Summer Intensive Courses
All MEDS students will complete 12 units of intensive coursework
(early August through mid-September) to start the program. Summer
courses will be held at the National Center for Ecological Analysis and
Synthesis (NCEAS) in downtown Santa Barbara.
Course number
|
Course title
|
Units
|
Description
|
Instructor
|
EDS 212
|
Essential
Math for Environmental Data Science
|
2
|
Review of quantitative methods that are commonly used in environmental
science. The course will cover single and multivariable functions and
graphing, basic linear algebra, complex numbers, integral calculus and
simple differential equations.
|
Allison Horst
|
EDS 221
|
Scientific
Programming Essentials
|
4
|
This course teaches key scientific programming skills and demonstrates
the application of these techniques to environmental data analysis and
problem solving. Topics include structured programming and algorithm
development, flow control, simple and advanced data input-output and
representation, functions and objects, documentation, testing and
debugging. The course will be taught using a combination of the R and
Python programming languages.
|
Allison Horst
|
EDS 214
|
Analytical
Workflows and Scientific Reproducibility
|
2
|
The generation and analysis of environmental data is often a complex,
multi-step process that may involve the collaboration of many people.
Increasingly tools that document and help to organize workflows are
being used to ensure reproducibility, shareability, and transparency of
the results. This course will introduce students to the conceptual
organization of workflows (including code, documents, and data) as a way
to conduct reproducible analyses. These concepts will be combined with
the practice of various software tools and collaborative coding
techniques to develop and manage multi-step analytical workflows as a
team.
|
Julien Brun
|
EDS 217
|
Python for Environmental Data Science
|
2
|
This course teaches the fundamentals of programming in Python. Students
will learn foundational skills and concepts including data structures,
programming basics, and how to clean, subset, aggregate, transform and
visualize data. Course materials demonstrate the application of these
techniques for environmental data analysis and problem solving.
|
Kelly Caylor
|
Fall Quarter
Note: Fall, Winter and Spring courses will utilize
both of the MEDS campuses (UC Santa Barbara and NCEAS), which are ~11
miles (~20 minutes driving, ~30 minutes by bus) apart from each other.
Students will only ever have planned activities at one of the campus
locations on any given day.
Course number
|
Course title
|
Units
|
Description
|
Instructor
|
EDS 220
|
Working
with Environmental Data
|
4
|
Introduces students to the broad range of data sets used to monitor and
understand human and natural systems. Course will cover field and
station data, remote sensing products, and large-scale climate datasets
including climate model projections. Skills will include evaluating data
collection and quality control methods used in existing datasets, and
working with existing databases of time-series and spatial information
including cloud computing databases and new repositories of
environmental datasets. Students will learn basic workflows for
selecting, obtaining, and visualizing datasets, and best practices for
reliable data intercomparisons. Students will gain hands-on experience
with an environmental dataset of their choice by developing tutorial
Jupyter notebook materials for a relevant use case.
|
Samantha Stevenson
|
EDS 222
|
Statistics
for Environmental Data Science
|
4
|
This course teaches a variety of statistical techniques commonly used to
analyze environmental data sets and quantitatively address environmental
questions with empirical data. The course covers fundamental statistical
concepts and tools, including sampling and study design, linear
regression, inference, and time series analysis, as well as foundational
concepts of spatial and space-time dependency and associated impacts on
inference.
|
Tamma Carleton
|
EDS 223
|
Geopatial Analysis & Remote Sensing
|
4
|
This course introduces the spatial modeling and analytic techniques of
geographic information science to data science students. The emphasis is
on deep understanding of spatial data models and the analytic operations
they enable. Recognizing remotely sensed data as a key data type within
environmental data science, this course will also introduce fundamental
concepts and applications of remote sensing. In addition to this
theoretical background, students will become familiar with libraries,
packages, and APIs that support spatial analysis in R.
|
Ruth Oliver
|
EDS 242
|
Ethics & Bias in Environmental Data Science
|
2
|
This course will focus on ethical considerations in collecting, using,
and reporting environmental data, and how to recognize and account for
biases in algorithms, training data, and methodologies. Students will
also examine the human and societal implications of these issues within
environmental data science.
|
Dena Montague
|
Winter Quarter
Course number
|
Course title
|
Units
|
Description
|
Instructor
|
EDS 211
|
Team
Science, Collaborative Analysis and Project Management
|
2
|
Science in general, and data science in particular, are more and more
requiring team science approaches to addressing the most pressing
questions. Managing team science projects is therefore becoming an
increasingly important skill for any scientist. This course will explore
the principles and practical tools available for effective and efficient
project management.
|
Ben Best
|
EDS 232
|
Machine
Learning in Environmental Science
|
4
|
Machine learning can help process big/complex data and extract
knowledge. It forms one of the foundations in data science. This course
provides a broad introduction to machine learning and statistical
pattern recognition. Topics include supervised learning (decision tree,
random forest, support vector machines, neural networks) and
unsupervised learning (clustering, dimensionality reduction, deep
learning). Problems and exercises are framed within environmental
science applications. The course will use programming languages like R
and Python to support learning how to do advanced scientific programming
to solve real environmental problems.
|
Ben Best
|
EDS 240
|
Data Visualization and Communication
|
4
|
This course will focus on basic principles for effective communication
through data visualization. Students will deepen their understanding of
how people perceive and interpret graphical representations, and will
learn about information visualization frameworks they can apply to
design intuitive and impactful data visualizations. Beyond effective
visualization design, we will explore ‘storytelling with data’
–integration of visual elements and text in a way that is clear, concise
and engaging. Class time will consist of brief periods of lecture
interspersed with small group and whole group discussions, peer
critiques, and hands-on data visualization activities. Assignments will
involve applying such frameworks and concepts in critique of existing
visualizations, and in creation of data visualizations using popular
software packages.
|
Stacy Rebich-Hespanha
|
EDS 241
|
Environmental Policy Evaluation
|
4
|
This course will present state of the art program evaluation techniques
necessary to evaluate the impact of environmental policies. The program
evaluation methods presented will aim at identifying and measuring the
causal effect of policies, regulations, and interventions on
environmental outcomes of interest. Students will learn the research
designs and methods for estimating causal effects with experimental and
non-experimental data. This will prepare the students for interpreting
and conducting high-quality empirical research, with applications in
cross-sectional data and panel data settings.
|
Olivier Deschenes
|
EDS 411A
|
MEDS
Capstone Course
|
4
|
First quarter of a two-quarter group study/analysis of how to apply data
science and tools to an environmental problem. In this quarter, students
are expected to work with their project client to finalize project
plans, assign individual roles and responsibilities, develop a project
design plan and deliverables, and make significant headway on
implementing those plans.
|
tbd
|
Spring Quarter
Course number
|
Course title
|
Units
|
Description
|
Instructor
|
EDS 213
|
Databases and Data Management
|
4
|
This course will teach students how to store and manage environmental
information. The course will focus on relational database structure,
schemas and data relationships, and introduce SQL as a means to create
and query databases. This course also covers the concept of metadata as
well as archiving data products on data repositories to make them
available to the broader community.
|
tbd
|
EDS 230
|
Modeling
Environmental Systems
|
4
|
Computer-based modeling and simulation for practical environmental
problem solving and environmental research. The course will cover both
the selection and application of existing models and best practices for
designing new models. Topics include conceptual models, static and
dynamic models, and models of diffusion, growth and disturbance.
Techniques include sensitivity analysis, calibration and model scenario
design.
|
Naomi Tague
|
EDS 231
|
Text
and Sentiment Analysis for Environmental Problems
|
2
|
This course will cover foundations and applications of natural language
processing. Problem sets and class projects will leverage common and
emerging text-based data sources relevant to environmental problems,
including but not limited to social media feeds (e.g., Twitter) and text
documents (e.g., agency reports), and will build capacity and experience
in common tools, including text processing and classification,
semantics, and natural language parsing.
|
Mateo Robbins
|
EDS 411B
|
MEDS
Capstone Course
|
4
|
Second quarter of a two-quarter group study/analysis of how to apply
data science and tools to an environmental problem. In this quarter,
students are expected to complete all project plans and deliverables,
develop and submit a project repository and technical documentation,
give an oral defense of the project, present the research to a general
audience.
|
tbd
|