INTL
Freelancer
어려움
외주
원격 가능
Heart Rate Data Analysis Report
예산
$1,500~$12,500 INR
예상 기간
2~4주
난이도
어려움
기술 스택
Python
R
pandas
matplotlib
Seaborn
tidyverse
Excel
Power Pivot
Data Analysis
Data Visualization
Statistical Analysis
Data Cleaning
Time Series Analysis
AI 분석 요약
이 프로젝트는 대규모 심박수 데이터를 분석하여 시간 경과에 따른 안정 시 심박수, 심박수 변동성 통계 및 운동 심박수 패턴을 파악하는 것입니다. 원시 CSV/Excel 파일을 심층 분석하고, 방법론, 주요 발견 및 실질적인 함의를 담은 보고서, 정리된 데이터, 시각화를 제공해야 합니다. 데이터 분석, 통계 해석, 데이터 시각화 및 보고서 작성 역량이 요구됩니다.
프로젝트 원문 설명
I have a sizeable set of heart-rate data that needs a professional eye. The task is strictly analytical—I am not after personal training sessions or rehabilitation plans, but a focused review of the data itself. Your brief is to take my raw files (CSV / Excel export from a popular wearable) and turn them into a clear, insight-rich report.
Scope of analysis
• Resting heart rate trends over time
• Heart rate variability statistics and interpretation
• Exercise heart rate patterns, including peaks, recovery curves, and intensity zones
What I expect back
1. A concise written report (PDF or Word) explaining your methods, key findings, and practical implications.
2. Cleaned and annotated data files so I can trace every result.
3. Visualisations—time-series plots, variability charts, and any other graphics that highlight the story behind the numbers.
Use whatever toolkit you prefer—Python (pandas, matplotlib, Seaborn), R (tidyverse), Excel with Power Pivot, or specialised HRV software—as long as the final deliverables are reproducible and well documented.
Accuracy, clarity, and actionable insight matter more than page count. If this sounds like your domain, I’m ready to share the dataset and any additional context you require.
Scope of analysis
• Resting heart rate trends over time
• Heart rate variability statistics and interpretation
• Exercise heart rate patterns, including peaks, recovery curves, and intensity zones
What I expect back
1. A concise written report (PDF or Word) explaining your methods, key findings, and practical implications.
2. Cleaned and annotated data files so I can trace every result.
3. Visualisations—time-series plots, variability charts, and any other graphics that highlight the story behind the numbers.
Use whatever toolkit you prefer—Python (pandas, matplotlib, Seaborn), R (tidyverse), Excel with Power Pivot, or specialised HRV software—as long as the final deliverables are reproducible and well documented.
Accuracy, clarity, and actionable insight matter more than page count. If this sounds like your domain, I’m ready to share the dataset and any additional context you require.
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