Curriculum

The certificate is designed to be completed within one year. All courses are on 8-week schedules, with the potential to complete the certificate in as short as 8 months depending on the courses selected.

Course Summer 2021
Session
Fall 2021
Session
Spring 2022
Session
Advanced GIS 8/18/21 – 10/12/21
Data Visualization and Communication 5/24/21 – 7/16/21 8/30/21 – 10/22/21
Geospatial Computation 1/18/22 – 3/18/22
Introduction to GIS 10/20/21 – 12/14/21
Remote Sensing Foundations

 

By the end of this program, you will have gained the skills to:

  • Understand important conceptual underpinnings of GIS and their practical applications
  • Harness remote sensing as a tool for environmental or landscape problem-solving
  • Analyze data to reveal patterns and relationships to better inform decision-makers
  • Create impactful maps and visualizations of large and complex datasets that encourage comparison and visual analysis by the viewer
  • Go beyond “off the shelf” buttons towards the ability to customize workflows, scripts, or models in Python and R

Course descriptions

Snapshot:

Instructor: Brian Voigt
Start Date: October 20, 2021

A Geographic Information System (GIS) is a computer-based tool for managing, documenting, analyzing and presenting data and information. GIS allows us to investigate the spatial (location, size and shape), temporal and descriptive qualities (e.g. population, % coverage) of data across multiple disciplines, and is increasingly used to tell complicated “stories” using printed and web-based materials.

This course will introduce the student to the basic principles and techniques of GIS through a mixture of readings, videos, slide-based presentations and real-world examples. Participants will gain practical skills in the application of GIS to data analysis and problem solving through the completion of lab assignments and participation in the course Discussion Forum.

This course uses the most current version of ESRI’s ArcGIS Pro software.

By the end of the course, the student will be able to:

  • Develop a practical understanding of GPS and GIS concepts (e.g., data & file types, projections & coordinate systems and spatial relationships) & geoprocessing techniques
  • Perform spatial analysis using desktop and web-based GIS software
  • Practice effective communication using the (technical) language of GIS, presentation-quality maps and web applications
  • Understand best practices for creating, storing and managing data in a variety of geospatial and tabular formats
  • Gain experience identifying and accessing publicly available data
Do I enroll in Introduction to GIS or Advanced GIS?

Snapshot:

Instructor: Brian Voigt
Start Date: August 18, 2021

Geographic Information Systems (GIS) are increasingly featured as an integrating technology for analyzing economic, social and environmental data. This course will expose the learner to advanced techniques for collecting, curating, analyzing and understanding spatial data and communicating technical findings to a broad range of audiences. Building on the content of the Introductory Course, Advanced GIS will prepare the student to independently conduct spatial analyses.

Learning materials will include a mixture of readings, videos, slide-based presentations and critical evaluation of real-world examples. Participants will gain practical skills in the application of GIS to data analysis and problem solving through the completion of lab assignments and participation in the course Discussion Forum.

This course uses the most current version of ESRI’s ArcGIS Pro software.

By the end of the course, the student will be able to:

  • Understand & identify ethical questions surrounding data creation, analysis and representation
  • Integrate public data resources from multiple repositories into a cohesive analytic framework
  • Apply advanced vector (e.g. network analyses, attribute conflation, queries) and raster (e.g. hydrology, interpolation, raster calculator) geoprocessing tools to analyze spatial data
  • Create automated geoprocessing workflows using Model Builder and the Python programming language
  • Apply critical thinking skills to assess the suitability of available data (e.g. scale, resolution, accuracy and currency) and/or geoprocessing approaches
  • Acquire the necessary skills to develop and execute a project requiring GIS as a management, analytical, and/or visualization tool

Snapshot:

Instructor: Bill Shander
Start Date: May 24, 2021

Decisions in our work lives are driven more and more by data. Analyzing this data can reveal patterns and relationships that can be used to better inform decision-makers. One way to present this analysis is through data visualization, which is the graphical representation of information.

Visualizations such as charts, graphs, or maps can help make data easier to understand, leading to better decisions. Data-driven decisions could mean the difference between the success or failure of any organization. There are many tools for visualizing data, but without a solid grasp on the fundamentals of graphic design and perceptual theory, visualizations may not convey the intended message.

This course will introduce students to the fundamentals of data visualization and communication. Students will learn the ways humans use their cognitive and perceptual abilities to comprehend information, best practices for creating compelling and effective data visualizations, and the many nuanced factors influencing the successful application of these practices.

By the end of the course, the student will be able to:

  • Apply a framework for communication to get the right people to the right content at the right time
  • Recognize, explain, and identify solutions for some of the challenges we face working with data
  • Produce an entire conceptual data story in a form that will allow you to move on to execution of individual components, in detail, later
  • Describe human visual perception and apply it to specific design and visualization decisions
  • Effectively use visual variables such as size, value, texture, color, orientation, and shape to communicate information
  • Recognize the purpose for different data visuals and when and how to apply them
  • Create impactful maps and visualizations that enable understanding

Snapshot:

Instructor: Orhun Aydin
Start Date: January 18, 2022

Geospatial computation is the study of general computational methods that are applied to spatial and spatiotemporal data for exploratory, confirmatory, descriptive or predictive analysis. This course introduces foundational concepts in geospatial computation and its applications in spatial data science within the context of geographic information systems.

Computational approaches in spatial simulation, exploratory data analysis, predictive analysis and geospatial data visualization will be elaborated. Implementations of data-science centric geospatial approaches in R and ArcGIS will be covered in the context of spatial problem solving in a string of applied computational project assignments. No prior knowledge of R programming is required.

By the end of this course, the student will be able to:

  • Identify feasible geocomputational approaches in spatial data science for a wide variety of spatial problems
  • Gain knowledge of spatial pattern mining, spatial regression and exploratory data analysis
  • Recognize stages of spatial analysis that rely on geospatial computation and form repeatable analysis workflows leveraging GIS and R
  • Obtain working knowledge of R language within the context of geospatial computation and its integration to a GIS

Snapshot:

Instructor: TBA
Start Date: Spring 2022

Significant advances in access to geospatial datasets and cloud-based computing resources have ushered in a new era of user-friendly big data analysis, and satellite remote sensing has become a critical component of many environmental research and monitoring programs. However, effective use of satellite imagery requires a foundational understanding of sensor, image and surface characteristics as well as methods for translating analysis-ready data to decision-ready analysis.

This course will introduce students to fundamentals of remote sensing theory and image processing techniques using the Google Earth Engine Platform, which “combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.” By integrating hands-on practical training with evidence-based experiential learning, students will establish a basic understanding of how to use remote sensing as a tool for environmental problem-solving.

By the end of the course, the student will be able to:

  • Improve their understanding of the physical processes involved in the acquisition of remote sensing imagery, as well as the unique spectral, spatial, temporal and radiometric properties of different image sources
  • Build a working knowledge of a wide array of geospatial datasets available in the Earth Engine Data Catalog, including optical, thermal and microwave imagery from the Landsat, MODIS, Sentinel-2, and Sentinel-1 satellites, and derived products such as the Hansen Global Forest Change and JRC Global Surface Water datasets
  • Use the Earth Engine Code Editor to develop basic geospatial workflows using the JavaScript API
  • Explore both static and interactive data visualization techniques including tables, maps, charts, GIFs and Earth Engine Apps
  • Apply standard multi-spectral and multi-temporal image processing techniques, including basics of land cover classification and change detection
  • Develop a portfolio of example scripts, data visualizations and analyses across a range of environmental application areas