[Online] GGS-Workshop: Web Data Science: Scraping and Analyzing the Web with Python
- https://www.uni-giessen.de/de/fbz/zentren/ggs/veranstaltungen/index_html/wise-2021_22/online-ggs-workshop-web-data-science-scraping-and-analyzing-the-web-with-python
- [Online] GGS-Workshop: Web Data Science: Scraping and Analyzing the Web with Python
- 2021-10-04T09:00:00+02:00
- 2021-11-08T17:00:00+01:00
04.10.2021 09:00 bis 08.11.2021 17:00 (Europe/Berlin / UTC200)
Online [Tool wird später kommuniziert]
Instructor: | Dr Jan Kinne | |
Dates: |
October 4, 11, 18 & 25 and November 1, 2021 Lectures: 9.00 am - 1.00 pm & November 8, 2021 Final presentations: 9.00 am - 1.00 pm |
|
Max. participants: |
20 | |
Course language: | English | |
Registration Deadline: |
September 22, 2021 |
|
ECTS: | 6 |
Objectives
- After this workshop, participants understand Python as a data science tool. They have learned Mapping, Web Scraping, and Text Mining with Python.
- The participants have a basic understanding of Web Data Science and an overview of relevant tools
- The participants have leaned how to present individual data analysis projects, based on what they have learned.
Content & Methods
Day 1: Introduction to Python I (optional for advanced Python users)
Day 2: Introduction to Python II (optional for advanced Python users)
Day 3: Mapping
Day 4: Web Scraping
Day 5: Text Mining
Day 6: Project Presentation
The course will be held completely online. Each day there will first be a short introductory presentation by the lecturer. Afterwards, we will work together on a Data Science workflow using a pre-built and fully commented Jupyter notebook. These notebooks are prepared in such a way that they can be worked through again by the participants on their own after the course and parts of the workflow can be used for their own Data Science project. The participants will start such a project based on a self selected dataset on Day 1. Over the progression of the course, participants will explore their chosen dataset using the methods learned. Grades for the course are based on projects presented and submitted on the final day.
Further information will follow soon in the syllabus "Web Data Science".