Resources

Of course, this course can not cover everything, and it would be presumptous to assume so. Much of what we cover here has been learnt thanks to the countless excellent books, tutorials and examples to programming in Python that are out there in the great widths of the Internet. Please find, below, a list of good places to look for further inspiration.

Books

There are no required textbooks for this course. That said, these are textbooks we would recommend if you want to deepen your knowledge further:

• Books related to data analysis in Python:

  • Zelle (2017) Python Programming: An Introduction to Computer Science. Available at the Kumpula Campus library.

  • McKinney (2017) Python for Data Analysis: Data wrangling with Pandas, NumPy and iPython. Available as an e-book.

• Books related to spatial data analysis in Python:

  • Lawhead (2015) Learning Geospatial Analysis with Python: An effective guide to geographic information systems and remote sensing analysis using Python 3, Second edition. Packt Publishing.

  • Westra (2016) Python Geospatial Development.

  • Zandbergen (2013) Python Scripting for ArcGIS. Available at Helsinki University library

  • Diener (2015) Python Geospatial Analysis Cookbook.

Python tutorials

• Codecademy’s Learn to program in Python

• Software Carpentry’s programming in Python

Git and 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 (most probably) not 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