1. Python basics

• intro-code-examples.ipynb
  Brief code examples discussed in the welcome lecture, and partly in the second lecture, to recapitulate the basics of how Python functions and how it is different from many other programming languages.
• intro-performance-comparison.ipynb
  Example for how we can measure and plot the performance and scaling behaviour of codes in comparison to each other.
• view-example.ipynb
  Illustrates the difference between creating a sublist (from a Python list) and a view (from a numpy array).

2. Data and objects

• book-index.ipynb
  Introductory example on object orientation in Python
• class-hierarchy-and-constructors.ipynb
  Inheritance, including calls to the parent classes' constructors
• dat121-website.json
  JSON-LD file representing an EducationEvent object for the DAT121 course

3. Regression basics

• autocorrelation-statsmodels.ipynb
  Compute the autocorrelation of a time series using statsmodels
• block-averaging.ipynb
  Block averaging for uncertainty estimation from a time series
• decorrelation.ipynb
  Illustrates the concept of the decorrelation time and how to compute it
• football.ipynb
  Some football data (for the lab worksheet)
• harmonic.ipynb
  Harmonic time series exhibiting long-timescale autocorrelation
• nok-eur.ipynb and nok.xml
  Application of regression and block averaging to the EUR:NOK exchange rate time series
• regression_data-from-lecture.ipynb
  Jupyter notebook containing the regression example from the lecture
• regression_random-data.ipynb
  Same as above, but using newly generated randomized data

4. Good practice

• biorealm.owl
  Biogeographic Area Ontology (BIOREALM) by Ung & Buttigieg (doi:10.1111/jbi.14631)

5. Multidimensionality

• linear-combination.ipynb
  Two-parameter, two-objective optimization problem
• pareto-front-visualization.ipynb
  Pareto front construction and visualization example (can be used as a template as described in the lecture)

Index