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.
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.
Day 1 covers how to use Python for basic scripting and automation tasks, including tips and tricks for making this easy:
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:
Day 3 shows you how to manipulate time-series and matrix/vector data. It then describes simulation methods and walks you through using powerful methods of inference and modelling, clustering and outier detection:
Day 4 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.
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 aross a cluster.
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.
Courses are conducted online via video meeting 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.
Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).
Most courses will run from 9:00 to roughly 17:00 (AEDT) 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 send you a bound copy of the course notes, cheat sheets, and a USB stick containing the materials, exercise solutions, and further resources.