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Nishitha Jagarlamudi

Data Analyst • Hyderabad, India • n********************@gmail.com • +91*******928 • linkedin.com/••••• • nishithajagarlamudi.github.io/•••••

Professional Summary

Data Analyst with 0 years of experience leveraging SQL, Python, Power BI, and Excel to convert raw data into actionable business insights. Demonstrated ability to build end-to-end analytics workflows — data cleaning, aggregation, segmentation, and interactive dashboarding — through projects on customer segmentation, pricing analysis, and campaign performance reporting. Strong foundation in KPI tracking, exploratory data analysis, and communicating findings to inform targeting and retention strategies.

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Matplotlib,Seaborn
Databases: SQL,MySQL,MySQL Workbench,Joins,Subqueries,Common Table Expressions,Window Functions
Data and Analytics: Power BI Dashboards,Excel Charts,Power BI,Data Cleaning,Exploratory Data Analysis,Feature Engineering,Segmentation RFM
Tools and Methodologies: Jupyter Notebook,Git,GitHub,Microsoft Excel
Business Intelligence: DAX,Interactive Dashboards,KPI Reporting

Projects

Bike Sales Customer Analysis Dashboard | Jan 2026 – Mar 2026
Tools Used: Excel, Pivot Tables, Data Cleaning, KPI Reporting
  • Collected and cleaned a 1,000-row customer dataset by removing duplicates and imputing missing values to produce an analysis-ready table for segmentation.
  • Engineered an Age Brackets feature to enable behavioral segmentation and used pivot tables to compare conversion by cohort.
  • Built an interactive Excel dashboard with slicers and KPIs to track customer response rates and campaign performance.
  • Identified that Middle Age customers convert at 54.6% compared to 35.5% for Adolescents, informing age-targeted outreach.
  • Analyzed income impact on purchase decisions and found buyers earn on average $57,963 versus $54,875 for non-buyers, guiding income-based targeting.
Used Car Price Analysis Dashboard | Oct 2025 – Dec 2025
Tools Used: Power BI, Python, Data Integration, Dashboarding
  • Integrated data from four city listing sources into a single, standardized dataset of 3,765 records by resolving inconsistent formats and columns.
  • Developed a Power BI dashboard with city-level filters and brand KPIs to enable market comparison and monitor pricing trends.
  • Performed price-driver analysis and found a 57% fuel-type price premium, surfacing a major pricing factor for stakeholders.
  • Presented city-level price variation and brand performance metrics to support market-entry and pricing decisions.
  • Documented the analytics methodology and data transformations to ensure reproducibility and knowledge transfer.
E-Commerce Customer RFM Segmentation | Mar 2026 – May 2026
Tools Used: SQL, MySQL, RFM Segmentation, Customer Analytics
  • Designed and implemented an RFM segmentation pipeline in SQL over 93,357 records to score customers on recency, frequency, and monetary value.
  • Segmented customers into eight actionable groups and mapped each to tailored engagement strategies to increase campaign relevance.
  • Identified a 1.8% at-risk customer segment responsible for 3.4% of revenue, enabling targeted retention tactics.
  • Automated SQL scoring steps using window functions and CTEs to support repeatable segmentation runs.
  • Produced stakeholder-ready tables and segment definitions to inform marketing and retention workflows.

Education

SRKR Engineering College, Bhimavaram
B.Tech — Computer Science Engineering • Bhimavaram, India • Aug 2019 – Apr 2023

Certifications

SQL Silver Badge — HackerRank • Oct 2025
Data Analysis with Python — IBM SkillsBuild • Aug 2025
Certified Python Developer Associate — CPDA-24 — CPDA

Achievements

  • CodeChef Gold Streak Badge — 50 Days Coding Streak — May 2026: Maintained a 50-day consecutive coding streak on CodeChef, demonstrating consistency in practice and problem-solving.

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