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GPGN490 Class Survey, Spring 2022

Which modules to expand, shrink,or keep about the same?

Python and Jupyter Intro
Machine Learning with SkLearn
ArcGIS Online and Python API
Deep Learning
AGOL Applications (StoryMaps, Dashboards, etc.)
SuAVE with SDGS and Spatial Statistics
Raster analysis in Jupyter

Please add comments:

Which additional lecture topics would you suggest?

(To make room for additional topics, please suggest which current topics should be removed or reduced)

Was the homework too difficult, too easy, or about right?

Python and Jupyter Intro (HW1)
Geopandas/Spatial analysis (HW2)
ML/Geoenrichment/ArcGIS Python (HW3)
Deep Learning (HW4)

Please add comments about HWs. In particular, which modules should be accompanied by homework?

Should there be more emphasis on machine learning or on spatial data analysis in Python? 

Should we add more discussions during lectures, quizzes, a midterm, a final exam?

Discussions during lectures
Quizzes during lectures or online
Midterm exam
Final exam

How many hours, on average, did you spend on this course per week (including lectures, lab work, office hours?)

What was the most valuable component (or components) you learned that you see yourself using in the coming year?

How does this course compare with similar GPS courses, and how is it different? Basically, does it fit the curriculum or is it an outlier?

Any additional comments?

What was your level of expertise before this course?

Machine Learning
Deep Learning
Python and Jupyter
GIS and spatial analysis
Geopandas and other open-source libraries
ArcGIS Desktop and/or Online
ArcGIS Python API
Spatial statistics
Raster analysis