Outline of module, list of resources, etc. and a review quiz on computational skills covered in last year's Computational Tools and Environments.

This first (short) topic is a bit different from the other topics. Here we will look at the python data analytics library pandas and use it to reproduce the analysis of a simple data set.

In semester 5, we looked at a number of problems involving basic probability. In this module we will build on this and look at problems requiring more advanced treatment, in particular, the use of discrete and continuous probability distributions.

Cellular automata are simple, grid-based systems where each cell follows a set of rules to determine its state, typically 'on' or 'off', based on the states of its neighboring cells. Over time, the cells evolve, creating complex patterns from basic interactions.

Agent-based modeling (ABM) is a way of simulating complex systems by creating virtual 'agents', which are individual entities that follow simple rules and interact with each other and their environment. Each agent acts independently, making decisions based on its surroundings and goals, much like how people, animals, or even businesses behave in real life.

Markov-chains are models where the system can be in any one of distinct states and we have known, fixed probabilities (based on state) of transiting between various pairs of states.

Monte Carlo is a class of computational algorithms that are based on random sampling to compute numerical results.

Single point to access all assignments that contribute towards the module grade.