Yaswanth Rosannagari
College Graduate | Analyst • Bangalore • y***@gmail.com • +91*******723 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Data Analyst with 0 years of professional experience, including an internship focused on product and client-facing analytics. Skilled in Python, SQL, Pandas and Power BI, with hands-on experience cleaning and modeling datasets up to 200K+ rows and delivering executive dashboards. Experienced in exploratory data analysis, RFM-based customer segmentation and clustering using scikit-learn, and building KPI visualizations with Power BI and DAX. Worked on e-commerce and customer-behavior projects using SQL Server and SQL for ETL, aggregation and forecasting. Comfortable working in Jupyter Notebook and Google Colab and producing stakeholder-ready reports and Excel PivotTable summaries. Seeking an entry-level Data Analyst role where I can apply analytical rigor and dashboarding to improve product and marketing decisions.
Technical Skills
Programming Language: Python,SQL
Databases: Microsoft SQL Server
Version Control & Development Tools: Jupyter Notebook,Google Colab,Git
Data Analysis & Visualization: Pandas,NumPy,Matplotlib,Seaborn,Power BI,DAX,Exploratory Data Analysis
Machine Learning & AI: Clustering
AI/ML Frameworks & Libraries: scikit-learn
Project Management & Collaboration: PowerPoint
Marketing Analytics: RFM Analysis
Work Experience
Unified Mentor
Bangalore
Data Analyst Intern
Feb 2025 – Oct 2025
Provided data analytics and reporting for client-facing platforms; created dashboards and datasets used by product and leadership for retention and performance decisions.
Tech Stack: Python, Pandas, NumPy, Matplotlib, Power BI, DAX, SQL, Jupyter Notebook, Excel, Git
- Analyzed engagement trends across client-facing platforms using Python and Pandas; identified behavior patterns that increased insight accuracy by 45% for leadership decision-making.
- Designed and deployed interactive Power BI dashboards with DAX measures and drill-downs to monitor 20+ KPIs, enabling faster leadership decisions and reducing manual reporting effort.
- Structured and modeled 200K+ records in SQL by applying joins, normalization and indexing to improve query reliability and downstream reporting.
- Validated and cleansed source data by implementing transformation checks and Python scripts (NumPy), preventing recurring anomalies in reporting pipelines.
- Executed exploratory data analysis in Jupyter Notebook using Pandas and Matplotlib to surface retention drivers and inform targeted client interventions.
- Automated recurring ETL steps with SQL queries and Python scripts to streamline weekly reporting cadence and improve data readiness for analysts.
- Versioned analysis notebooks in Git and prepared executive summaries and PivotTable reports in Excel for stakeholder reviews.
Projects
Customer Shopping Behavior Analysis | Nov 2025 – Nov 2025
Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, Power BI, Jupyter Notebook, Google Colab, SQL
- Cleaned and engineered features on a 3.9K-record customer dataset using Python and Pandas in Jupyter Notebook and Google Colab, improving analysis readiness and reducing missing values by 35%.
- Performed EDA and SQL-based queries to uncover purchase patterns across gender, age groups and shipping types to inform segmentation strategy.
- Built RFM segmentation and implemented clustering with scikit-learn to classify loyal and returning customers, enabling targeted campaigns that increased revenue potential by 28%.
- Developed an interactive Power BI dashboard to visualize KPIs, purchase frequency and product performance for business stakeholders.
E-Commerce Analytical Hub | May 2024 – June 2024
Tools Used: SQL Server, SQL, Python, Pandas
- Integrated 23K+ transaction records and 5.6K+ customer records into SQL Server, applying deduplication and validation routines to improve data cleanliness and query performance.
- Authored optimized SQL Server queries to calculate sales trends, repeat purchases and revenue by category to accelerate insights for marketing and product teams.
- Automated demand forecasting and performance tracking with SQL aggregations and Python preprocessing to improve short-term planning precision.
- Identified high-value product categories and frequent shopper cohorts via cohort analysis, guiding promotions that increased potential revenue opportunity by 22%.
Education
Gandhi Institute of Technology and Management
Bachelor of Business Administration in Business Analytics • June 2019 – May 2022
Sri Chaitanya junior College
Board of Intermediate Education • June 2016 – April 2018
Certifications
Advanced Certification Program in Data Analysis (in collaboration with IBM) — EBSC Technologies Pvt. Ltd.
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