Advanced Practical Course Data Science (Summer 2021)

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:	Friday 16:00-18:00
Place:	2.101(online)
UniVZ	[1]

Announcement

20.10.2020 Our first lecture will be on 06.11.2020 through the Meeings tool on StudIP

30.09.2020 Our course in this semeter will be online

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, when and where are the lectures, how is presentation arranged). 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?
06.11.2020	Lecture 1: Introduction & The Data Science Pipeline
13.11.2020	Lecture 2: The Python Data Science Stack - Task 1: Release
20.11.2020	No lecture
27.11.2020	Task 1: Intermediate meeting
04.12.2020	// Task 1 report submission //Task 2: release
11.12.2020	No lecture
18.12.2020	Lecture 3: Advanced Algorithms for Data Science
25.12.2020	No lecture // Task 2 report submission
01.01.2021	No lecture (holidays）
08.01.2021	No lecture (holidays)
15.01.2021	Lecture 4: Evaluation and Tuning of Models // Task 3: release
22.01.2021	No lecture
29.01.2021	Task 3: Intermediate meeting
05.02.2021	Final Presentation (TBD)
12.02.2021	Final Presentation (TBD)
31.03.2021	Final Report deadline (Including report and code)