Fitness Metrics and Wellness Surveys Analysis and Visualization
Budget: $250 – $750 USD
Project Description
We need an experienced data scientist/analyst to take our raw “pre” and “post” quantitative fitness data (body-composition, strength, power, endurance metrics across 22 sites/ 2 cohorts) and our wellness survey data (PSQI, MAAS, ESES, IPAQ, and Nutrition [modified REAPs] instruments) same n=500ish, clean and score everything per published protocols, run appropriate pre- versus post- analyses (paired tests, deltas, site-level comparisons), and produce professionally-styled charts/graphs. Final deliverable is a PowerPoint deck that mirrors our example report, with all visuals embedded and formatted for immediate presentation.
Key Deliverables
Fully cleaned & scored datasets (R or Python scripts included)
Statistical analysis outputs (summary tables, significance tests)
High-resolution charts/graphs (bar, line, box, scatter, distribution) broken out by site/cohort and overall
PowerPoint deck with all visuals inserted, styled to match provided example
Timeline
— Must start immediately and deliver all assets within 2 days of project award, extra incentives if faster.
Budget
Fixed-price bid; please include total and, if desired, milestones (e.g., 50% on data delivery, 50% on slides).
Skills Required
Data cleaning & preprocessing (R, Python, SAS or equivalent)
Psychometric instrument scoring (PSQI, MAAS, ESES, IPAQ)
Statistical analysis (paired t-tests, ANOVA, delta calculations)
Data visualization (matplotlib/ggplot2/Plotly or similar)
PowerPoint design & formatting
Public health / epidemiology background a strong plus
To Apply
Please provide:
Two examples of similar survey or fitness-data projects you’ve completed
Your approach (tools/language) and confirmation you can meet a 2-day turnaround
Any questions before getting started
Category & Subcategory
Data Science & Analytics › Data Cleaning & Processing
Presentation Design › PowerPoint
Skills Tags
R programming · Python · Data Cleaning · Survey Analysis · Statistics · Data Visualization · PowerPoint · Public Health · Epidemiology
We need an experienced data scientist/analyst to take our raw “pre” and “post” quantitative fitness data (body-composition, strength, power, endurance metrics across 22 sites/ 2 cohorts) and our wellness survey data (PSQI, MAAS, ESES, IPAQ, and Nutrition [modified REAPs] instruments) same n=500ish, clean and score everything per published protocols, run appropriate pre- versus post- analyses (paired tests, deltas, site-level comparisons), and produce professionally-styled charts/graphs. Final deliverable is a PowerPoint deck that mirrors our example report, with all visuals embedded and formatted for immediate presentation.
Key Deliverables
Fully cleaned & scored datasets (R or Python scripts included)
Statistical analysis outputs (summary tables, significance tests)
High-resolution charts/graphs (bar, line, box, scatter, distribution) broken out by site/cohort and overall
PowerPoint deck with all visuals inserted, styled to match provided example
Timeline
— Must start immediately and deliver all assets within 2 days of project award, extra incentives if faster.
Budget
Fixed-price bid; please include total and, if desired, milestones (e.g., 50% on data delivery, 50% on slides).
Skills Required
Data cleaning & preprocessing (R, Python, SAS or equivalent)
Psychometric instrument scoring (PSQI, MAAS, ESES, IPAQ)
Statistical analysis (paired t-tests, ANOVA, delta calculations)
Data visualization (matplotlib/ggplot2/Plotly or similar)
PowerPoint design & formatting
Public health / epidemiology background a strong plus
To Apply
Please provide:
Two examples of similar survey or fitness-data projects you’ve completed
Your approach (tools/language) and confirmation you can meet a 2-day turnaround
Any questions before getting started
Category & Subcategory
Data Science & Analytics › Data Cleaning & Processing
Presentation Design › PowerPoint
Skills Tags
R programming · Python · Data Cleaning · Survey Analysis · Statistics · Data Visualization · PowerPoint · Public Health · Epidemiology