evaluating an expected error rate, while the second one observes the fraction of classification decisions made with a confidence below a given threshold. Further, a heuristics for automatically determining this threshold is suggested and the performance of this approach is experimentally explored as a function of the threshold parameter. Some empirical results show that both methods work well in a simulated change scenario with real world data.