Python Training

Python for Geospatial Analysis

This is a course for scientists, engineers, and analysts working with geospatial data sets.


Familiarity with spatial analysis concepts is assumed. No prior experience with programming (in any language) is assumed.

Expected Outcomes

Spatial data is ubiquitous and location analytics are more important than ever. A well drawn map is not only beautiful to look at, but can change how you see the world. In the last 10 years Python has become the go-to language for scientific computing and spatial science.

By the end of the course, you will have all the knowledge you need to start solving a wide range of analytical problems in Python for scientific and geospatial applications.

You will learn how to analyze raster and vector geospatial datasets; perform Monte Carlo simulations; construct regressions and other statistical models; perform optimization; analyze images and time-series; and create beautiful statistical charts and maps.

You will learn to work with and analyze general, scientific, and geospatial datasets in many useful formats, including CSV, Excel, SQL, shapefiles, KML, and spatial formats (raster and vector).

You will also learn about the elegance and power of the Python language and the breadth of its amazing ecosystem of powerful packages.

Course Syllabus

Session 1: Python Basics

Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:

  • Why Python? What’s possible?
  • The Jupyter notebook for rapid prototyping
  • Modules and packages
  • Python concepts: an introduction through examples
  • Essential data types: strings, tuples, lists, dicts
  • Worked example: retrieving real-time data from a REST web API
  • Raising and handling exceptions

Session 2: Handling, Analyzing, and Presenting Data in Python

Python offers amazingly productive tools like Pandas for working with different kinds of data. Day 2 gives a thorough introduction to analyzing and visualizing data easily:

  • Reading and writing essential data formats: CSV, Excel, SQL, time-series (others on request)
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarization with “group by” operations; pivot tables
  • Visualization and statistical graphics with Plotly Express
  • Worked example: creating automated reports
  • Creating interactive dashboards with Streamlit

Session 3: Further Data Analytics

Day 3 shows you in-depth how to manipulate time-series and matrix/vector data. It then gives examples of Monte Carlo simulation, interpolation, linear regression, and outlier / anomaly detection:

  • Time-series analysis: parsing dates; resampling; interpolation
  • Introduction to NumPy for manipulating vector and matrix data:  data types, powerful indexing, reshaping, ufuncs
  • Monte Carlo simulation and applications
  • Interpolation and linear regression
  • Outlier and anomaly detection with pyod; applications to time-series

Session 4: Spatial Analysis in Python

This day will provide a comprehensive tutorial in working with geospatial data using Python. It will cover spatial data access, spatial analysis, and visualizing the results on a map.

  • Reading & writing vector data with Geopandas and GDAL/OGR
  • Projections with Geopandas and pyproj
  • Introduction to vector analysis with Geopandas
  • Reading and writing with common raster formats (including NetCDF)
  • Introduction to raster analysis with xarray
  • Creating beautiful maps and overlaying statistical data
  • Introduction to raster and vector image analysis with scikit-image and Geopandas

Session 5: Scientific Computing with Python

Day 5 teaches you specialized tools in Python for scientific and engineering computing. It gives you a comprehensive introduction to SciPy and the broader package ecosystem. It then teaches you how to profile and speed up slow numerical code and how to parallelize code for large datasets across several cores/processors or distribute them across a cluster.

Tour of SciPy and related packages, with fancy demos:

  • handling scientific data formats
  • handling scientific units and uncertainities
  • dense & sparse linear algebra
  • clustering with scikit-learn, with spatial applications
  • dimensionality reduction
  • statistical modelling and density estimation
  • optimization and curve fitting

On request:

  • integration / ODEs
  • signal & image processing
  • speeding up slow code by 4x to 10,000x: profiling, vectorization, JIT compilation with numba
  • parallel and distributed computing with dask

Personal help

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.

Other information


Courses are conducted online via live video meeting and using Python Charmers' cloud notebook server for sharing code with the trainer(s).


Hardware: we recommend ≥ 8 GB of RAM and a webcam. Preferably also multiple screens and a quiet room (or headset mic).

Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge); and Zoom.


Most courses will run from 9:00 to roughly 17:00 (AEST) 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.


You will have access to all the course materials via the cloud server. We will also mail you cheat sheets and a USB stick with all the materials for reference.


“The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!”

- Marius Roman

“Great course. Enjoyed it and will follow up with some practical implementation of some of the work.”

- Adam Grace

“One of the best training courses I've been on.”

- David Scurrah

“I loved it. Ed was inspiring.”

- Onoriode Coast

“Really impressed by Python's capability and excited to use as alternative to MatLab, as is free and better supported.”

- Carsten Hofmann

“Very comprehensive intro to every aspect of python. Highly qualified trainer. Beyond my expectation on every aspect.”

- Baichuan Sun

“The VM setup and USB is great. Ed is an excellent instructor - he presents well and welcomes any questions. He is clearly a super smart guy who has a great grasp on what he is teaching - able to just prototype on the fly and the course overall really opened my eyes to python.”

- Jack Hendy

“Course content was well presented and easily digested. Practical exercises were an essential part of the course – a good ratio of lecture/play was achieved. Well done Ed and Henry!”

- Steve Zegelin

“Simply awesome!!”

- James Park

“Both Ed and Henry presented well…. The course structure was adjusted to suit the participants quickly and easily.”

- Jenet Austin

“Excellent training course, excellently presented. Perhaps the best that I have had in the area of IT / programming.”

- George Grozev

“One of the best programming courses I have attended - thanks!”

- Giant Billen

“This course has shown me how I could have done the work I was doing just last week 10x more efficiently in Python.”

- Maruf Rahman

“It was a pleasure ... Shared feedback from all involved was that it’s been one of the most beneficial and well delivered training courses we’ve been a part of.”

- Dylan Matthews

“Was the most fulfilling and rewarding class I have taken since "general relativity" at uni. Was extremely well run. Excellent all round!”

- Dr Millicent Maier

“Very impressed with the course, delivery. And depth of knowledge of Ed and Henry. Far exceeded my expectations and has greatly improved my core skills as well as inspired so many new ideas for my current work / projects. Thank you!”

- Kelsey Druken