Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018): Difference between revisions

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==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.  


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).
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:
 
* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* 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==

Revision as of 11:13, 27 July 2017

Details

Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer: Dr. David Koll
Teaching assistant: None
Time: TBA
Place: TBA
UniVZ TBA


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.

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:

  • Introduction to the practical machine learning pipeline
  • Exploratory data analysis
  • The Python Data Science stack
  • 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

  • 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.)
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