SAURABH SINGH

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Data Visualization - Support Efficiency Infographic

Overview

Developed a 12″×12″ data visualization infographic in Python to evaluate the efficiency of a tech company’s customer support department (20 employees across 4 teams). Implemented a complete ETL pipeline to process ticket data (timestamps, channels, teams, ratings, resolution status) and derived metrics such as time-to-close, SLA compliance, resolution rate, and average customer rating.

Technical Features

  • Data Engineering: Preprocessed timestamps, calculated handling times, and aggregated metrics at both team and specialist levels using pandas and NumPy.
  • Visualizations: Built three distinct Matplotlib/Seaborn visualizations—
    • Time-series view of ticket volume vs. resolution latency.
    • Heatmap of team responsiveness by hour of day.
    • Correlation matrix and radar chart highlighting trade-offs between throughput, ratings, and resolution rates.
  • Design: Integrated all plots into a single cohesive infographic using Matplotlib gridspec, applied colorblind-safe palettes, direct annotations, and optimized figure layout for legibility.
  • Reproducibility: Delivered as a Jupyter notebook with reproducible pipelines and a build_infographic() function exporting high-resolution PNG/PDF outputs.

Tech Stack

  • Python
  • Pandas
  • NumPy
  • Matplotlib
  • Seaborn
  • Jupyter Notebook
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