We run a series of summer/winter short courses focused on advancing students' skill-sets in the latest geospatial techniques, topics, and technologies.

In Winter 2019, we are offering:

  • GEOG198A:Introduction to GIS in the Era of Big Data  (1 credit; course schedule is here)
  • GEOG788X:Introduction to GIS in the Era of Big Data  (1 credit; course schedule is here)
  • GEOG498A:Introduction to Python for Data Science (1 credit; course schedule is here)


In Summer 2018, we offered:

  • GEOG198A:Introduction to GIS in the Era of Big Data  (1 credit; course schedule is here)

In the past Winter 2018, we offered:

  • GEOG788O:Processing of Geospatial Data Using Open Source Tools  (1 credit; course schedule is here; register at OES)
  • GEOG788C: GIS and Hadoop for Big Data (1 credit; details are included in the flyer; course schedule is here; register at OES)

GEOG788O:Processing of Geospatial Data Using Open Source Tools 

The goal of this 1-cr course is to teach students to use various open source tools for handling spatial information on the web.   Students will learn to use open source GIS tools like QGIS, GeoServer and PostGIS to collect, store, analyze, serve and visualize spatial data on the web.

This course will include the following topics:

  1. Introduce students to QGIS, an actively maintained open source GIS platform for collecting, storing, and analyzing spatial information.
  2. Understand the backend of web mapping services like Google Map, Bing Map etc. Use open source GIS server to build our own Google Map-like web mapping services, including data services (WFS), visualization services (WMS), and processing services(WPS).  
  3. Handling spatial data on the web (e.g., OpenStreetMap Data) use open source GIS tools.

Students in this course will develop an appreciation for the principle of both the front-end and back-end of spatial data processing on the web. Previous exposure to GIS environment and basic concepts of web server will be helpful for learning in this course. 

GEOG788C: GIS and Hadoop for Big Data

This course is tailored for students and professionals who are newcomers to spatial data sciences and high-performance computing, especially those who are interested in Hadoop implementation in processing aspatial and spatial data.

 

Current UMD graduate and undergraduate students, Students from other universities and colleges, Professionals, and Life-long learners are welcome to enroll. Undergraduates may contact Dr. Ruibo Han (ruibo [at] umd.edu) for permission. 

Students will also get opportunities to work hands-on with these technologies using our state-of-the-art facilities, including or high-performance computing cluster and connections to commercial cloud computing providers.

Tuition Remission

Don’t forget to utilize your tuition remission to attend winter courses. E.g. Full-time Graduate Assistants are eligible for 4 credits of tuition remission for Winter Term, and Part-time Graduate Assistants are eligible for 2 credits. More info. is available at https://uhr.umd.edu/benefits/tuition-remission


The skill-set course sequence at the UMD Center for Geospatial Information Science is designed to provide a comprehensive overview of the current state-of-the-art in geospatial information science (GIS) and computational social science (CSS), through intensive training over a short time frame. The skill-set courses provide advanced training in key aspects of GIS and CSS, alongside hands-on lab-based exercises across a diversity of devices, software libraries, exercises, and data environments that emphasize “learning by doing”.

All courses are taught by faculties and staffs in the School of Geographical Sciences and Center for Geospatial Information Science at the University of Maryland, who have extensive experience in building and applying geographic information systems for use in research and professional environments.

In Winter 2017, we offered courses on:

  • GEOG788C: GIS and Hadoop (1 credit; details are included in the flyer; course schedule is here; register at OES)

This course is tailored for students and professionals who are newcomers to spatial data sciences and high-performance computing, especially those who are interested in Hadoop implementation in processing aspatial and spatial data.


We also offer workshops on various topics to students, faculty, and the general public. The schedule of workshops is tailored to f the t audience, and the topics are customized to meet demanding needs, but are not necessarily limited to GIS.

In Winter 2017 we hosted:

Python Workshop For BSOS Faculty

January 9-12, 2017

Topic: Python, known for its simplicity, power, and efficiency, has been increasingly used in teaching and researching. This workshop introduces conceptual and practical aspects of using Python for beginners in programming. The main focus is on developing a solid understanding of basic programming techniques of using Python, including variables, looping, conditional statements, nesting, math, strings, and other concepts. The examples and problems will be drawn from diverse areas such as text processing, simple graphics creation and image manipulation, HTML and web programming, spatial data processing, etc. 

Dates and Times: January 9, 10, 11, and 12, 2017 from 10am until 3pm (lunch will be provided).

Participants: Any faculty member (Tenure Track or Professional Track)

 
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