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Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) (view source)

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{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}
== Details ==
== Details ==
{{CourseDetails
{{CourseDetails
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|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|ta=
|time=Thursday, 14-16 (tentative)
|time=Thursday, 14-16  
|place=Ifi 3.101 (tentative)
|place=Ifi 0.101  
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}
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==Prerequisites==
==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 Coursera 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 Coursera 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 Python or R
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries


==Preliminary Schedule==
==Schedule==
{| {{Prettytable|width=}}
{| {{Prettytable|width=}}
|-
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
|-
| align="right" | 19.10.2017
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
|-
| align="right" | 26.10.2017
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| Lecture 2: The Python Data Science Stack
|
|-
|-
| align="right" | 02.11.2017
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
| Task 1: Intermediate meeting
|
|-
|-
| align="right" | 09.11.2017
| align="right" | 09.11.2017
| No lecture
| No lecture
|
|-
|-
| align="right" | 16.11.2017
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|
|-
|-
| align="right" | 23.11.2017
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
| Lecture 3: Advanced Algorithms for Data Science
|
|-
|-
| align="right" | 30.11.2017
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
| Lecture 4: Evaluation and Tuning of Models
|
|-
|-
| align="right" | 07.12.2017
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
| Task 2: Intermediate meeting
|
|-
|-
| align="right" | 14.12.2017
| align="right" | 14.12.2017
| No lecture
| No lecture
|
|-
|-
| align="right" | 21.12.2017
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
| Task 2: Presentations // Task 3: Release
|
|-
|-
| align="right" | 04.01.2018
| align="right" | 04.01.2018
| No lecture
| No lecture
|
|-
|-
| align="right" | 11.01.2018
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
| Task 3: Intermediate meeting I
|
|-
|-
| align="right" | 18.01.2018
| align="right" | 18.01.2018
| No lecture  
| No lecture  
|
|-
|-
| align="right" | 25.01.2018
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
| Task 3: Intermediate meeting II
| 
|-
|-
| align="right" | 01.02.2018
| align="right" | 01.02.2018
| No lecture
| No lecture  
| 
|-
|-
| align="right" | 08.02.2018-22.02.2018
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
| Task 3: Presentations  
| 
|-
|-
| align="right" | 31.03.2018
| align="right" | 31.03.2018
| Final Report deadline  
| Final Report deadline  
|
|-
|-
|}
|}
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