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.
By the end of the course, you will have all the knowledge you need to start programming competently in Python for scientific and engineering applications, with a focus on geospatial applications. You will know what's available with Python and how to use its powerful data types and amazing libraries to write clean, efficient code. You will have had experience with using Python for various scientific data manipulation tasks and solving a range of analytical tasks, including easily creating beautiful plots, performing Monte Carlo simulations, constructing statistical models, regression, optimisation, analysing images, time-series data, geospatial data, and plotting statistical data on maps.
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 other languages
How to install a complete Python development environment (with plotting etc.)
The Jupyter notebook and shell for rapid prototyping
Python syntax and concepts
Essential data types, tips and tricks
Modules and packages; handling exceptions
Tour of the amazing standard library
Worked example: fetching and ranking real-time temperature data for global cities
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
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,
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)
Bring Your Own Data
We also encourage you to supply your own data sets for us to use in the course if you wish.
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.
- 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.
- 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.
10/07/2017 at Melbourne
We also offer custom courses on-site for teams within organisations. These are particularly appropriate if your team uses particular tools or data sources in its workflow. Please contact us to discuss your requirements.