View source for Advanced Practical Course Data Science for Computer Networks (Summer 2017)

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{{Announcement | The fourth and final task is now online.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (bi-weekly)
|place=IFI 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=196722&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|
}}

==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.

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 (September 30).

==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.).
*Knowledge of Python or R... 
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Task Descriptions==
* [[Media:ds_task_1.pdf | Task 1: Bike Sharing]]
* [[Media:ds_ss_17_task_2.pdf | Task 2: Network Intrusion Detection]]
* [[Media:ds_ss_17_task_3.pdf | Task 3: Social Network Analysis]]
* Task 4: [[Media:ds_ss_17_task_4.pdf | Mobile Phone Data Analysis]] or Kaggle InstaCart Competition

==Schedule (Tentative)==
* April 13:
** Informational meeting
** Release of warmup problem
* April 20: Release of first project
* April 27: Submission of warmup problem (5% of final grade) in single PDF by E-Mail to David
** Submit as a PDF report
** In the PDF describe your steps in exploratory data analysis and link your results to the predictive model you have built.
** Also attach your Code
** Overall, this submission also decides on whether or not you will be able to continue the course.
* May 4th: Meeting to discuss properties of / problems with data set for first project
* May 18th: 
**Presentation of first project results (20% of final grade)
** Release of second project
* MONDAY, June 12th, 16:00: Meeting to discuss properties of / problems with data set for second project
* TUESDAY, June 20 th, 16:00:
** Presentation of second project results (15% of final grade)
** Release of third project 
* '''July 13, 16:00:''' Meeting to discuss properties of / problems with data set for third project
* August 3: Presentation of third project results (40% of final grade)
* September 30: Submission of final reports for projects 1-3 (20% of final grade)

All meetings will be at 2.15pm in room IFI 3.101.