Python Training

Python for Geospatial Analysis

This is a course for scientists and researchers interested in using Python for solving computational problems and spatial analysis problems that arise in daily work.

Prerequisites

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

Expected Outcomes

By the end of the course, you will have all the knowledge you need to start programming competently in Python. You will know what's available with Python, how to structure your code and use Python's data structures competently, and how to find further resources for learning more. You will have had experience with using Python for various scripting and scientific data manipulation tasks, with a focus on analysing spatial data.

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? Python versus Java, C, C++, Matlab, R, IDL, ...
  • How to install a complete Python environment (with plotting etc.)
  • Python syntax and concepts; file IO
  • Useful data structures
  • Modularity and packaging: functions, classes, modules, packages
  • Practical tips and tricks (including rapid prototyping with the IPython shell)
  • Tour of the amazing standard library (including handling dates, CSV files, zip files)

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
  • Visualisation and statistical graphics with Seaborn
  • Indexing and selecting data in Pandas
  • Data fusion: joining & merging datasets
  • Summarisation with “group by” operations; pivot tables
  • Time-series analysis: parsing dates, resampling
  • Worked example: creating automated reports with Pandas and nbconvert </li>

Day 3: Essentials of Scientific Computing with Python

Day 3 teaches the use of Python for numerical and scientific computing. It covers array and matrix manipulation, working with labelled and tabular data, an overview of available scientific routines, and creating simple but beautiful 2D plots, with the packages NumPy, SciPy, and Matplotlib.

  • Introduction to numerical data manipulation with NumPy
  • Statistics in Python: modelling, confidence intervals, hypothesis testing, regression, Monte Carlo
    simulation, with scientific applications
  • Tour of SciPy and related packages for scientific data manipulation, with fancy demos: clustering,
    interpolation, optimisation, dense & sparse linear algebra, signal processing, image processing,
    unit conversions
  • 2D plotting with Matplotlib
  • Demos: interactive and 3D plotting with Plotly

Day 4: Real-world programming in Python

Day 4 focuses on techniques for creating larger codebases in teams, interfacing Python with other data sources, scaling from small datasets and small problems to realistic ones that may be too big for memory or too slow for one computer to process.

  • Integrated development environments; tools for benchmarking and profiling code
  • Finding and installing packages with conda and pip
  • Writing maintainable code with classes
  • Working in teams: creating modules and packages; Python idioms and style
  • Interfacing Python with other languages: Excel, R, C/C++, Fortran, Matlab (topics on request)
  • Interfacing with NetCDF and/or HDF5 data (on request)
  • Introduction to parallel computing with Dask

Day 5: Spatial analysis in Python

Maps are a powerful tool for data visualisation. 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. This day will provide a tutorial in working with geospatial data using Python. It will cover spatial data access, spatial analysis, and visualising 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
  • Introduction to network analysis with NetworkX (on request)

Supplemental materials

We will supply you with printed course notes and a USB stick containing a complete Python environment based on VirtualBox. This saves time in the course and allows us to focus on using Python rather than installing it. The USB stick also contains 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 an hour for lunch and 15 minutes each for morning and afternoon tea.

Upcoming Courses

Melbourne

Python for Geospatial Analysis:
04 Dec – 08 Dec 2017

50 Queen Street, Melbourne CBD

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Other Locations

Python for Geospatial Analysis:
Dates TBA

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Upcoming Public Courses

Melbourne

Python for Scientists & Engineers:
04 Dec – 07 Dec 2017

50 Queen Street, Melbourne CBD

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Melbourne

Introduction to Python:
04 Dec – 05 Dec 2017

50 Queen Street, Melbourne CBD

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Melbourne

Python for Geospatial Analysis:
04 Dec – 08 Dec 2017

50 Queen Street, Melbourne CBD

View Details Brochure Book Now

Sydney

Introduction to Python:
12 Dec – 13 Dec 2017

Level 4, 60 Clarence Street, Sydney CBD

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Sydney

Introduction to Python:
29 Jan – 30 Jan 2018

Level 4, 60 Clarence Street, Sydney CBD

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Sydney

Python for Predictive Data Analytics:
29 Jan – 01 Feb 2018

60 Clarence Street, Sydney CBD

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Melbourne

Python for Predictive Data Analytics:
26 Mar – 29 Mar 2018

50 Queen Street, Melbourne CBD

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Melbourne

Introduction to Python:
26 Mar – 27 Mar 2018

Ground Floor, 50 Queen Street, Melbourne CBD

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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

“Instructor was superb - very impressive. I really enjoyed the course - thanks!”

- Steven Edgar

“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