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Courses
Here we curate a collection of skill-building courses that we can recommend.
One of the first major decisions to make when getting into data analysis is about which language to choose. This infographic by Datacamp provides a helpful overview about R and Python and might be a good starting point to inform yourself.
In terms of courses, we also recommend the courses by Datacamp, as they offer free class accounts that can be applied for by academic instructors.
Some courses we recommend are: (excerpt)
Course | Category | Level | Notes |
---|---|---|---|
Introduction to Shell | Shell | Beginner | |
Introduction to Bash Scripting | Shell | Beginner to Intermediate | |
Introduction to Data Visualization with Plotly in Python | Python | Beginner | |
Building Dashboards with Dash and Plotly | Python | Intermediate |
We also have made good experiences with Codecademy, especially for other programming languages and use cases beyond data analysis, however, their classroom program is limited to the USA at the time of writing (February 2021).
Furthermore, Kaggle is a reliable and free of charge platform for learning Python, SQL and the basics of machine and deep learning. Further skills such as data visualization with seaborn and data manipulation with pandas, among others, can also be learned, applied, and solidified through challenges and competitions.
This course will teach you, step by step, how to set up your Twitter developer account and how to use the Twitter Academic API (v2). It contains 8 modules including a module dedicated to a lab environment for practical experiences using R or Python. It also covers the identification of suitable endpoints for your use-cases, how to build better search queries, and storage of Twitter data using R or Python.
Test-Driven Development with Python (2nd ed.) - Obey the Testing Goat: Using Django, Selenium, and JavaScript by Harry Percival
(CC-licensed and free to read online)
Introduces the reader to the basics of test-driven development and Django through the practical application of setting up a web app. The first part provides enough information to understand the basics of functional and unit tests and should be prioritized.
A hands-on course on aligning language models for your unique needs. It’s beginner-friendly, with minimal requirements:
- Runs on most local machines
- Minimal GPU requirements
- No paid services needed
This Wiki is curated by the Social Media Observatory, which is hosted by the Leibniz Institute for Media Research | Hans-Bredow-Institut and supported by the Research Institute Social Cohesion. A pretty version can be found here.
- Instagram-Tools
- Twitter-Tools
- Wikipedia-Tools
- Facebook-Tools
- YouTube-Tools
- Telegram-Tools
- Smaller Platform Tools
- Cross-Platform Tools
- General News Scrapers
- Secure Storage and Archiving
- Data Anonymization Tools
- Data Publishing