Department of Psychology
Department of Psychology

Data Science Internship

The Department of Psychology will be funding summer internship opportunities for undergraduate students who are interested in working on developing their data science skills in a psychology research lab. This summer the program will be in residence and run from June 13th to Aug 12th. The internship will include:  

  • An opportunity for students to gain hands-on experience with a psychological data set with guidance from faculty and graduate students. Students will apply current skills and learn new ones. This will not include typical research assistant functions such as clerical work, data entry or running human subjects. 
  • There will be a weekly Friday lunch meeting with all fellow interns that will include professional development tips and discussion of topics in data science. 
  • Interns will be expected to devote 20 hours/week to their work. 
  • The internship will provide the student with a 2K stipend. 
  • Not open to graduating seniors. 

Before clicking on the application link, please write a paragraph that is no more than 500 words explaining why you wish to do a summer data internship and how it will help you in developing your career interests.  

If you wish to apply, please fill out the survey Summer of Data - Application 

Application due date: April 15, 2022 

Notification date: May 1st 

Start date: June 13th 

Examples of Internship Projects:

1) Imagining alternative worlds: Pretend play, learning, and exploration in childhood

Purpose: Analysis of videos of lab-based and naturalistic pretend play aimed at developing an open-access coding scheme

Skills Needed: R, data wrangling, basic understanding of mixed-effects models

Skills Likely Acquired: tidyverse, git/github, zotero, OSF, video coding (e.g., ELAN), databrary

Lab Website:

2) Neuroimaging Meta-Analysis in Depression

Purpose: We propose a neuroimaging meta-analysis that will synthesize the current literature on either risk factors for major depressive disorder or altered cognitive control in depression. The student will carry out a literature review, and carry out a meta-analysis using the Activation likelihood estimation (ALE) algorithm on neuroimaging studies (MRI, PET). The meta-analysis will be carried out using BrainMap, a database of published neuroimaging experiments, and BrainMap software.

Skills Needed: Excel, data wrangling

Skills Likely Acquired: Meta-analysis

Lab Website: 

3) Temporospatial Analysis of Animal Social Networks

Purpose: In our laboratory we track the movements of mice in a large complex environment using RFID tags. This provides us with time-stamped location data of every mouse in a social group over weeks. From this huge dataset, we can generate social networks of groups and track how these change over space and time. Our data science intern will generate a data science pipeline using multiple tools that takes raw tracking data, cleans it, converts it to social networks and then visualizes and analyzes it.

Skills Needed: Students should have basic to intermediate familiarity with R and RStudio. Students should have good knowledge of introductory statistical concepts and methods.

Skills Likely Acquired: Students will learn the following skills: advanced R programming methods, ability to collaborate on open projects via GitHub, statistical time-series analysis of temporospatial data, social network analysis, interactive visualization with Shiny.

Lab Website: 

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    The University of Texas at Austin
    SEA 4.208
    108 E. Dean Keeton Stop A8000
    Austin, TX 78712-1043