Advanced Practical Course Data Science (Winter 2020/2021): Difference between revisions
Jump to navigation
Jump to search
No edit summary |
No edit summary |
||
| (19 intermediate revisions by the same user not shown) | |||
| Line 8: | Line 8: | ||
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang] | |lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang] | ||
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang] | |ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang] | ||
|time= | |time=Friday 16:00-18:00 | ||
|place= | |place=2.101(online) | ||
|univz=[https://univz.uni-goettingen.de/qisserver | |univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20202&getglobal=semester&htmlBodyOnly=true] | ||
}} | }} | ||
==Announcement== | ==Announcement== | ||
Due to the recent situations in the context of Covid-19, how to arrange the lectures in this winter semester is still not determined (online or face-to face lecturing, when and where are the lectures, how is presentation arranged). Any new information will be updated here in time, please check this webpage periodically to get the newest information. | '''20.10.2020 Our first lecture will be on 06.11.2020 through the Meeings tool on StudIP''' | ||
'''30.09.2020 Our course in this semeter will be online''' | |||
'''Due to the recent situations in the context of Covid-19, how to arrange the lectures in this winter semester is still not determined (online or face-to face lecturing, when and where are the lectures, how is presentation arranged). Any new information will be updated here in time, please check this webpage periodically to get the newest information. | |||
''' | |||
| Line 44: | Line 50: | ||
|{{Hl2}} |'''What?''' | |{{Hl2}} |'''What?''' | ||
|- | |- | ||
| align="right" | TBD | | align="right" | 06.11.2020 | ||
| TBD | | Lecture 1: Introduction & The Data Science Pipeline | ||
|- | |||
| align="right" | 13.11.2020 | |||
| Lecture 2: The Python Data Science Stack - Task 1: Release | |||
|- | |||
| align="right" | 20.11.2020 | |||
| No lecture | |||
|- | |||
| align="right" | 27.11.2020 | |||
| Task 1: Intermediate meeting | |||
|- | |||
| align="right" | 04.12.2020 | |||
| // Task 1 report submission //Task 2: release | |||
|- | |||
| align="right" | 11.12.2020 | |||
| No lecture | |||
|- | |||
| align="right" | 18.12.2020 | |||
| Lecture 3: Advanced Algorithms for Data Science | |||
|- | |||
| align="right" | 25.12.2020 | |||
| No lecture // Task 2 report submission | |||
|- | |||
| align="right" | 01.01.2021 | |||
| No lecture (holidays） | |||
|- | |||
| align="right" | 08.01.2021 | |||
| No lecture (holidays) | |||
|- | |||
| align="right" | 15.01.2021 | |||
| Lecture 4: Evaluation and Tuning of Models // Task 3: release | |||
|- | |||
| align="right" | 22.01.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 29.01.2021 | |||
| Task 3: Intermediate meeting | |||
|- | |||
| align="right" | 05.02.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 12.02.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 31.03.2021 | |||
| Final Report deadline (Including report and code) | |||
|- | |- | ||
|} | |} | ||
Latest revision as of 03:30, 11 December 2020
|
Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there. |
Details
| Workload/ECTS Credits: | 180h, 6 ECTS |
| Module: | M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |
| Lecturer: | Prof. Xiaoming Fu; MSc. Jiaquan Zhang |
| Teaching assistant: | MSc. Jiaquan Zhang |
| Time: | Friday 16:00-18:00 |
| Place: | 2.101(online) |
| UniVZ | [1] |
Announcement
20.10.2020 Our first lecture will be on 06.11.2020 through the Meeings tool on StudIP
30.09.2020 Our course in this semeter will be online
Due to the recent situations in the context of Covid-19, how to arrange the lectures in this winter semester is still not determined (online or face-to face lecturing, when and where are the lectures, how is presentation arranged). Any new information will be updated here in time, please check this webpage periodically to get the newest information.
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 machine learning pipeline
- Exploratory data analysis
- The Python Data Science stack
- How to deal with unbalanced data
- Advanced algorithms for Data Science (an overview of competition winning algorithms)
- Parameter tuning for predictive models
Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continuous effort throughout the whole semester. Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (September 30).
Prerequisites
- You are highly recommended to have completed a course on Data Science (e.g., "Data Science and Big Data Analytics" taught by Dr. Steffen Herbold or the Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
- Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
Schedule
| When? | What? |
| 06.11.2020 | Lecture 1: Introduction & The Data Science Pipeline |
| 13.11.2020 | Lecture 2: The Python Data Science Stack - Task 1: Release |
| 20.11.2020 | No lecture |
| 27.11.2020 | Task 1: Intermediate meeting |
| 04.12.2020 | // Task 1 report submission //Task 2: release |
| 11.12.2020 | No lecture |
| 18.12.2020 | Lecture 3: Advanced Algorithms for Data Science |
| 25.12.2020 | No lecture // Task 2 report submission |
| 01.01.2021 | No lecture (holidays） |
| 08.01.2021 | No lecture (holidays) |
| 15.01.2021 | Lecture 4: Evaluation and Tuning of Models // Task 3: release |
| 22.01.2021 | No lecture |
| 29.01.2021 | Task 3: Intermediate meeting |
| 05.02.2021 | Final Presentation (TBD) |
| 12.02.2021 | Final Presentation (TBD) |
| 31.03.2021 | Final Report deadline (Including report and code) |