Warsaw University

November 2022 - January 2023

## Data Science for Quantitative Psychology and Economics

** Course Information**

**Introduction**

This course deals with a variety of data science techniques for dealing with experimental data from psycology and economics. These techniques are applied using the R programming language.

**Course Content**

The course will cover the following topics:

**Course Organisation**

The teaching schedule consists of 8 lectures.

The lectures are held Mondays 13.30 - 15.00 using Zoom. The dates are:

21st, 28th November,

5th, 12th, 19th December,

9th, 16th, 23rd January.

Meeting ID: 966 0389 4968

Passcode: 728156

Click here for invite link

**Grading Policy**

Assessment will be based on an assignment, relevant to the student's research interests, involving analysis of data and application of the techniques from the course using R.

**Course Notes**

**Data**
Click here for the directory containing the data files

**Resources** For a folder containing articles, information on R packages and other material, click here.

*(Last modified: 22nd November 2022 by John M. Noble)*

November 2022 - January 2023

This course deals with a variety of data science techniques for dealing with experimental data from psycology and economics. These techniques are applied using the R programming language.

The course will cover the following topics:

- The geometry of data; dimension reduction, principal component analysis, factor analysis (2 lectures).
- Cluster Analysis (1 lecture)
- Bayesian Networks, graphical models, causal probability calculus (2 lectures).
- Linear Discriminant Analysis (1 lecture)
- Bayesian Nonparametric Models: Chinese Restaurant Process, Indian Buffet Process (1 lecture)
- Introduction to Time Series: Trends, Seasonal Component, Stationary Component (1 lecture)

The lectures are held Mondays 13.30 - 15.00 using Zoom. The dates are:

21st, 28th November,

5th, 12th, 19th December,

9th, 16th, 23rd January.

Meeting ID: 966 0389 4968

Passcode: 728156

Click here for invite link

Assessment will be based on an assignment, relevant to the student's research interests, involving analysis of data and application of the techniques from the course using R.

- Click here for the course notes
- R file for geometry of data, principal components and factor analysis
- R file for cluster analysis
- R file for Bayesian Networks
- R file for Time Series
- R file for Dynamic Bayesian Networks (Multivariate Time Series)
- R file for Discriminant Function Analysis
- R file for Classification and Regression Trees (CART)
- R file for analysing discrete choice experiments