Advanced Practical Course Data Science (Summer 2020)

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

Announcement

Due to the recent recommendations in the context of Covid-19, we have to defer the start of the lectures of this course to 23rd April 2020. Currently, this course is scheduled in a purely online, non-face-to-face way. We plan to use some tools and platforms, e.g., zoom or DFNconf, see: https://www.uni-goettingen.de/en/622774.html. Please register into studIP in advance. The registration deadline is at 23:59 pm on 22nd April 2020. I will announce which tool will be used before our lecture start.  

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	Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission // Task 2: release
28.05.2020	No lecture
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	No lecture
09.07.2020	Task 3: Intermediate meeting I
16.07.2020	No lecture
23.07.2020	No lecture
30.07.2020	Task 3: Intermediate meeting II
TBD	Task 3: Presentations (Final Presentation)
09.31.2020	Final Report deadline (Including report and code)