Course Title: Data Analysis & Statistics in the Earth Sciences
[Note: This course was last given in 2023.]
Course Introduction
Use of statistics and data-analysis methods is a practical necessity for conducting investigations into virtually any aspect of earth science research. As a consequence, instruction in the methods and procedures used most often by earth scientists needs to be provided to all students who aspire to professional careers in this field. In the past this training has often been organized on an ad hoc or “as needed” basis, usually in conjunction with a specific research project, and usually focused only on the procedures employed in the context of that project. This approach is no longer suitable for the training of undergraduate earth science students insofar as it usually (1) fails to provide adequate grounding in the wide range of procedures and methods available, (2) provides inadequate coverage of the particular application histories, assumptions, strengths and weaknesses inherent in the use of these procedures, and/or (3) neglects to provide focused instruction on the manner in which the results of such applications can be logically derived from, and tied back to, the earth science hypotheses. By the same token, general-service courses offered by university departments of statistics or mathematics often fail to include instruction in common earth-science procedures and/or take the specific nature of earth science data into consideration. In addition, the advent of new and highly sophisticated data-analysis strategies, such as machine learning, artificial intelligence, are widely acknowledged to have the potential to revolutionize earth scientists’ abilities to address many complex problems. Yet these are rarely included in basic data analysis instructional programs.
This course will address these needs and concerns by drawing on the instructor’s 30+ years of using, developing, promoting, writing about and teaching the skill – and the art – of data analysis in earth science contexts. Offering a trifecta that includes a survey of data-analysis theory, non-mathematical descriptions of how various procedures interact with, and reveal patterns in, datasets, and practical experience with the application of selected procedures to various earth-science data-analysis contexts, this course will take students with limited backgrounds in mathematics and train them to be confident practitioners of basic statistical analysis, regression analyses, time series analysis, directional data analysis, cluster analysis, eigenvector methods, multidimensional scaling and discriminant analysis. The course program will then go on to survey the procedures and applications of machine-learning methods and artificial intelligence in the earth sciences. Students who work through the course materials diligently and complete the instructional program successfully will be well positioned to select appropriate data-analysis strategies for a wide range of earth science data-analysis problems, design, and carry out their own descriptive and exploratory analyses, and interpret the results of these analyses with confidence. More importantly though, they will understand the power of these methods to make patterns in earth science data that are invisible to the naked eye and/ore causal inspector, visible and in so doing to enable the realization of investigations that would be impossible to deliver otherwise.
Topics Covered
Course Introduction, Intro. To Data Analysis I
Autumn Semester Finals Week – No Class
Intro. To Data Analysis II
Intro. To Data Analysis III, Stats & Probability I
Stats & Probability II, Matrices I
Matrices II; Spatial Data Analysis I
Spatial Data Analysis II, Quantitative Stratigraphy I
Quantitative Stratigraphy II, Sequential Data Analysis
Principal Components, r-Mode Factor Analysis
Principal Coordinates, Q-Mode Factor Analysis
Linear Discriminant Analysis, Canonical Variates Analysis
Correspondence Analysis, SVD, Partial Least Squares
Multivariate Morphometrics, Shape Theory, Geometric Morphometrics
Analysis of Outlines
Analysis of Images, Overview of Machine Learning
Deep Learning, Artificial Intelligence
Course Resources
MacLeod NJU Courses Site 