These notes intend to provide the Python basics that most undergrad and grad students need for the analysis of their data. No derivations of formulas are presented, and only the absolutely necessary background. Instead, hands-on demonstrations of how to impelement the analysis in Python are given.
In addition to the sourcecode here on github, these notes are available
- in a compiled form ( http://work.thaslwanter.at/Stats/html/ )
- as PDF ( http://work.thaslwanter.at/Stats/StatsIntro.pdf )
Python rocks - also for statistical tasks!
I have changed the copyright, so this work no longer has a CC-licence!
The last few month I have spent a lot of time thinking about this, and briefly want to let you know why I have made this decision:
- Over the 2 years that this repository has been online, and I have had only one other very small contribution, and three emails providing feedback on the content.
- When I wanted to produce an open-source book, I realized that this is not as easy as I thought it would be. In addition, I would have to pay a significant amount of money to any publisher. Also even big open-source gurus, like Wes McKinney, have published their books through classical publishers.
- Python for simple statistical applications is not very well established right now. And I truly believe that going through a standard publisher will provide a wider dissemination of Python for simple statistical applications, thereby increasing the Python community.
Feel free to send me feedback, even if is critical! This was not an easy decision for me, and i would like to hear the opinion of the community.