Course overview and structure
In this course you will work with environmental data and learn to apply basic concepts of statistical data analysis. You will use your computer/laptop and learn to solve statistical problems that are often found in atmospheric and environmental research. For example you will be working with computer programs and data examples that illustrate the following typical statistical problems: How can we summarize large data sets in visual graphs? Why is it important to have a large sample size when dealing with statistical data analysis methods? How can we detect climatic trends in global temperatures or local rainfall? When is a correlation significant? What is the idea behind statistical hypothesis tests? How do we make inferences from noisy and uncertain observational data? What signals can we detect in time series of atmospheric CO2 concentrations? This course will cover standard concepts in probability theory, univariate and multivariate statistical analysis methods, statistical description of data, visualization of data and the concepts of hypothesis testing, and time series analysis.
You will learn the basic principles in computer coding in Python (available for all common computer operating systems as free software). This year we will use a central Jupyter Hub and do the programming and statistical analysis with Jupyter Notebooks.
Data sets for direct access with our Python code:
- Unit1
- Survey results (Excel file) SHA-256 key:
- Survey results (CSV format)