Practical Course on Data Science for Computer Networks (Winter 2016/2017): Difference between revisions
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== 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= | |ta=None | ||
|time= | |time=Thursday, 14-16 (bi-weekly) | ||
|place=IFI 3.101 | |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] | |univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=196722&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | ||
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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). | 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). | ||
There is an option to participate as a team in the 2017 [http://btw2017.informatik.uni-stuttgart.de/?pageId=CallForDSChallenge BTW Data Science Challenge] (sponsored by IBM). | |||
==Prerequisites== | ==Prerequisites== | ||
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* Prepare a '''written report''' on the work done in the course (15-20 pages containing the most important steps taken and their results, Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]) (20%) | * Prepare a '''written report''' on the work done in the course (15-20 pages containing the most important steps taken and their results, Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]) (20%) | ||
* It is mandatory for all students to '''stick to the deadlines''' mentioned in [[#Schedule]]''' and to attend other teams' presentations. | * It is mandatory for all students to '''stick to the deadlines''' mentioned in [[#Schedule]]''' and to attend other teams' presentations. | ||
==Schedule (Tentative)== | ==Schedule (Tentative)== | ||
* October 20 | * October 20: | ||
** Informational meeting | ** Informational meeting | ||
** Release of warmup problem | ** Release of warmup problem | ||
* November 3: Submission of warmup problem (5% of final grade) | * November 3: Submission of warmup problem (5% of final grade) in single PDF by E-Mail to David | ||
** Submit as a PDF report | ** 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. | ** In the PDF describe your steps in exploratory data analysis and link your results to the predictive model you have built. | ||
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** Overall, this submission also decides on whether or not you will be able to continue the course. | ** Overall, this submission also decides on whether or not you will be able to continue the course. | ||
* November 3: Release of first project (on network security) | * November 3: Release of first project (on network security) | ||
* November 17: Meeting to discuss properties of data set for first project | * November 17: Meeting to discuss properties of / problems with data set for first project | ||
* December 1 | * December 1: | ||
**Presentation of first project results (20% of final grade) | **Presentation of first project results (20% of final grade) | ||
** Release of second project (on social network analysis) | ** '''Release of second project (on social network analysis)''' | ||
* December 18: Meeting to discuss properties of data set for second project | * '''December 18: Meeting to discuss properties of / problems with data set for second project''' | ||
*January | * '''January 13 (note: this is a friday, time and room remains the same):''' | ||
** Presentation of second project results (15% of final grade) | ** '''Presentation of second project results (15% of final grade)''' | ||
** Release of third project (on user profiling in mobile network data) | ** '''Release of third project (on user profiling in mobile network data)''' | ||
* January 25: Meeting to discuss properties of data set for third project | * January 25: Meeting to discuss properties of / problems with data set for third project | ||
* TBA: Presentation of | * Date TBA: Presentation of third project results (40% of final grade) | ||
* March 31: Submission of final reports for projects 1-3 (20% of final grade) | * March 31: Submission of final reports for projects 1-3 (20% of final grade) | ||
All meetings will be at 2.15pm in room IFI 3.101. | |||
Latest revision as of 15:04, 10 April 2017
Details
| Workload/ECTS Credits: | 180h, 6 ECTS |
| Module: | M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |
| Lecturer: | Dr. David Koll |
| Teaching assistant: | None |
| Time: | Thursday, 14-16 (bi-weekly) |
| Place: | IFI 3.101 |
| UniVZ | [1] |
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).
There is an option to participate as a team in the 2017 BTW Data Science Challenge (sponsored by IBM).
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 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.)
Passing requirements
- Solve the warmup-problem (5% of final grade, this is also required in order to continue the course)
- Present your findings in class (75% in total)
- Prepare a written report on the work done in the course (15-20 pages containing the most important steps taken and their results, Template:[2]) (20%)
- It is mandatory for all students to stick to the deadlines mentioned in #Schedule and to attend other teams' presentations.
Schedule (Tentative)
- October 20:
- Informational meeting
- Release of warmup problem
- November 3: 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.
- November 3: Release of first project (on network security)
- November 17: Meeting to discuss properties of / problems with data set for first project
- December 1:
- Presentation of first project results (20% of final grade)
- Release of second project (on social network analysis)
- December 18: Meeting to discuss properties of / problems with data set for second project
- January 13 (note: this is a friday, time and room remains the same):
- Presentation of second project results (15% of final grade)
- Release of third project (on user profiling in mobile network data)
- January 25: Meeting to discuss properties of / problems with data set for third project
- Date TBA: Presentation of third project results (40% of final grade)
- March 31: Submission of final reports for projects 1-3 (20% of final grade)
All meetings will be at 2.15pm in room IFI 3.101.