Karan Sonawane

Dynamic Senior Technical Editor turned Data enthusiast at Packt Publishing Pvt. Ltd. with skills in Power BI, Python, and data transformation. Achieved a 50% reduction in editorial time through automation and enhanced stakeholder satisfaction by resolving 95% of technical queries swiftly. Passionate about driving efficiency and delivering high-quality, impactful data solutions.

• Received the Expert Performer Award twice — in 2021 and 2024 — for exceptional performance and process optimization.
• Achieved a 4.7-star average rating across Amazon & Kindle listings, reflecting consistent content quality and reader satisfaction.
• Automated manual editorial processes, reducing editorial time by 50% and improving project satisfaction scores through enhanced efficiency and accuracy.
• Achieved 4.7-star average ratings across Amazon & Kindle listings

Track down Customer Churn - PowerBI Dashboard

• Objective: Analyze churn patterns for a telecom company.
• Approach: Built a comprehensive data model to support flexible analytical queries, applied DAX to calculate churn rates, average tenure, and revenue loss, and conducted exploratory data analysis (EDA) to identify key patterns and trends.
• Outcome: Identified churn hotspots, enabling strategies that reduced churn by 12% and improved customer retention by 8% within six months. Decreased reporting time by 30% through optimized data modeling.
• Tools & Techniques: Power BI, DAX, EDA, Dashboarding, Data Modeling, Data Visualization
  
  Workforce Navigator - HR Analytics Dashboard (PowerBI)
• Objective: Create a data-driven HR reporting system.
• Approach: Transformed raw data using Power Query, developed an interactive dashboard highlighting turnover, satisfaction, and hiring rates.
• Outcome: Delivered actionable insights, enabling a 5% improvement in employee retention and reducing hiring costs by 10%. Improved leadership decision-making speed by 20% with dynamic filtering and drill-down capabilities.
• Tools & Techniques: Power BI, Power Query, Bookmarks, Dashboarding, Data Transformation, Data Visualization, KPI Tracking
  
  LoanFit Eligibility Check - Python & Colab
• Objective: Automate loan eligibility checks.
• Approach: Cleaned and visualized data, then implemented classification models including KNN, Decision Tree, and Random Forest to predict loan eligibility.
• Outcome: Achieved high prediction accuracy of 92% using Random Forest, minimizing manual eligibility checks and improving processing efficiency by 40%. Resolved over 100+ data anomalies, improving model reliability.
• Tools & Techniques: Python, Colab, Pandas, NumPy, Scikit-learn, Data Cleaning, Data Visualization, ML Classification Models, Feature Engineering
  
  BikeTrack-Pro - SQL & Tableau
• Objective: Analyze bike sales performance.
• Approach: Designed a relational schema, transformed data, and developed an interactive Tableau dashboard with drill-down capabilities for granular insights.
• Outcome: Enabled deeper sales insights, empowering regional sales teams to identify top-performing product categories, contributing to a 10% revenue boost.
• Tools & Techniques: SQL, Tableau, Dynamic Dashboarding, Data Transformation, Joins & Subqueries, Trend Analysis, Sales Performance Tracking

• IBM Python 101 for Data Science
• Career Essentials in Data Analysis
• Oracle Cloud Infrastructure 2024, Generative AI certified professional

• Enjoys producing and creating music, merging technology and creativity