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Advanced Practical Course Data Science (Summer 2021): Difference between revisions

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Advanced Practical Course Data Science (Summer 2021) (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/jiaquan-zhang MSc. Jiaquan Zhang]; [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/fabian-wölk MSc. Fabian Wölk]
|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=Thursday 16:00-18:00
|place=2.101(online)
|place=2.101(online)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=256838&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung&k_semester.semid=20202&idcol=k_semester.semid&idval=20202&getglobal=semester]
|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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*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 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.).
*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 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
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 15.04.2021
| Lecture 1: Introduction & The Data Science Pipeline
|-
| align="right" | 22.04.2021
| No lecture (Girls Day)
|-
| align="right" | 29.04.2021
| Lecture 2: The Python Data Science Stack - Task 1: Release
|-
| align="right" | 06.05.2021
|  Task 1: Intermediate meeting
|-
| align="right" | 13.05.2021
| No lecture (Ascension Day)
|-
| align="right" | 20.05.2021
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
|-
| align="right" | 27.05.2021
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 06.03.2021
| No lecture
|-
| align="right" | 10.06.2021
| No lecture
|-
| align="right" | 17.06.2021
| No lecture 
|-
| align="right" | 24.06.2021
| // Task 3: release // Task 2 report submission
|-
| align="right" | 01.07.2021
| No lecture
|-
| align="right" | 08.07.2021
| Task 3: Intermediate meeting
|-
| align="right" | 15.07.2021
| Final Presentation (TBD)
|-
| align="right" | 22.07.2021
| Final Presentation (TBD)
|-
| align="right" | 31.09.2021
| Final Report deadline (Including report and code)
|-
|}
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