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Advanced Practical Course Data Science (Summer 2021)

Revision as of 00:28, 13 April 2021 by Jzhang (talk | contribs)
Imbox content.png Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.


Details

Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Lecturer: Prof. Xiaoming Fu; MSc. Fabian Wölk
Teaching assistant: MSc. Jiaquan Zhang
Time: Thursday 16:00-18:00
Place: 2.101(online)
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.

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
  • 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 continuous 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 (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 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 any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

Schedule

When? What?
15.04.2021 Lecture 1: Introduction & The Data Science Pipeline
22.04.2021 Lecture 2: The Python Data Science Stack - Task 1: Release
29.04.2021 No lecture
06.05.2021 Task 1: Intermediate meeting
13.05.2021 No lecture (Ascension Day)
20.05.2021 Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release
27.05.2021 Lecture 4: Evaluation and Tuning of Models
06.03.2021 No lecture
10.06.2021 No lecture
17.06.2021 No lecture // Task 2 report submission
24.06.2021 // Task 3: release
01.07.2021 No lecture
08.07.2021 Task 3: Intermediate meeting
15.07.2021 Final Presentation (TBD)
22.07.2021 Final Presentation (TBD)
31.09.2021 Final Report deadline (Including report and code)
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