Introduction#
Welcome to the Advanced data science Section!
Here would will build on the previous MQB sections to learn about important techniques in advanced data analysis and statistics.
Here is a list of the prerequisite computing and mathematics skills and topics you need to be at least somewhat familiar with:
Prerequisites#
Maximum Likelihood and Bayesian Methods in Ecology and Evolution#
Understanding and analysing functions (e.g., as a ``height’’ over a parameter space).
Probability Theory
Manipulating probability distributions (joint, marginal, conditional) - Integrating PDF’s.
Familiarity with key distributions (e.g., Gaussian, Binomial, Poisson).
Frequentist Hypothesis Testing
Understanding null and alternative hypotheses.
Interpreting p-values and test statistics.
Differential Equations
Setting up and analyzing first-order ordinary differential equations (ODEs).
Machine Learning / AI for Ecology and Evolution#
All of the above, and in addition,
Function Minimization and Optimization
Knowing what it means to minimize a function (e.g., sum of squared errors).
Understanding how parameters are searched in an optimization process.
Linear Algebra: Eigenvectors and Eigenvalues
Familiarity with the definitions and significance of eigenvectors and eigenvalues.
Computing and Software Installation Skills
Ability to troubleshoot R/Python environment setups.
Installing and configuring libraries like Keras/TensorFlow in Linux/UNIX.