Geography 385: Python Primer
Note that this is an optional exercise. If you successfully complete the Python primer, you will earn 3 tokens.
The SDSU Course Catalogue entry for our course reads:
Analysis of spatially distributed data, including computer applications. Spatial sampling, descriptive statistics for areal data, inferential statistics, and the use of maps in data analysis.
To achieve this, we will adopt a computational approach based on the Python programming language. As the course progresses, we will introduce the elements of Python necessary for spatial data analysis. However, this is a course in spatial data analysis, not Python programming. The course assumes no previous experience with Python (or programming), what we cover will be self-contained and start from basic principles. To bolster your understanding of Python, I suggest taking the Python Primer.1 2
Instructions
- Go to http://www.codeacademy.com and create a free account, or sign in with a social media or GitHub account.
- Take the self-paced entry-level minicourse on Python 2.3
- The minicourse has 12 units, each with 1-2 lessons to be done in a browser. Complete the first 9 units of the course, up to and including “Exam Statistics.”
Completing this primer should take between 6-8 hours. You should spread out your effort in 30-minute chunks over a few weeks.
Submission (Due: September 23, 3:30 PM)
When you have completed the nine units, take a screenshot that shows your name on the screen as well as all the checks. Upload the screenshot to Canvas.
Footnotes
The Python Primer is inspired by and modeled after the work of Robert Talbert.↩︎
If you are interested in further exploring Python for geography, an excellent course is Geog 383.↩︎
Do not sign up for the Python 3 course as it is not free. We will cover the main differences between Python 2 and Python 3 in the studio sessions.↩︎