Trinity Turner

Newly Graduated and ready to continue exploring what the world of data science has to offer! I have made use of 10+ modeling and algorithmic techniques and hope to apply them to find more real world solutions!

Data Science/Analyst Intern | Goodwill Industries - Charlotte, NC | 01/2025 - Present

• Created a database in SQL to concentrate data from multiple sources into a single database for the department.
• Goal: Enhance effectiveness of Goodwill Training Programs to increase postgraduate employability.
• Questions: 
• How can we maximize skill confidence in those enrolled in Goodwill Training programs from feedback in surveys?
• What features separate those able to find employment after graduation from Goodwill University from those who aren’t?
• Can we use trainee personal and survey data to isolate problem areas within the program and begin to aid them?
• Utilized SAS and Python libraries (K-means, Linear Reg. ) to identify and minimize hindering factors from trainee data.
• Increased r  by 2% (from 75% to 77%) by refining feature selection and reducing noise in trainee data.
• Used tools such as Power BI to create visual reports to investors for GoodWill University programs, expecting detailed insight on program success.

Mental Health App Development (Remote Internship) | Howard University-BRSP - Charlotte, NC | Data Science Researcher  | 05/2023 - 09/2025

• Goal: Use smart watch health data and user anecdotes data to inform those with mental disorders about themselves and possible triggers for prototype.

• Questions:
• To what extent do the combination of physiological indicators explain/align with proclaimed psychological disorders?
• Can we use user personal data to identify triggers of these behaviors, and possibly predict another event?
• Conducted in-depth research analyzing mental health data to support app development using data manipulation and analysis with Python.
• Created database using SQL and analyzed data using Python libraries, such as pandas and NumPy. Built and evaluated predictive & classifier models (e.g., linear regression, decision trees) using scikit-learn to identify key variables for app prototype.
• Predicted the general usefulness of variables and improved R² score by 20% (from 0.45 to 0.65) by optimizing feature engineering for mental health predictions.

Bachelors of Science: Data Science and Analytics