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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=TBA
|time=Thursday, 14-16
|place=TBA
|place=Ifi 0.101
|univz=TBA
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}


==Course Organization==
==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. The course is structured as a competition, i.e., all groups of students will receive the same tasks.
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:


Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (March 31).
* 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 continous 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 (March 31).


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