🚣 The Erg Log — Analytics Dashboard

📊 Descriptive Statistics Aggregation functions (mean, sum, min/max) compute KPIs from raw data. 💼 Executive dashboards · Operational KPIs · Anomaly detection See business application ↓

Business case: Just as these cards track rowing KPIs (total distance, avg pace, streak), the same approach builds executive dashboards that monitor revenue per customer (ARPU), conversion rates, and operational efficiency ratios — alerting when metrics breach thresholds.

Last Workout

28 Mar 2026

Days Since Last Workout

4 days

Total Workouts

Total Distance

503.55 km

Total Time

46.36 hrs

Avg Pace /500m

2:42.9

Avg Stroke Rate

28.9 spm

Avg Calories

398.0 cal

Personal Bests

Distance	Time	Pace /500m	Date
5000m	24:51.5	2:29.2	2026-03-02
6000m	33:13.2	2:46.1	2025-04-01
10000m	50:22.2	2:31.1	2026-02-14

📈 Time Series Analysis Temporal aggregation groups data into weekly/monthly buckets using Pandas groupby to reveal volume trends and seasonality. 💼 Revenue trending · Demand forecasting · Seasonal planning See business application ↓

Business case: Monthly/weekly volume charts here mirror how businesses track monthly revenue trends and seasonal demand patterns. Spot dips before they become problems — forecast next quarter’s demand and optimize inventory levels accordingly.

📅 Training Heatmap

A GitHub-style calendar showing my daily rowing volume. Darker green = more meters.

🗓️ Matrix Transposition & Heatmap Visualization NumPy matrix transposition maps daily values into a weeks × weekdays grid. Custom colorscale encodes intensity. 💼 User engagement patterns · Website traffic analysis · Activity monitoring See business application ↓

Business case: This heatmap reveals when I train most. For a business, the same visualization shows daily/weekly active users (DAU/WAU), peak session times by channel, and feature adoption rates — answering “when and how often do customers engage?”

📈 Pace Trend Analysis

📉 Linear & Polynomial Regression OLS linear regression and degree-3 polynomial fit model trends over time. R² measures goodness of fit. Rolling average smooths noise. 💼 Sales forecasting · Price prediction · Performance trajectory modeling See business application ↓

Business case: The regression line predicting my pace trend is the same math behind sales forecasting and price prediction models. R² tells you how reliable the forecast is. Use it to project growth trajectories and set data-backed targets.

Regression analysis reveals whether my pace is improving over time. Improving 0.12s /500m per month · Linear R² = 0.001 · Poly R² = 0.027

Model:

🎯 Workout Clusters (K-Means)

🎯 K-Means Clustering (Unsupervised ML) K-Means algorithm with StandardScaler feature normalization discovers natural workout groupings from distance, pace, and duration. Elbow method evaluates optimal K. 💼 Customer segmentation · Market basket analysis · User behavior profiling See business application ↓

Business case: K-Means groups my workouts into Sprint, 5K, 10K, etc. The same algorithm segments customers by CLV and behavior, builds RFM scores (Recency, Frequency, Monetary), identifies churn-risk cohorts, and powers market basket analysis to find cross-sell opportunities.

Machine learning groups my workouts into 5 categories based on distance, pace, and duration.

Sprint

3 workouts

Avg 1968m · 2:16.2 /500m · 10 min

5K Steady-State

30 workouts

Avg 5051m · 2:39.7 /500m · 27 min

Mid-Distance (5-10K)

2 workouts

Avg 7500m · 2:42.4 /500m · 40 min

10K Steady-State

26 workouts

Avg 10001m · 2:47.8 /500m · 56 min

Endurance 10K+

5 workouts

Avg 14223m · 2:53.3 /500m · 82 min

📊 Training Balance

🥧 Distribution Analysis Proportional analysis of cluster assignments reveals how training effort is allocated across categories. 💼 Portfolio allocation · Resource distribution · Market share analysis See business application ↓

Business case: The pie chart shows how my training is distributed. For business, this same analysis drives budget allocation efficiency, compares channel ROI, measures market share by segment, and identifies where capacity is over- or under-utilized.

What percentage of my workouts fall into each category?

💼 Business Applications

The same data science techniques powering this dashboard can drive value across industries. Here are KPIs and indicators I can build for your business:

📊

Performance Dashboards

Real-time executive scorecards with aggregated KPIs, trend indicators, and automated alerting on threshold breaches.

Revenue per customer (ARPU)
Conversion rates & funnel drop-off
Operational efficiency ratios
Goal attainment tracking

👥

Customer Segmentation

ML-driven clustering identifies distinct customer groups by behavior, value, and engagement — enabling targeted strategies.

Customer Lifetime Value (CLV)
RFM scoring (Recency, Frequency, Monetary)
Behavioral cohort analysis
Churn risk profiling

📈

Demand & Sales Forecasting

Time series models and regression analysis predict future demand, revenue, and resource needs with confidence intervals.

Monthly/quarterly revenue forecast
Seasonal demand patterns
Inventory optimization signals
Growth trajectory & R² confidence

🔍

Customer Activity Monitoring

Heatmaps and engagement analytics reveal when, how, and how often customers interact with your product or service.

Daily/weekly active users (DAU/WAU)
Session frequency & duration
Feature adoption rates
Engagement heatmaps by time & channel

🧠

Behavioral Analytics

Pattern recognition and trend analysis uncover what drives customer actions — from purchase triggers to churn signals.

Purchase propensity scoring
Cross-sell / up-sell opportunity detection
Customer journey mapping
Anomaly detection on behavior shifts

⚖️

Resource & Portfolio Optimization

Distribution analysis and optimization algorithms help allocate budgets, staff, and inventory where they matter most.

Budget allocation efficiency
Channel ROI comparison
Workload balancing metrics
Capacity utilization rates

Interested in leveraging these techniques for your business? 📧 Get in Touch