course is designed to introduce Environmental Science students to the task
of constructing models based on sparse data and, in so doing, to understand
the difficulties associated with this task. Virtually every environmental
problem is characterized by three things:
- Noisy/ambiguous data that defines the problem
problems are therefore usually defined by perception and opinion rather than
by objective data.
- Difficult physical modeling since not all of the input physics/chemistry/biology/geology is known
- Policy makers/public interest groups that do not understand science
and the scientific process.
This course is designed to immerse the students in these three intertwined
difficulties. This class will function like an environmental consulting
company with me as the Boss O Rama.
The class will be broken up into 4 teams (this will be done
on Thursday). Each team will be given the same problem to work on and
to prepare and present a consulting report in class.
This class is being held in a wireless laptop classroom and we will be
making use of that infrastructure throughout the term. A by product of
this class is that you will learn how to use Microsoft Excel as a statistics
and data analysis tool.
There will be no assigned text book as there is certainly not one that
is relevant to this material.
Most all of the assignments in this class will be group based and will
take two weeks to do. A few individual assignments to build skills in key
statistics areas will also be given.
Lectures will be a mix of computer based presentations and standard
blackboard lectures and derivations. Extensive notes for each lecture
will appear on the course web site:
Make sure you reload the course homepage each time you visit it
to pick up any changes.
Course Grading will occur around the following guidelines:
There will be no midterm in this course but there will be a final exam.
The final exam will count for 1/3 of your grade.
All student teams will be expected to be active in class discussions
and prepare presentations for the class during the course. There will
be 5 team assignments in this class, each with presentation components.
These exercises will found for the other 2/3 of your grade.
The goal in this class is to learn important techniques and to gain experience
building real models from real data. In this manner, I regard this class
as a skill building and proficiency based class and you will be graded
accordingly. In the past, no one in this class has received a grade lower
than B- .