GEOG 4463 - Earth Analytics Data Science Bootcamp
Primary Instructor
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Fall 2021 / Fall 2022 / Fall 2023 / Fall 2024
Learn key skills to automate data processing and visualization workflows that support both repeatable analysis and collaborative project approaches using scientific programming, version control and project management tools. Covers working with heterogeneous, large spatio-temporal data derived from space, airborne and ground based sensors and other sources. Gain applied experience through group projects that address real world problems. Same as GEOG 5463.
GEOG 5463 - Earth Analytics Data Science Bootcamp
Primary Instructor
-
Fall 2021 / Fall 2022 / Fall 2023 / Fall 2024
Learn key skills to automate data processing and visualization workflows that support both repeatable analysis and collaborative project approaches using scientific programming, version control and project management tools. Covers working with heterogeneous, large spatio-temporal data derived from space, airborne and ground based sensors and other sources. Gain applied experience through group projects that address real world problems. Same as GEOG 4463.
GEOG 5563 - Earth Analytics
Primary Instructor
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Spring 2022 / Spring 2023 / Spring 2024
Introduce students to major unanswered questions in Earth science and to the analytical tools, including data management, analysis and visualization, necessary to explore 'big data' from a suite of sensors. Aligns with Earth Lab, a new initiative of the University's Grand Challenge (http://www.colorado.edu/grandchallenges/) to use our expertise in space-based observation to address our world's most pressing problem. Comparable programming course work may be substituted for GEOG 5463 with instructor approval. Same as GEOG 4563.
GEOG 5663 - Earth Analytics Applications
Primary Instructor
-
Summer 2022 / Summer 2023 / Summer 2024
Develop expertise in finding, organizing, managing and processing large, heterogeneous, spatio-temporal data to address a real-world problem. Students will work collaboratively on semi-guided science project. Students gain critical skills required to understand data structures, utilize APIs, extract insight from data and understand how uncertainty propagates. Culminates with a formal presentation of project results. May be repeated up to 3 total credit hours.
