Behavioral Data Science Initiative

We aim to train a new generation of data-science-ready graduates grounded in a solid understanding of human psychology, knowledge of the ethical responsibilities of their skills and the technical toolkit to tackle the most ambitious data problems. Training in Behavioral Data Science will improve a student’s career prospects and the creation of a next-generation human-centric data science workforce has the potential to facilitate ethical progress while promoting the good of society. Indeed, a majority of what is commonly referred to as data science more specifically pertains to understanding, classifying, and predicting human behavior, emotions, and intentions. This is true for market and political research, medical and psychological interventions, AI-based support systems as well as medical imaging analysis. As a result, there is a critical need to develop a new curriculum to effectively infuse the data-science world with the much-needed understanding of humans’ psychology and behavior. 

The Behavioral Data Science Initiative aims to:  

• Create a course roadmap for the following areas: text and voice analysis, mining social media data, chartering behavior with GPS activity, data simulation and modeling, applied machine and statistical learning, and data extraction, organization, and visualization
• Develop a Bachelor’s of Science in Behavioral Data Science –possibly the first in the nation –and a Master’s in Behavioral Data Science within five years
• Connect undergraduate students to research labs at UT where they can put their data skills to work exploring real data through capstone projects
• Foster industry relationships in order to tailor our educational initiatives to the skills desired and to cultivate an internship referral program with industry experts

What is unique about Behavioral Data Science?

• Human centered approach: Deep understanding of the human psychological processes, strengths, and weaknesses. Understanding underlying psychological processes, concepts, and biases of human behavior. 
• From data to insight: Deep understanding and interpretation of behavioral data 
• From data acquisition to science: Ability to design and create studies to collect data instead of simply relying on pre-acquired datasets. 
• From theory to practice: Using model-based theory and data analytics skills to solve real-world problems.