# PSYC 201B: Statistical Intuitions for Social Scientists

## When and where? 
**Communication**: [**Slack**](https://psyc201b.slack.com/) | [**Github**](https://github.com/psyc201b)  
**Location**: Mandler 3545 (Crick Conference Room)  
**Lectures**: Mon+Wed 2:00-3:50pm  
**Lab**: Tues 5:00-6:50pm  

## About
As computers have become increasingly powerful modern statistical practice has changed, offering approaches that go far beyond standard methods taught in classic psychological statistics (Efron, Bradley, Hastie, Trevor {cite:year}`Efron2016`). And yet at the heart of these developments are just a handful of key ideas (Gelman, {cite:year}`Gelman2021`). This course is designed to help you interactively develop your own statistical intuitions about these ideas using the Python programming language. At the core of the class is a deep understanding of the General-Linear-Model (GLM) from which we’ll build-up additional concepts (e.g. linear-contrasts, mixed-effects-models) and connect to related ideas in machine-learning (e.g. resampling, cross-validation, regularization).  
**Requirements**: [PSYC 201A](https://canvas.ucsd.edu/courses/58741) or equivalent  
**Note**: this course will be taught in the *Python*, but experience in another language (e.g. R, Matlab) is sufficient

## Goals
- Build a strong foundation in statistics based on a deep understanding of the GLM
- Learn computational thinking, rather than statistical ritualizing - understanding the relationship between your analytic approach and what *inferences* are justified from first principles
- Develop *practical* Python programming, data analysis, and visualization skills
- Set you up for further coursework in advanced data-science, artificial intelligence, computational social science, or econometrics

## Acknowledgments

This course draws inspiration from numerous sources including:
- [Psych252: Statistical Methods by Tobias Gerstenberg](https://psych252.github.io/)
- [Statistical Thinking for the 21st Century by Russ Poldrack](https://statsthinking21.github.io/statsthinking21-core-site/index.html)
- [DartBrains by Luke Chang](https://dartbrains.org/content/intro.html#)
- [Foundations of Data Science @ UC Berkley](https://www.data8.org/fa24/)

*Course logo for this year created with the assistance of GenAI*

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