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Advanced Practical Course Data Science (Winter 2021/2022): Difference between revisions

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Advanced Practical Course Data Science (Winter 2021/2022) (view source)

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|credits=180h, 6 ECTS
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk]
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/weijun-wang MSc. Weijun Wang]
|ta=TBD
|ta=Guanxiong Luo, Weijun Wang
|time=Friday 16:00 - 18:00
|time=Friday 16:00 - 18:00
|place=2.101(online)
|place=(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}
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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.
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).
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).
Data Science for Smart City, we focus on one specific data, i.e., visual data (images and videos). We try to build a system that uses the data analysis methods to extract useful information. This part collaborated with the Goettingen government and the Goettingen bus company.
The goal of this course is to:
* Help students to further understand computer networks and data science knowledge.
* Help students to use computer science knowledge to build a practical AI system.、
* Guide students to utilize knowledge to improve the performance of the system.
In this course, each student (max. number 30) needs to:
* Read state-of-art papers.
* Use programming to build systems including computer vision algorithms, embedded design programs, and SOCKET network programs.
* Learn how to analyze city public transport sensor data.
The final task of students and implementation plan
The students will be divided into 2-person teams. Each group will take responsibility to reimplement (and possibly adopt) a different existing software architecture for all the bus lines used in our project. Two of the 2-person teams in each group will be responsible for one specific sub-task inside independently (in case one team can’t compete). The teams inside one group will therefore have to co-operate.
Note that we will give a default version of each module to guarantee the basic operation of the whole system.


==Prerequisites==
==Prerequisites==
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|{{Hl2}} |'''What?'''
|{{Hl2}} |'''What?'''
|-
|-
| align="right" | 15.04.2021
| align="right" | 29.10.2021
| Lecture 1: Introduction & The Data Science Pipeline
| Lecture 1: Introduction  
|-
|-
| align="right" | 22.04.2021
| align="right" | 05.11.2021
| No lecture (Girls Day)
| Lecture 2: The Data Science Pipeline
|-
|-
| align="right" | 29.04.2021
| align="right" | 12.11.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
| No Lecture
|-
|-
| align="right" | 06.05.2021
| align="right" | 19.11.2021
| Task 1: Intermediate meeting
| Lecture 3: The Python Data Science Stack - Task 1: Release
|-
|-
| align="right" | 13.05.2021
| align="right" | 26.11.2021
| No lecture (Ascension Day)
| No lecture  
|-
|-
| align="right" | 20.05.2021
| align="right" | 03.12.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
| Lecture 4: Video analysis in smart city - Task 2: Release
|-
|-
| align="right" | 27.05.2021
| align="right" | 10.12.2021
| Lecture 4: Evaluation and Tuning of Models
| TBD
|-
|-
| align="right" | 06.03.2021
| align="right" | 17.12.2021
| No lecture
| TBD
|-
|-
| align="right" | 10.06.2021
| align="right" | 24.12.2021
| No lecture  
| No lecture  
|-
|-
| align="right" | 17.06.2021
| align="right" | 31.12.2021
| No lecture   
| No lecture   
|-
|-
| align="right" | 24.06.2021
| align="right" | 07.01.2022
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
| No lecture
|-
|-
| align="right" | 08.07.2021
| align="right" | 14.01.2022
| Task 3: Intermediate meeting
| Task 3 released.
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
|-
| align="right" | 22.07.2021
| align="right" | 23-25.02.2022
| Final Presentation (TBD)
| Final Presentation
|-
|-
| align="right" | 31.09.2021
| align="right" | 28.02.2022
| Final Report deadline (Including report and code)
| Final Report deadline (Including report and code)
|-
|-
|}
|}
==Announcement==
'''05/12/2021: Today will not have lecture. Task 1 will be released before 5 pm.'''
'''Due to the recent situations in the context of Covid-19, new information will be updated here in time, please check this webpage periodically to get the newest information.
'''
==General Description==
Computer Networks Group, Institute of Computer Science, Universität Göttingen is collaborating with Göttinger Verkehrsbetriebe GmbH (represented by Dipl. Anne-Katrin Engelmann) and setting up this exciting course.
==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 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 computer networking and mobile communications.
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries


==Grading==
==Grading==
* Participation: 50%
* Participation:  
** Task 1: 20%
** Task 1:
** Task 2: 30%
** Task 2:  
** Task 3:


* Presentation: 20%
* Presentation:  
**Present on your work with a slide to the audience (in English).
**Present on your work with a slide to the audience (in English).
**20 minutes of presentation followed by 10 minutes Q &A for one student.
**20 minutes of presentation followed by 10 minutes Q &A for one student.
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Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. 
Note: The team needs to clearly introduce the division of their work, and both team members need to present their respective work and answer questions. 


* Final report: 30%
* Final report:  
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.  
The report must be written in English according to common guidelines for scientific papers, 6-8 pages for a student and 12-16 pages for a team of content (excluding bibliography, etc.) in double-column latex.  
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.
Please note that you can not directly copy content from papers or webpages, as this will be considered plagiarism, and we will treat it seriously. All quoted images and tables need to indicate their source.
The source code, data (or URL of data) and a manual should be uploaded with the report.
The source code, data (or URL of data) and a manual should be uploaded with the report.
==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2|width =0.2}} |'''Time'''
|{{Hl2|width =0.5}} |'''Topic'''
|{{Hl2}} |'''Output'''
|-
| align="right"|
w1
| Lecture I:
| No
|-
| align="right"|
w2
| Lecture II:
|
|-
| align="right"|
w3-4
|
| No
|-
| align="right"|
w5-8
|
Task 1:
|
|-
| align="right"|
w8 (9th June)
|
Discussion on Task 1
|NO
|-
| align="right"|
w9-13
|Task 2
|Report
|-
| align="right" |
17.08
|  Final presentations
|
|-
| align="right" |
24.08
|  Final report
|
|-
|}
Wwang
100

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