100
edits
Advanced Practical Course Data Science (Winter 2021/2022): Difference between revisions
Jump to navigation
Jump to search
Advanced Practical Course Data Science (Winter 2021/2022) (view source)
Revision as of 09:31, 28 July 2021
, 28 July 2021no edit summary
(Created page with "{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} == Details == {{CourseDetails |credits=180...") |
No edit summary |
||
| Line 1: | Line 1: | ||
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} | |||
== Details == | |||
{{CourseDetails | |||
|credits=180h, 6 ECTS | |||
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke | |||
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu]; [http://www.net.informatik.uni-goettingen.de/?q=people/fabian-wölk MSc. Fabian Wölk] | |||
|ta=[http://www.net.informatik.uni-goettingen.de/?q=people/jiaquan-zhang MSc. Jiaquan Zhang] | |||
|time=Thursday 16:00-18:00 | |||
|place=2.101(online) | |||
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=267540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung] | |||
}} | |||
==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., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 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== | |||
{| {{Prettytable|width=}} | |||
|- | |||
|{{Hl2}} |'''When?''' | |||
|{{Hl2}} |'''What?''' | |||
|- | |||
| align="right" | 15.04.2021 | |||
| Lecture 1: Introduction & The Data Science Pipeline | |||
|- | |||
| align="right" | 22.04.2021 | |||
| No lecture (Girls Day) | |||
|- | |||
| align="right" | 29.04.2021 | |||
| Lecture 2: The Python Data Science Stack - Task 1: Release | |||
|- | |||
| align="right" | 06.05.2021 | |||
| Task 1: Intermediate meeting | |||
|- | |||
| align="right" | 13.05.2021 | |||
| No lecture (Ascension Day) | |||
|- | |||
| align="right" | 20.05.2021 | |||
| Lecture 3: Advanced Algorithms for Data Science // Task 1 report submission //Task 2: release | |||
|- | |||
| align="right" | 27.05.2021 | |||
| Lecture 4: Evaluation and Tuning of Models | |||
|- | |||
| align="right" | 06.03.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 10.06.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 17.06.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 24.06.2021 | |||
| // Task 3: release // Task 2 report submission | |||
|- | |||
| align="right" | 01.07.2021 | |||
| No lecture | |||
|- | |||
| align="right" | 08.07.2021 | |||
| Task 3: Intermediate meeting | |||
|- | |||
| align="right" | 15.07.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 22.07.2021 | |||
| Final Presentation (TBD) | |||
|- | |||
| align="right" | 31.09.2021 | |||
| Final Report deadline (Including report and code) | |||
|- | |||
|} | |||
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} | {{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}} | ||