Advanced Practical Course Data Science (Winter 2020/2021): Difference between revisions

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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://134.76.18.81/?q=people/dr-yali-yuan Dr. Yali Yuan]
|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=Thursday, 16-18  
|time=Friday 16:00-18:00
|place=Ifi 2.101  
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver//rds?state=verpublish&status=init&vmfile=no&publishid=248178&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&idcol=k_semester.semid&idval=20201&getglobal=semester&htmlBodyOnly=true]
|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==
22.04.2020 : We decide to use the meetings module in studip for our lectures. Please check the message in Studip.


Due to the recent recommendations in the context of Covid-19, we have to defer the start of the lectures of this course to 23rd April 2020. Currently, this course is scheduled in a purely online, non-face-to-face way. We plan to use some tools and platforms, e.g., zoom or DFNconf, see: https://www.uni-goettingen.de/en/622774.html. '''Please register into studIP in advance. The registration deadline is at 23:59 pm on 22nd April 2020.''' I will announce which tool will be used before our lecture start.
'''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.
'''
 
 
 


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|{{Hl2}} |'''What?'''
|{{Hl2}} |'''What?'''
|-
|-
| align="right" | 23.04.2020
| align="right" | 06.11.2020
| Lecture 1: Introduction & The Data Science Pipeline
| Lecture 1: Introduction & The Data Science Pipeline
|-
|-
| align="right" | 30.04.2020
| align="right" | 13.11.2020
| Lecture 2: The Python Data Science Stack - Task 1: Release
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
|-
| align="right" | 07.05.2020
| align="right" | 20.11.2020
| No lecture  
| No lecture  
|-
|-
| align="right" | 14.05.2020
| align="right" | 27.11.2020
| Task 1: Intermediate meeting
| Task 1: Intermediate meeting
|-
|-
| align="right" | 21.05.2020
| align="right" | 04.12.2020
| No lecture // Task 1 report submission
| // Task 1 report submission //Task 2: release
|-
|-
| align="right" | 28.05.2020
| align="right" | 11.12.2020
| Lecture 3: Advanced Algorithms for Data Science // Task 2: release
| No lecture
|-
|-
| align="right" | 04.06.2020
| align="right" | 18.12.2020
| No lecture
| Lecture 3: Advanced Algorithms for Data Science
|-
|-
| align="right" | 11.06.2020
| align="right" | 25.12.2020
| Lecture 4: Evaluation and Tuning of Models // Task 2 report submission // Task 3: release
| No lecture  // Task 2 report submission  
|-
|-
| align="right" | 18.06.2020
| align="right" | 01.01.2021
| No lecture
| No lecture (holidays）
|-
|-
| align="right" | 25.06.2020
| align="right" | 08.01.2021
| No lecture
| No lecture (holidays)
|-
|-
| align="right" | 02.07.2020
| align="right" | 15.01.2021
| Task 3: Intermediate meeting I
| Lecture 4: Evaluation and Tuning of Models // Task 3: release
|-
|-
| align="right" | 09.07.2020
| align="right" | 22.01.2021
| No lecture
| No lecture
|-
|-
| align="right" | 16.07.2020
| align="right" | 29.01.2021
| Task 3: Intermediate meeting II ('''FlexNow Registration Deadline''')
| Task 3: Intermediate meeting
|-
|-
| align="right" | 23.07.2020
| align="right" | 05.02.2021
| No lecture
| Final Presentation (TBD)
|-
|-
| align="right" | 30.07.2020 (online)
| align="right" | 12.02.2021
| Task 3: Presentations (Final Presentation)
| Final Presentation (TBD)
|-
|-
| align="right" | 20.08.2020
| align="right" | 31.03.2021
| Final Report deadline (Including report and code)
| Final Report deadline (Including report and code)
|-
|-
|}
|}

Latest revision as of 03:30, 11 December 2020

Imbox content.png 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)
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