View Proposal

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Proposer

Drishty Sobnath

Title

Data-Enabled Mental Health: From Patterns to Interventions

Goal

The main aim of the project is to explore data science methodologies to identify meaningful patterns and insights within mental health data among young adults. By analyzing diverse datasets, the project aims to uncover trends, correlations, and predictive indicators related to mental health conditions.

Description

According to a new report from a project carried out by Harvard Graduate School of Education, young adults in the U.S. report twice the rates of anxiety and depression as teens. The report identifies a variety of stressors that may be driving young adults’ high rates of anxiety and depression. The proposed project can utilize a mixed-methods approach, including surveys, focus groups, and interviews, and use of machine learning and data science tools to predict or visualize patterns in young people's perceptions and experiences surrounding their mental health.

Resources

Python programming, Statistical modelling, Data Visualisation, Machine learning, Mental health, Data Science

Background

Ethical Approval Yes (if data is being collected, no if open datasets are used)

Url

Difficulty Level

Variable

Ethical Approval

Full

Number Of Students

1

Supervisor

Drishty Sobnath

Keywords

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Data Science
Master of Science in Information Technology (Software Systems)
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Postgraduate Diploma in Artificial Intelligence
Bachelor of Science in Statistical Data Science