Python Training

Python for Geospatial Analysis

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

Prerequisites

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 spatial science and scientific computing more broadly.

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

Day 1: Introduction to Python

Day 1 covers the basics of using the Python language and standard library, with a focus on scientific and engineering applications, including tips and tricks for making this easy.

  • Why use 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: fetching and ranking real-time data from a web API
  • Raising and handling exeptions

Day 2: Handling, Analysing, and Presenting Data in Python

Day 2 gives a comprehensive introduction to reading and writing the most important data formats in science and engineering and how to analyse and visualise data easily.

  • Reading and writing essential data formats: CSV, Excel, SQL databases
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarisation with “group by” operations; pivot tables
  • Time-series analysis: parsing dates, resampling
  • Visualisation and statistical graphics with Seaborn / Altair
  • Worked example: creating automated reports with Pandas and nbconvert

Day 3: Essentials of Scientific Computing with Python

Day 3 teaches you how to use Python for numerical and scientific computing. It covers array and matrix manipulation and an overview of available scientific routines, including an introduction to statistical modelling:

  • Introduction to manipulating vectors and matrices with NumPy
  • Hands-on tour of SciPy and related packages for scientific data manipulation: clustering, interpolation, optimisation, dense & sparse linear algebra, signal processing, image processing, unit conversions
  • Statistics in Python: modelling, confidence intervals, hypothesis testing, regression, Monte Carlo simulation, with scientific applications

Day 4: Real-world programming in Python

Day 4 focuses on techniques for creating larger codebases in teams and scaling up from small datasets to large ones that are too big for memory or too slow for one computer to process. This includes an introduction to machine learning for automatically inferring models from large datasets:

  • Best practices: creating scripts, modules and packages; IDEs; revision control; Python idioms and style
  • Speeding up code by 4x to 10,000x: profiling, vectorization, JIT compilation, parallel computing with Dask
  • Machine learning with scikit-learn: classification, non-linear regression, clustering

Day 5: Spatial analysis in Python

Day 5 will provide a 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
  • Reading and writing rasters with Rasterio
  • Working with NetCDF data with xarray
  • Projections with Geopandas, pyproj and shapely
  • Creating beautiful maps with Cartopy and overlaying statistical data
  • Introduction to vector and raster image analysis with PySAL and SciPy

Supplemental materials

We will supply you with printed course notes, cheat sheets, and a USB stick containing kitchen-sink Python installers for multiple platforms, solutions to the programming exercises, several written tutorials, and reference documentation on Python and the third-party packages covered in the course.

Other information

Personal help:
Your trainer(s) will be available after the course each day for you to ask any one-on-one questions you like — whether about the course material and exercises or about specific problems you face in your work and how to use Python to solve them.
Food and drink:
We will provide lunch, morning and afternoon tea, and drinks.
Timing:
The course will run from 9:00 to roughly 17:00 each day, with a breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.

Upcoming Courses

Melbourne

Python for Geospatial Analysis:
21 Oct – 25 Oct 2019

Cliftons, Level 1, 440 Collins Street, Melbourne CBD

Brochure Book Now

Canberra

Python for Geospatial Analysis:
02 Dec – 06 Dec 2019

Cliftons, Level 2, 10 Moore Street, Canberra CBD

Brochure Book Now

Other Locations

Python for Geospatial Analysis:
Dates TBA

Be notified

Testimonials

“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