Data analysis in earth science

Objectives:
The overall goal of the course is to be a direct contributor to calculations and analyses to be included in the student's MSc or PhD thesis. A secondary goal is that the student learns to do basic calculations and analyses with the software programming language Python.

Content:
The teaching is focused on exercises using Python software. Through applications the students are presented to basic concepts and problems of data analysis in general (variables, significance, confidence, hypothesis testing, p-value, statistical test methods, model choice, experimental design, etc.) The course includes time series analysis as well as analysis on spatial data. The weighting of these topics will depend on the students background . At the end of the course the students deliver a term paper. Here, it is recommended that the students are using data from their own MSc or PhD work.

On completion of the course the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• can elaborate on fundamental terms and problems in data analysis
• can discuss which techniques are suitable on different types of data
• can show insight with respect to the functionality of Python software

Skills

The student

• can do basic computations and analyses using the Python software
• can summarize observations, data, and methodological principles orally and in writing
• can interpret and make decisions based on results from the computations and analyses

General competence

The student

• can communicate the importance of applying advanced methods and software, within a specialized work environment and in a general context
• can apply and combine different types of computer programs to solve a complex task
• can demonstrate ability to function well individually and in a team