Laura R.

Key Skills

Software

No software listed

Top Subject Matter

Music preferences and mental health/emotion analytics

Task management and mental wellness (mood and priority categorization)

Top Data Types

Text

Computer Code Programming

Top Task Types

Data Collection

Emotion Recognition

Freelancer Overview

The Power of Music on Mental Health (Capstone Project) — dataset preparation and KPI labeling for emotion/music analysis. Brings 24+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Tableau, Kaggle, and Mendeley. Education includes Associate of Applied Science, Gateway Technical College (2026). AI-training focus includes data types such as Text and labeling workflows including Data Collection and Emotion Recognition.

Labeling Experience

Task Flow — Task & Mental Wellness App — labeling mood signals and task priority for analytics

TextEmotion Recognition

The personal task and wellness app required categorizing and tracking user-reported emotional well-being signals for downstream analytics. Laura designed features that classify tasks by priority and connect mood tracking to productivity and mental-health trends. This effectively creates labeled emotion/mood data for visualization and interpretation. • Implemented mood-tracking inputs that serve as emotion/well-being labels. • Developed analytics views to visualize productivity and mental-health trends over time. • Designed task categorization (e.g., priority groupings) for structured analysis. • Built end-to-end functionality supporting collection and use of the labeled data.

2025 - Present

The Power of Music on Mental Health (Capstone Project) — dataset preparation and KPI labeling for emotion/music analysis

TextData Collection

The capstone project involved preparing and organizing textual/tabular datasets for analysis of music and mental-health patterns. Laura cleaned, merged, and transformed the data to define KPIs such as emotional response rate, valence impact, and genre distribution. She then used analytics outputs to support interpretation of relationships between emotions and music preferences. • Used Kaggle and Mendeley datasets as the source data. • Performed data cleaning and dataset merging to create analysis-ready inputs. • Defined KPIs for emotional and mental-health related metrics. • Visualized and presented results using Tableau to communicate findings.

2024 - 2025

Education

Work History