This is a course for GIS analysts, scientists, engineers, surveyors, and other data analysts with prior experience working with spatial data in Python.
Completion of the Python Charmers Python for Geospatial Analysis course and six months Python programming experience.
This course will let you take your spatial analysis further using powerful methods to discover new information using location.
At the end of the course you will understand scientifically and statistically grounded methods of geospatial analysis that you can use to aid in your interpretation of real-world data and to solve real-world problems.
You will learn fundamentals of network analysis through automation of common geospatial tasks, the basics of dealing with network data, as well as more advanced spatial statistics such as measures of spatial autocorrelation and multi-dimensional interpolation and regression. You will also learn techniques for dealing with very large datasets through parallel processing and visualization.
Day 1 of the course will revise core concepts, introduce network analysis, and look at common geospatial analysis tasks to automate your workflow and analysis:
Day 2 looks at extending this analysis. You will learn how to perform spatial autoregression tests for spatial dependence, work with point pattern datasets for optimisation and to interpolate surfaces, before finally techniques for managing and visualising large spatial datasets:
We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.
Live, online, interactive instructor-led training with video streaming (via Zoom) and a cloud server for coding during the course.
Some sessions are jointly offered both online and face-to-face; see locations and dates below.
You will have access to all the course materials via the cloud server: PDF of the course notes, Jupyter notebooks, sample datasets.
We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, and reference documentation on Python and the third-party packages covered in the course.
Computer (online participants):
Hardware: we recommend ≥ 8 GB of RAM, a headset mic, webcam. Preferably also a quiet room and multiple screens.
Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge) and Zoom. You do not need to install Python on your own computer.
Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).
The course will run from 9:00 to roughly 17:00 each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.
Certificate of completion:
We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.
For face-to-face participants (where applicable):
Venue: modern computer-based training facilities (CBD venues)
Computer: An internet-connected computer will be provided for you.
Food and drink: We will provide lunch, morning and afternoon tea, and drinks.