Practical Course on Data Science for Computer Networks (Winter 2016/2017)

Details

Workload/ECTS Credits:	180h, 6 ECTS
Module:	M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer:	Dr. David Koll
Teaching assistant:	TBA
Time:	start:20th October 2016, 16.15 CET (Introduction Meeting)
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).

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.

Slides and Task Descriptions

• TBA

Schedule (Tentative)

• October 20: Informational meeting
• November 3: Submission of Warmup-Problem (5% of final grade)
  • 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 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 data set for second project
• January 4
  • 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 data set for third project
• TBA: Presentation of results for third project (40% of final grade)
• March 31: Submission of final reports for projects 1-3 (20% of final grade)