Environmental Data Analysis EESC
BC 3017
Introduction
- course aims -
- learn various tools for analysing and communicating
environmental data
- good preparation for senior thesis
- importance of data analysis in career and everyday life (e.g.,
for scientists,
- environmental problems often difficult to address
scientifically
because:
- they are complex and interdisciplinary
- many factors come into play (e.g. air quality issues)
- often can't manipulate system, lack of controls
- we need to learn how to
- obtain data
- perform your own measurements
- get data from another source
- manage & curate data
- convert data (unit conversions, derivations)
- visualize data, e.g.:
- analyse data (within conceptual/mathematical models)
- <>observations of ant nests occurences, table, boxplot, t-test<>
- perform statistical analysis
- communicate data and interpretation to others
Technical aspects and course overview
- website tour
- overview of modules
- software
- expectations from students (readings, homework, labreports)
- login: eda - password:....
- textbook
- Statplus 3.0 add-ins in Excel,
- USB drive (>=1GB) for backing up data and storing Statplus
files
- Mac/PC issues
- Statplus works only on PCs, we will not use it a lot outside of
class, you can use it on the lab computers
- the Mac version of MS office does not have the 'Analysis tool
pack' which we'll need, however you can add most of its functionality
by following these
instructions
- sign-up sheet and questions for students, background, what would
you like to get out of this class, access to computers
Prototype sequence for Environmental Data analysis
(As in Bangladesh as example)
1 Conceptualization of problem:
- Problem identification; background information; scientific/
engineering
context
- What data is important and why? How does information relate to
each
other
and to problem at hand?
2 Data collection
- appropriate methods
- methodological limitations
3 Presentation of
results
- data organization and reduction
- data visualization and statistical analysis
- Results of data analysis
4 Discussion of results and conclusions
- expected vs. anomalous results
- reconceptualization
- prediction
- remaining conceptual/data gaps
