Seminar Data Science
The data science seminar involves presentations covering recent topics in data science. The area of this year’s seminar is time series imputation. As part of the seminar, we will study research papers that propose algorithms for imputing missing values and will learn how to integrate them into a ready-made library. The studied papers present methods for reconstructing incomplete sensor data by applying various replacement strategies to estimate missing segments.
Imputation offers benefits on two levels. At the data processing level, the completed time series can be adequately utilized in a wide range of Machine Learning (ML) tasks, such as classification and forecasting. At the data management level, properly imputed time series can be more effectively stored and maintained, one reason why many Time Series Database Systems (TSDBs) have begun to incorporate native support for missing value imputation.
Details
| Code | 63626 |
| Type | Seminar |
| ECTS | 5 |
| Site | Fribourg |
| Track(s) |
T6 – Data Science |
| Semester | A2025 |
Teaching
| Learning Outcomes | The goal for the students is to learn how to critically read and study research papers, describe a paper in a report, and present it in a seminar. Under supervision, students will select one paper to study and compare it with related work. This seminar aims to help students gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the time series field in the form of both a presentation, a written report, and an empirical evaluation. |
| Lecturer(s) |
Mourad Khayati |
| Language | english |
| Course Page | The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_3419457.html. |
Schedules and Rooms
| Period | On Appointment |
| Location | UniFR, PER21 |
Evaluation
| Evaluation type | continuous evaluation |
Additional information
| Comment | First Lecture |