What this resource explores
An archived continuation of introductory Python programming, applying computation to modeling, simulation and data analysis. Topics include stochastic processes, sampling, experimental data and introductory machine learning.
- Optimization, graph models and stochastic thinking
- Random walks, simulation, sampling and uncertainty
- Experimental data, clustering and classification
Who it is for
Learners building on introductory Python who want to use small programs to explore models and data.
Background & preparation
The syllabus lists 6.0001 introductory Python or instructor permission. The archived course uses Python 3.5.
A way to approach it
Connect computational models to random processes and sampling, then follow experimental-data analysis into clustering and classification. Use the lecture code alongside the explanations.
Edition & context
These are Fall 2016 teaching materials. Check historical software assumptions and distinguish learning exercises from a validated model for a particular dataset.