Advanced Practical Course Data Science (Winter 2020/2021)

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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. Jiaquan Zhang
Teaching assistant: MSc. Jiaquan Zhang
Time: Thursday, 16-18
Place: Ifi 2.101
UniVZ [1]


Announcement

Due to the recent situations in the context of Covid-19, how to arrange the lectures in this winter semester is still not determined (online or face-to face lecturing). Any new information will be updated here in time, please check this webpage periodically to get the newest information.

 

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?
23.04.2020 Lecture 1: Introduction & The Data Science Pipeline
30.04.2020 Lecture 2: The Python Data Science Stack - Task 1: Release
07.05.2020 No lecture
14.05.2020 Task 1: Intermediate meeting
21.05.2020 No lecture // Task 1 report submission
28.05.2020 Lecture 3: Advanced Algorithms for Data Science // Task 2: release
04.06.2020 No lecture
11.06.2020 Lecture 4: Evaluation and Tuning of Models // Task 2 report submission // Task 3: release
18.06.2020 No lecture
25.06.2020 No lecture
02.07.2020 Task 3: Intermediate meeting I
09.07.2020 No lecture
16.07.2020 Task 3: Intermediate meeting II (FlexNow Registration Deadline)
23.07.2020 No lecture
30.07.2020 (online) Task 3: Presentations (Final Presentation)
20.08.2020 Final Report deadline (Including report and code)
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