Course intent: The objective of this course is to provide students with basic data analysis, quantitative and computer skills increasingly necessary to succeed in geoscience careers, and in many other career paths. Similar courses at other institutes are commonly taught as an upper division course, but it also makes sense to acquire these skills early and build on them in subsequent geology courses, which is the approach taken here. Some of the software you will be introduced to includes Adobe Illustrator and Photoshop, ArcGIS, Excel, Surfer, and Visual Basic for Applications. In addition, the course should familiarize the student with department and university analytic resources, and with a variety of sources of data. As an important byproduct students should learn a fair bit of geoscience also.

Specific topics covered include: describing and comparing sample populations, simple data manipulations, creating and working with databases, surface contouring and modeling, fractal geometries and distributions, exponential behavior, basic GIS principles, analysis of remotely sensed data, graphic data representation, and simple computer modeling of geologic processes. In all cases examples and exercises will involve geoscience data, but geoscience provides a large umbrella. For many exercises the student can chose to work with data that matches their interests.

Student audience: The intended student audience consists of geology, geography and environmental science majors who are in the first or second year of their program. Students minoring in these areas may also want to take this course, as may education majors obtaining endorsements in earth or natural science.

Prerequisites would be one of the following: GEOL1010, GEOL 1100, GEOL1170, GEOG1030, GEOG1060, or GEOG1070. Other courses that give the student a background in physical earth science would be acceptable on a case by case basis. Please do not hesitate to talk to the instructor if you have a question in this regard.

Texts: There is not an appropriate text to assign, so we will take readings from a variety of sources. Some particularly good books that are suggested for the course are:

• Swan, A. R. H. & Sandilands, M., 1995, Introduction to geological data analysis; Blackwell Science, p. 446. (not required).
• Liengme, Bernard V., 1997, A guide to Microsoft Excel for Scientists and Engineers, Wiley, New York, 209 p. ISBN 0 470 24428 3 (required).
• There is a suite of assigned readings.
• other useful references.

Grading: The grade should be a direct function of how much work you put in. There will be a total of 150 points possible, 10 for each of the 14 weeks of exercise, and 10 for the final (which will be a multiple choice exam). The grade bins are as follows:

• 150 -142 = A+
• 142 -134 = A
• 134 -126 = A-
• 126 -118 = B+
• 118 -110 = B
• 110 - 102 = B-
• 102 - 94 = C+
• 94 - 86 = C
• 86 -78 = C-
• < 78 -you don't want to be there!

Late assignments will be taken, but with a 1 point penalty if less than a week late and a 3 point penalty if more. Opportunities for accumulating more points may arise during the semester.

While you are encouraged to help each other, the final product must reflect your own work and understanding. The penalty for cheating will be failure for the course, and the stigma associated with cheating. It just isn't worth it, and it isn't necessary.

Clearly the majority of your effort should go into the weekly assignments. When group work is involved, based on peer evaluation and with instructor discretion, students will be penalized if they did not constructively contribute to the group's effort in an amount proportional to the lack of contribution.

Week 1: Introduction to course:

• introduction to course content and objectives, and computer lab facilities and policies.
• course text books, other references and information.
• review of topics covered in the course and relevance to the discipline
• types of data, and data and software sources.
• Exercise 1.

Week 2: Identifying , describing and comparing populations:

• description of histograms, rose and other frequency diagrams.
• summary statistics..
• comparative tests (e.g Chi Square).
• example of an analysis of a data set in Excel.
• Exercise 2.

Week 3: Classical linear and curvilinear regression:

• simple linear regression.
• significance of R-squared.
• simple transformations (e.g. log), and normalization.
• example of an analysis of flood recurrence interval vs. discharge.
• cautions on extrapolations.
• Exercise 3.

Week 4: Analysis of fractal geometries in the geosciences:

• definition of fractal geometry.
• log-log size vs. frequency plots and the fractal dimension.
• examples of fractal geometries in earth science (Turcotte, 1992).
• use of fractal relationships in filling in information or resolution gaps, in risk assessment, and in modeling.
• Exercise 4.

Week 5: Surface analysis and modeling:

• types of and use of 'surface' diagrams in earth science.
• gridding and interpolation procedures.
• introduction to contouring software packages (e.g. Surface3).
• 3-d transect diagrams and visualization elements.
• derivative surfaces.
• example from data to contour and transect diagrams of groundwater tables and of associated flow nets.
• Exercise 5.

Week 6: Computer aided map construction:

• imp. of maps and cross sections as data models.
• basic components and principles of map construction.
• sources of base maps, including the web.
• computer maps and linked databases (this way to GIS).
• common software packages used to compile maps (will focus on Adobe Illustrator).
• examples of different types of maps.
• Exercise 6.

Week 7: Analysis of imagery:

• scales of application: can be applied to satellite or thin section imagery.
• sources of imagery.
• image distortion and rectification.
• spectral analysis, and classification.
• variety of related software will be introduced.
• examples of use of imagery in resource exploration and management.
• Exercise 7.

Week 8: Geoscience databases:

• basics of database structure.
• querying and manipulating databases.
• relevant software.
• metadata.
• Exercise 8.

Week 9: Complex systems, multiple data sets and Geographic Information Science:

• introduction to Stella software for modeling complex systems.
• examples of problems requiring management of multiple data sets explored (e.g. land fill siting, groundwater flow modeling, mineral exploration).
• basic approach of Geographic Information Science described.
• DEMs
• common software packages described.
• vector vs. rastor based representation will be explored.
• tour of departments GIS facilities.
• examples of GIS products explored.
• Exercise 9.

Week 10: Modeling with exponential and power law functions (see Vacher 2000):

• basics of equations and equation manipulations.
• examples of geologic phenomena that display this non-linear behavior.
• Exercise 10.

Week 11 & 12 : Computer modeling of earth processes - Visual Basic for Applications in Excel, and Stella.

• uses of computer modeling in geosciences.
• intro to programming languages typically used in geoscience modeling (Visual Basic, C++).
• Students will be introduced to the basic design and approach behind VBA, and Visual Basic.
• Introduction to Stella.
• Exercise 11.

Week 13: Temporal spectral analysis:

• examples of temporal earth science data: e.g. tree ring thicknesses, facies changes in a stratigraphic column, growth structures in fossils.
• explanations for cyclicity (e.g. Milankovich cycles).
• Markov Chain analysis.
• tests for cyclicity vs. randomness.
• examples from the literature (Swan and Sandilands, 1995).
• Exercise 13.

Week 14: Classification of data using fuzzy logic:

• concepts of fuzzy sets, expert rules and fuzzy measures will be introduced.
• examples will be given of fuzzy sets, and expert rules (e.g. in sedimentary facies classification, in hand specimen mineral identification).
• Exercise 14.

Week 15: Wrap up.

• importance of knowing about garbage in - garbage out.
• context of skills in geoscience.
• class evaluations.

Computer lab facilities and policies available for this course:

It is beneficial to get used to using and migrating between different platforms, different operating systems, and different software versions. Having to get your data onto a different platform is a real world problem. One frustration in courses like this is when the computer or program doesn't work right or just as expected. The key is to get help!

• Durham Science and other computer labs on campus.
• Cartography and GIS lab.
• Complex systems lab.

Also, this is a relatively new course (third time its been taught), please be patient as we work our way through it!

Survey of student backgrounds.

Copyright by Harmon D. Maher Jr.. This material may be used for non-profit educational purposes if proper attribution is given. Otherwise please contact Harmon D. Maher Jr.