Spatial Data Science for Professionals 2017

March 29 - March 31, 2017

124 Mulford Hall

University of California, Berkeley

3 day technical bootcamp
$1,500 with 10 spots reserved at $1,200 for academia/non-profit

We live in a world where the importance of spatial data is ever increasing. Many of the societal challenges we face today — fire response, energy distribution, efficient resource allocation, land use, food scarcity, invasive species, climate change, privacy and safety — are associated with big spatial data. Addressing these challenges will require trained analysts fluent in:

  • Big Data Tools: integrating disparate data, from aircraft, satellites, mobile phones, historic collections, public records, the internet;
  • Geospatial Analysis: using easily available and open technology for robust data analysis, sharing, and publication;
  • Visualization: understanding and applying core spatial analysis methods;and applying visualization tools to communicate with project managers, policy-makers, scientists and the public.

Join us at the Geospatial Innovation Facility (GIF) to expand your geospatial toolkit and explore new web-enhanced ways to visualize and communicated your data!

See Spatial Data Science 2016

Your Takeaways

This 3-day intensive training will jump start your geospatial analysis and give you the background and resources you need to begin implementing open source and cloud/web-based techniques into your own spatial data projects.

Upon successful completion of the training, participants will receive a Certificate of Completion from the College of Natural Resources at the University of California, Berkeley.


This hands-on training is limited to 30 participants. The Spatial Data Science Bootcamp team is taking a holistic approach to reviewing the application, which is not solely based on the applicant's current skill level or experience. Applicants' goals and interests are also important. It is our goal to choose the applicants who would benefit most from the bootcamp.

Attendees are expected to already have some intermediate to advanced GIS skills and some basic experience with a programming language.

What's Included:

Course fees cover instruction, a digital handbook, breakfast, morning and afternoon refreshments, and one evening networking reception. For the hands-on tutorials, participants will use computers hosted at the GIF and are not required to bring laptops.

The digital handbook will be used throughout the training and beyond into your own workplace. The handbook will include: presentation materials; hands-on exercises; sample code from each module; and a wealth of resources for learning more about each element of the spatial data pipeline. This guide can be used as the base for writing your own code and as a valuable resource for solving your organization's data wrangling, analysis, visualization, and publication challenges.

Course fees do not cover transportation and lodging. See our logistics page for more information about hotels and transportation.

Who should attend

The Spatial Data Science Bootcamp is designed for the geospatial professional who wants to stay current in today’s dynamic world of spatial data. GIS professionals, spatial data researchers, scientists, and students will learn about the cutting edge of spatial data science and get hands-on practice using today’s latest open source analysis, visualization and database tools. This intensive hands-on training will help you to expand your geospatial toolkit from the desktop to new open source and cloud/web-based technologies.


  Green Valley International 




As technology is rapidly changing, the goal is not to teach a specific suite of tools but rather to familiarize participants with the modern spatial data workflow and explore open source and cloud/web-based options for spatial data management, analysis, visualization and publication. You will learn how and when to implement a wide range of modern tools that are currently in use and under development by leading Bay Area mapping and geospatial companies, as well as explore a set of repeatable and testable workflows for spatial data using common standard programming practices. Finally, you will learn other technical options that you can call upon in your day-to-day workflows.

See Spatial Data Science 2016