Spring 2023 - EASC 705 G100
Special Topics (3)
Class Number: 7867
Delivery Method: In Person
Course Times + Location:
Th 12:30 PM – 2:20 PM
TASC2 7201, Burnaby
1 778 782-3306
Prerequisites:Permission of the instructor.
In the Earth sciences, we deal with a variety of data acquired by geological mapping, geochemical analysis, and geophysical surveys. Although such data may be quite different, they can have certain common characteristics; for example, some types of data are recorded as a function of time or geographic location. Consequently, there are certain standard approaches to representing such data, understanding their significance, and analyzing uncertainties. Students will learn how data can be organized and gain practical experience with some basic techniques that will enable them to develop alogoithms for data analysis. While simple data analysis can sometimes be carried out using Excel spreadsheets, many problems will require the use of advanced programming languages such as Python. Geospatial analysis and 3D modelling of various data sets will involve the use of QGIS and Leapfrog Geo, respectively. Lectures will provide the background to the various methods, with the computer-based lab assignments and term project helping to develop skills in practical programming, field data analysis, and 2D/3D modelling.
Provisional course topics:
- Matrices and data organization
- Univariate/bivariate statistics, tests of statistical significance
- Principal component analysis
- Analysis of spatial data: interpolation, variograms and kriging
- Introductory computing with Python
- Introductory GIS and 2D/3D geological modelling
- Laboratory Excercises 20%
- Term Project (Integrated GIS & 3D modelling) 40%
- Midterm exam 1 20%
- Midterm exam 2 20%
Python, Leapfrog and QGIS software will be available in the computer lab.
Davis, J.C., 2003, Statistics and Data Analysis in Geology, 3rd Edition, Wiley, ISBN: 978-0-471-17275-8
Petrelli, M., 2021, Introduction to Python in Earth Science Data Analysis: From Descriptive Statistics to Machine Learning, Springer, ISBN: 978-3-030-78054-8
REQUIRED READING NOTES:
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Graduate Studies Notes:
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