Experimental semantics - programming module 3501-KOG-SE-MPRG
1.Data and research project organization
Lesson 1. Why your research should be reproducible and how to do it?
- reproducibility in different stages of the research
- implementing automation in the research workflow (data collection, data cleaning, data analysis)
- practical aim: a student understands why proper organization of the research workflow is necessary in experimental sciences, she can also see the benefits on documenting and automation in different stages of the research.
Lesson 2. Introduction to Jupyter Notebooks
- Jupyter Notebook as a literal computing tool
- preparing experimental reports with Jupyter Notebook
- export Jupyter Notebooks to different formats (pdf, docx, html)
- practical aim: a student can create documents using Jupyter Notebook and can export it to popular formats
2 Doing things programmatically
Lesson 1. Programmatically controlling Limesurvey using API. Using jinja2 templates to automate creating of questionnaires
- introduction to XML-RPC interface and interacting with LimeSurvey using Python
- discussion on different API functions offered by LimeSurvey
- introduction to jinja2 template system
- practical aim: a student can upload a whole survey or its parts using simple Python scripts, she also is able to generate a survey or set of questions using jinja2 template system
Lesson 2. Using PsychoPy as a library for programming psychological experiments
- programming experiments in Python using PsychoPy as a library
- creating different types of stimuli and responses using PysychoPy
- controlling experiments (trials, loops, training and main sessions)
- Analysis and visualization of the data
Lesson 1. Introduction to pandas library
- basic data structures in pandas
- manipulating data frames with pandas
- exporting and importing data from and to pandas
- simple descriptive statistics
- practical aim: a student can read the data in different formats using pandas library and can perform basic operations on the data
Lesson 2. Short statistical reminder. Introduction to scipy and statsmodels libraries
- discussion on most popular statistical tests in experimental semantics
- performing most pupular statistical tests using Python libraries
- practical aim: a student can perform simple data analysis
Lesson 3. Introduction to libraries for data visualization: matplotlib and seaborn
- creating different plots using matplotlib library - histograms, bar plots, scatter plots and box plots
- creating different types of joint plots using pandas - violin plots, regression plots
- preparing data visualizations for publication/presentation
- choosing right visualization techniques
- practical aim: a student can create visualization of experimental data with both - exploratory and publication goals
Type of course
Mode
Prerequisites (description)
Assessment criteria
For every week there will be an assignment for the students (7 assignments total, 10 points max for each assigment). Final mark depends only on succesfully completing assignments.
0-35 - 2
35-50 - 3
50-60 - 4
60-70 - 5
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: