Geologists collect a wide variety of data that are used to understand underlying geological processes with the aim to develop a geological model. Geologists are particularly interested in data patterns that are representative of a geological phenomenon or process. Therefore, a clear understanding of data patterns is a necessity for any geologist. The branch of mathematics that deals with understanding the distribution of data and patterns is statistics. Hence a good understanding of statistics is necessary to perform a proper analysis of geological data. In an increasingly data-driven future of geosciences, it is imperative for all young geoscientists to be familiar with basic statistical concepts and data analysis procedures. In the present course, we introduce statistical procedures commonly used in geological data analysis. Further, we use the Python programming language to help geologists develop their own code for statistical analysis of their data.

Course Outcome:

1. Data analysis using Pandas and plotting using Matplotlib

2. Statistical Concepts as applied in Geology

3. Application of python programming to perform statistical data analyses in geology

Course Length: 30 hours

Note: No background in Python Programming or Statistics are necessary to attend this course

Statistical Concepts:

1. Descriptive Statistics: mean, median, mode, quartile, skewness, kurtosis, variance and standard deviation.

2. Introduction to Inferential Statistics, which statistical test to choose.

3. Uncertainty, Accuracy, Precision

4. Probability, Probability Distribution, generating probability mass function, and density function.

5. Binomial, Poisson, and Normal Distribution. Why work with the normal distribution?

6. Null Hypothesis, Alternative Hypothesis, Hypothesis Testing, Error Types

7. Parametric Tests: One-Sample test, Two-sample test, one-tail and two-tail tests, Z-score, p-value, z-test

8. t-distribution, F-distribution, ANOVA, χ2 –distribution

9. Non-Parametric Tests: Mann-Whitney Test, Kolmogorov-Smirnov Test

10. Regression Analysis: Covariance, Correlation, Linear Regression, Non-Linear Regression

11. Sequential Data Analysis: Markov Chain, Runs Test, Auto-Correlation, Cross-Correlation

Python Programming Topics applying Statistical Concepts:

i) I/O, Data Types, Data Operators

ii) Procedural and Conditional Programming including Loops

iii) Data Containers

iv) Python Function

v) Numpy, Pandas, Matplotlib

vi) SciPy, Stats: Specifically used for statistical analyses

Learning about the application of statistics in geology using Python can provide several benefits. Here are some of the key advantages:

Data analysis

Hypothesis testing

Spatial analysis

Geological modeling

Uncertainty quantification

Visualization and communication

By acquiring knowledge in statistics and applying it using Python, geologists can enhance their analytical capabilities, improve their decision-making process, and gain a deeper understanding of geological processes. It enables them to extract valuable insights from complex geological datasets and communicate their findings effectively. 

Personal Benefits:

Enhanced skills:

Career Advancement

Increased productivity

Professional growth

Organizational Benefits:

Improved data analysis

Cost savings

Enhanced accuracy and reliability

Streamlined workflows

Effective communication

In summary, learning about statistics in geology using Python brings personal benefits by expanding your skill set, boosting your career prospects, and improving productivity. At the organizational level, it enhances data analysis, decision-making, and communication, leading to improved accuracy, cost savings, and streamlined workflows.