Introduction

Introduction#

Welcome to Geospatial Data Science! In the first lecture we will provide an overview of the course including motivations, expectations, grading, and the schedule.

Lecture content#

  • Overview of course

    • What is geospatial data science?

  • Schedule and expectations

    • Lectures, labs, grading

  • Final projects

  • Introduction to Friday’s lab

Geospatial data#

Vector and Raster/gridded data#

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Network data#

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Python#

  • Popular high-level programming language

  • Easy-to-read

  • Extensive and mature libraries (or packages)

  • Free and open-source

    • Accessible

    • Can be examined, modified, and improved

  • Constantly evolving

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Version control#

Git#

  • Version control software for tracking changes to a set of files

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GitHub#

  • A cloud-based Git repository hosting service

  • Makes it easier to coordinate work among programmers collaboratively developing source code during software development

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  • Python and projects that use Python (e.g. matplotlib) are all maintained and developed by a community of scientists and programmers on GitHub

  • An active, up-to-date GitHub profile, with contributions to open-source project is a great way to provide evidence of skills

Course schedule#

Lectures#

  • Tuesday @ 9am

    • Nine lectures

    • Project presentations in Week 10

Labs#

  • Wednesday @ 9am in MCK 445

    • Seven lab assignments

    • Two labs to concentrate on final project

    • No lab in Week 10

Activities/project work#

  • Thursday @ 9am

    • Three activities

    • One lecture about previous class projects

    • Five sessions for project work

    • Project presentations in Week 10

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Course evaluation#

  • Lab assignments (45%) due every Wednesday 11:59 pm

  • Final project (45%)

    • Presentations due May 31, 11:59 pm

    • Write-ups due June 6, 11:59pm

  • Participation (10%)

    • Credit can be earned through attendance in lectures, visiting Professor and GE during office hours, and helping other students in labs.

Final project#

  • An opportunity to explore a particular topic of interest using some of the skills developed in this course

  • Students can work independently or in groups of two or three

  • Sharing of project ideas is encouraged so we can form teams

Final project schedule#

  • Week 5: Discuss project ideas with peers and instructors, submit a short summary of a project idea via Canvas

  • Week 6: Form teams, create GitHub repo, and provide some basic info about project as a README.md

  • Week 8: Provide informal update to instructors, ensure data has been accessed, goals are accomplishable

  • Week 10: Present project to class and submit write-up by the end of the week

Some course themes#

Everything is open-source#

  • All software we use is freely available

  • Labs can be completed anytime, anywhere from any OS

  • Course materials are publicly-available on the internet

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Promote collaboration and communication#

  • With instructors and peers

  • On GitHub

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Learn about environmental challenges in the Western US#

  • Urban planning

  • Hazards (e.g. wildfires, flooding)

  • Energy

  • Climate, hydrology, glaciology

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Tips for success#

  • Don’t try and write perfect code - if it works, it works

  • It’s not always necessary to write code, adapting code is quite normal

  • Make use of stackoverflow

  • Don’t be afraid to ask (peers or instructors)

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  • Take responsibility for learning

  • Organize your files

  • Check Canvas regularly

  • Maintain your GitHub profile and repository

../../_images/horse.png ../../_images/doing.png

Learning outcomes#

  • Have confidence using Python specifically for GIS and other geospatial data science applications. In doing so, you will also be comfortable using Python for other things as well

  • Be able to download, process, analyze, and visualize the main types of geospatial data

  • Automate boring GIS tasks (no more clicking!)

  • Improve programming skills

  • Learn how think computationally and statistically

  • Solve real-world problems using spatial analysis

  • Run basic machine learning models

  • Manage a data science project using version control

  • Collaboratively develop a data science project

  • Communicate results of data science project orally and as a short write-up

Careers#

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