Data and earth and environmental science skills are a powerful combination in today’s job market. There are over 2.3 million jobs in the United States that require data science skills. Market demand for data skills is predicted to keep increasing through the next decade as the data revolution continues. There’s never been a better time to develop an earth and environmental data science skill set and jumpstart your career in earth analytics. 

The University of Colorado, Earth Lab offers earth data science workshops for students and professionals of all skill levels and backgrounds. In our workshops you will learn core programming and open reproducible science skills that will support your ability to work with environmental and earth systems data in collaborative team environments.

All skills taught in University of Colorado earth and environmental data science workshops are guided by industry research to ensure that you learn skills that are in-demand in the job market.

Our Workshops Are For You

Workshops are appropriate for anyone interested in improving their earth and environmental data science skills, from beginners to experts. Past attendees have come from government, academic, and corporate organizations. We present the concepts using real-world data and workflows common in the industry.

How You Will Learn

We teach our workshops using a cloud-based Jupyter Hub. This means that you do not need to have software installed on your computer to take our workshops. You just need a computer with internet access! However, if you do wish to setup your computer with the tools necessary to complete our workshops, you can do that as well.

Costs & Logistics

All workshops will be held in the Earth Analytics Visualization Studio located on east campus at the University of Colorado - Boulder. Rates range from $400-600 per day depending upon the content topic and level. Please contact us for specific rates. 

 

Workshop Topics

Introduction to Programming & Data Science

Time: 1-2 days

Level: Beginner. No experience necessary.

Skills Covered: In this workshop, you will learn to:

  • Use command line tools (e.g., Bash) to programmatically access and manipulate directories and files.
  • Write Python code to work with data (e.g., read files, analyze and plot data).
  • Use Jupyter Notebooks to organize and execute Python code.

Introduction to Version Control and Git for Data Science

Time: ½ day

Level: Beginner. No experience necessary.

Skills Covered: In this workshop, you will learn:

  • The basics of version control.
  • How to install and configure git on your computer.
  • Git and GitHub collaborative workflows (e.g. forking, cloning, committing, pushing, pull requests, and more).

Introduction to Clean Coding and the tidyverse in R

Time: ½ day

Level: Beginner/Intermediate. You should have some basic working knowledge of R to get the most out of this workshop.

Skills Covered: In this workshop, you will learn to:

  • Structure workflows with R projects.
  • Write expressive, well-organized code, and plan complex workflows using pseudo code.
  • Effectively design and manage workflows with many input and output files.
  • Use tidyverse to “munge” (e.g. sort, clean, group and summarize) tabular data.
  • Visualize data using ggplot2.

Introduction to Spatial Data Science Using Open Source Python

Time: 2 days

Level: Intermediate. You will get the most out of this workshop if you have some experience using Python. Specifically, you should know how to load packages and work with a numpy array. Experience with pandas is also helpful but not required.

Skills Covered: In this workshop, you will learn to:

  • Open, plot and manipulate vector data in Python using Geopandas.
  • Open, plot and manipulate raster data in Python using Rasterio.
  • Use Jupyter Notebooks to write code in Python.

 

Who We Are

We are a group of professionals with extensive expertise in teaching and applying earth and environmental data science approaches to a variety of data types and formats. You will learn at a workshop built by professionals for professionals. All instructors have geospatial industry experience and know the skills necessary to succeed firsthand. Our extensive expertise includes: Data Science, Education, Statistics, Machine Learning, Remote Sensing, Scientific Programming, Databases, Cloud Computing & more.

Leah Wasser's Headshot

Leah Wasser, PhD: Wasser has over 20 years of experience teaching data-intensive science. At CU Boulder she developed and leads the Earth Analytics professional program. She also developed www.earthdatascience.org, an open education portal with hundreds of free lessons reaching learners around the world. Previously Wasser developed and lead the Data Skills program at the National ecological Observatory Network. Wasser has a longstanding partnership with the Carpentries and is a certified Carpentry instructor. Wasser holds a PhD in Ecology from Penn State University. Leah currently leads the pyOpenSci effort and also is a maintainer for several open source software packages including EarthPy, matplotcheck and abc-classroom.

 

Jenny Palomino Headshot

Jenny Palomino, PhD: Palomino has over a decade of professional experience working with geospatial and database technologies and five years of data science teaching experience. Palomino develops and teaches earth data science courses at CU Boulder and is a primary contributor to www.earthdatascience.org, reaching thousands of students around the world each day. She holds a PhD in Environmental Science from the University of California Berkeley. Jenny is a core maintainer of the EarthPy package and an active contributor and package reviewer for the pyOpenSci community. 

 

Max Joseph Headshot

Max Joseph, PhD: Joseph is an experienced data scientist with skills in machine learning, statistics, ecology, and reproducible open science. He is an avid contributor to the R and Python programming open source communities. He has developed and taught many courses and workshops at CU Boulder, where he obtained a PhD in Ecology and Evolutionary Biology.

 

Joseph McGlinchy headshotJoe McGlinchy, M.S.: McGlinchy has a decade of experience in geospatial science and remote sensing, during which he has been using Python in professional and research settings. He has developed Python code for remote sensing applications such as data retrieval and machine / deep learning models using moderate-resolution and commercially available high-resolution satellite imagery. Prior to Earth Lab, he worked at ESRI as an Imagery Scientist. He holds an MS in Imaging Science from the Rochester Institute of Technology. Joe is a maintainer for the open source EarthPy package.