Data Science: Advanced Analytics
Data Science: Advanced Analytics
for questions, please contact: ad_isd@tu-dresden.de
Consultation with Prof. Brendel: on request(please contact kerstin.petzold@tu-dresden.de for an appointment)
Course description
The module deals with the basics of unsupervised methods for finding patterns in structured and unstructured data. Students learn methods from the field of machine learning using analytical information systems. Students are able to apply these methods to various practical examples and to evaluate, interpret and critically question the results.
Students are able to analyze unstructured data using text-mining methods. The students have an understanding of problems that can occur in the course of the knowledge-discovery-in-databases process and are able to recognize and solve these problems. Furthermore, the students have knowledge of special procedures in the context of specific application scenarios of machine learning, such as process analysis (process mining) and anomaly detection. They have the ability to design and implement solutions to problems on the basis of structured and unstructured data with selected application systems of machine learning.
Course facts
- Format: e-Lectures + In-Person exercises + In-Person consultations
- Language: English
- Semester: every summer term
- Scope: 5 CP
Important information
The number of participants is limited to 60. First come, first serve. There will be a waiting list. This semester in-person exercises and consultations will be offered.
There will be an In-Person Kick-off meeting with all the relevant information about the course. The corresponding information can be found in the timetable.
Final exam
The exam will take place this semester as a hybrid exam in OPAL. Further information will be given during the course.
Timetable
Kick-Off-Meeting:
- Date: 12.04.2023
- Hour: 11:10 - 12:40
- Place: TIL 205 (Entrance via the Hülße-Bau, follow the signs pointing to the Festsaal)
Exercises:
- Date: According to Timetable
- Hour: 11:10 - 12:40
- Place: Microsoft Teams (link in e-lectures menu)
In-Person Consultations:
- Dates:
- 04.05.2023 (Thursday)
- 11.05.2023 (Thursday)
- 08.06.2023 (Thursday)
- 29.06.2023 (Thursday)
- 20.07.2023 (to be confirmed)
- Hour: 13:10 - 14:20
- Place: SCH A200a
| Week number | Date | Holiday | Topic |
| 14 | 05.04.2023 | Eastern (Fr.) | - |
| 15 | 12.04.2023 | Eastern (Monday) | Kick-off |
| 16 | 19.04.2023 | Lecture - Introduction & Segmentation basis | |
| 17 | 26.04.2023 | Lecture - Segmentation Methods | |
| 18 | 03.05.2023 | 1. May (Monday) | Lecture - Association Analysis |
| 19 | 10.05.2023 | Dies Academicus (Wednesday) | - |
| 20 | 17.05.2023 | Himmelfahrt (Thursday) | Exercise 1 - Segmentation |
| 21 | 24.05.2023 | Lecture - Text Mining | |
| 22 | 31.05.2023 | Pfingsten (entire week) | - |
| 23 | 07.06.2023 | Exercise 2 - Text Mining | |
| 24 | 14.06.2023 | Lecture - Deep Learning | |
| 25 | 21.06.2023 | Lecture - Anomaly Detection | |
| 26 | 28.06.2023 | Lecture - Process Analytics | |
| 27 | 05.07.2023 | Exercise 3 - Deep Learning | |
| 28 | 12.07.2023 | Trial Exam | |
| 29 | 19.07.2023 | Begin of the exam period |
Access to e-lecture videos
After the kick-off, enrolled students will be able to find links to E-lecture videos in the link list. These videos will be hosted at videocampus sachsen.