This repository contains coding examples and exercises for Arizona State University's course CAS 523 — Methods for Complex Systems Science: Statistics and Dimensionality Reduction.
Instructor: Bryan Daniels
Complex systems involve a large number of interacting components, and consequently observational studies of these systems typically generate data sets of high dimension. It is common, for instance, to be faced with data from hundreds or thousands of distinct neurons in a brain, genes in a cell, people in a society, firms in an economy, or texts in a corpus. To make sense of such data, a diverse set of tools has been developed that summarizes key properties at the population level (statistics) and characterizes predictable lower-dimensional patterns (dimensionality reduction).
This course will provide a guided tour of the tools of data analysis, focusing on those most relevant to complex adaptive systems. With a solid foundation in inferential statistics, we will encounter PCA-type linear projections, nonlinear manifold techniques, topic modeling, clustering methods, network statistics, and more. We will also explore more abstract foundations for how these methods work and when they fail, from information bottleneck theory to Bayesian model selection. Students will hone their data skills by applying state-of-the-art open-source software to real-world datasets.
This course will use coding examples and exercises written in the programming language Python. We will typically run Python code using interactive Jupyter notebooks, a convenient format that allows for running and saving Python code along with results that the code produces. This github repository contains a collection of Jupyter notebooks that we will use in class.
To run Python and Jupyter on your own computer, we recommend installing the Anaconda package, available for Windows, Mac, and Linux here: https://www.anaconda.com/products/distribution
If you already have a different favorite way of running Python code, feel free to use what you are most comfortable with. We will identify any dependencies on packages outside of Python and Jupyter so you will know exactly what you will need (likely standard data science packages included in Anaconda such as numpy, matplotlib, and pandas).
The simplest way to access the code in this repository is to download a zip file containing all its files by clicking the green "Code" button and then "Download ZIP" on the webpage for this repository: https://github.com/bcdaniels/CAS-523-Stats-and-Dim-Red Once you have downloaded the zip file, you will need to open it to "unzip" it, which will create a folder called CAS-523-Stats-and-Dim-Red-main. All code and data for the course are in this folder. Within the folder, you can open each notebook file using Jupyter to complete the python assignments. Note: The notebook files depend on other code that is within the downloaded folder, so you will need to leave the notebook files in the folder for them to work. You can move the entire CAS-523-Stats-and-Dim-Red-main folder elsewhere on your computer, but moving files within or out of that folder will lead to problems.
A perhaps better way to access the code is to use git, a widely-used tool for version control. This is not required for the course, but it is something you will definitely want practice using if you are aiming for a career involving any kind of collaborative coding. Running git clone https://github.com/bcdaniels/CAS-523-Stats-and-Dim-Red.git at a command line will create a subdirectory containing a copy of the code as a full git repository, with all the version control powers that entails. There are plenty of places to get introduced to the basics of git. A good start would be to review the basic git commands and the popular github workflow.