Data Science in Smart City (Summer 2022): Difference between revisions
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|{{Hl2}} |'''What?''' | |{{Hl2}} |'''What?''' | ||
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| align=" | | align="left" | 04.04.2022 8:00-10:00 | ||
| Lecture 1 | | Lecture 1 | ||
|- | |- | ||
| align=" | | align="left" | 11.04.2022 8:00-10:00 | ||
| Lecture 2 | | Lecture 2 | ||
|- | |- | ||
| align=" | | align="left" | 18.04.2022 8:00-10:00 | ||
| Lecture 3 & Release of Task 1 | | Lecture 3 & Release of Task 1 | ||
|- | |- | ||
| align=" | | align="left" | 25.04.2022 | ||
| No Lecture | | No Lecture | ||
|- | |- | ||
| align=" | | align="left" | 02.05.2022 8:00-9:00 | ||
| Intermediate meeting of Task 1 | | Intermediate meeting of Task 1 | ||
|- | |- | ||
| align=" | | align="left" | 09.05.2022 8:00-10:00 | ||
| Lecture 4 & Task 1 report submission (Before 10PM) | | Lecture 4 & Task 1 report submission (Before 10PM) | ||
|- | |- | ||
| align=" | | align="left" | 16.05.2022 | ||
| Lecture 5 & Release of Task 2 | | Lecture 5 & Release of Task 2 | ||
|- | |- | ||
| align=" | | align="left" | 06.06.2022 | ||
| Task 2 report submission (Before 10PM) | | Task 2 report submission (Before 10PM) | ||
|- | |- | ||
| align=" | | align="left" | 07.06.2022 | ||
| Release of Task 3 | | Release of Task 3 | ||
|- | |- | ||
| align=" | | align="left" | 20.06.2022 8:00-9:00 | ||
| Intermediate meeting of Task 3 | | Intermediate meeting of Task 3 | ||
|- | |- | ||
| align=" | | align="left" | TBD | ||
| Final Presentation | | Final Presentation | ||
|- | |- | ||
| align=" | | align="left" | 30.09.2022 | ||
| Final Report deadline (Including report and code) | | Final Report deadline (Including report and code) | ||
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Revision as of 15:06, 15 March 2022
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Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there. |
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Note: This page is not finished |
Details
| Workload/ECTS Credits: | 180h, 6 ECTS |
| Module: | M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |
| Lecturer: | Prof. Xiaoming Fu; Zhengze Li |
| Teaching assistant: | Zhengze Li, Weijun Wang |
| Time: | Mondays 8:00 - 10:00 |
| Place: | (online) |
| UniVZ | [1] |
Course Organization
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.
While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:
- Introduction to the practical data science pipeline
- Exploratory data analysis
- The Python Data Science stack
- Video Analytics
Students need to finish three tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester. A final report needs to be submitted at the end of the semester (September 30).
Schedule
| When? | What? |
| 04.04.2022 8:00-10:00 | Lecture 1 |
| 11.04.2022 8:00-10:00 | Lecture 2 |
| 18.04.2022 8:00-10:00 | Lecture 3 & Release of Task 1 |
| 25.04.2022 | No Lecture |
| 02.05.2022 8:00-9:00 | Intermediate meeting of Task 1 |
| 09.05.2022 8:00-10:00 | Lecture 4 & Task 1 report submission (Before 10PM) |
| 16.05.2022 | Lecture 5 & Release of Task 2 |
| 06.06.2022 | Task 2 report submission (Before 10PM) |
| 07.06.2022 | Release of Task 3 |
| 20.06.2022 8:00-9:00 | Intermediate meeting of Task 3 |
| TBD | Final Presentation |
| 30.09.2022 | Final Report deadline (Including report and code) |