In addition to our course, there are countless of excellent books, tutorials and examples related to programming in Python. Here we list some good places to look for further information.
There are no required textbooks for this course. This course uses a wide range of sources for course information and the main textbooks are given below.
Books related to data analysis in Python:
- Zelle, J. (2017) Python Programming: An Introduction to Computer Science, Third edition. Franklin, Beedle & Associates. Copies of this book are available in the Kumpula Campus library.
- McKinney, W. (2017) Python for Data Analysis: Data wrangling with Pandas, NumPy and iPython, Second edition. O´Reilly Media, Incorporated. Available as Ebook in here.
- Lawhead, J. (2015) Learning Geospatial Analysis with Python: An effective guide to geographic information systems and remote sensing analysis using Python 3, Second edition. Packt Publishing.
Books related to spatial data analysis in Python:
- Westra, E. (2016) Python Geospatial Development: Develop sophisticated mapping applications from scratch using Python 3 tools for geospatial development, Third edition. Packt Publishing.
- Zandbergen, P. (2013) Python Scripting for ArcGIS, Alternate edition. ESRI press. (Available from the library
- Diener, M. (2015) Python Geospatial Analysis Cookbook: Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python. Packt Publishing.
Git + Github tutorials¶
- Online “Try-Git” tutorial (learn Git in your browser)
- Git simple guide (“no deep shit”) tutorial
- Software Carpentry’s Git novice tutorial
- Git official documentation
- Screencast series in Youtube for learning GitHub
- Tutorial on few extra features of GitHub not (most probably) covered in this course (e.g. branch, pull-request, merge)
- A TechCrunch article about ‘What is GitHub Anyway?’
- A list of resources for learning Git and GitHub