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Detail-oriented Data Analyst with over 5 years of experience in analyzing complex datasets to deliver actionable business insights. Skilled in SQL, Python, Tableau, and Power BI for data analysis, visualization, and reporting. Proven track record of improving operational efficiency and driving data-driven decision-making in dynamic, fast-paced environments.
Data Analysis & Reporting
SQL (MySQL, PostgreSQL)
Data Visualization (Tableau, Power BI)
Python (Pandas, NumPy, Matplotlib)
Statistical Modeling
Advanced Excel (Macros, Pivot Tables)
ETL Processes
Machine Learning (Basic)
A/B Testing
Business Intelligence
FutureTech Analytics | San Francisco, CA | January 2054 – Present
Designed and maintained 15+ interactive Tableau dashboards, enhancing data accessibility across teams.
Analyzed user behavior metrics, increasing customer retention rates by 15%.
Built Python-based predictive models to forecast sales, achieving a 10% boost in revenue.
Automated data pipelines, reducing manual processing time by 20%.
Presented data-driven insights to C-level executives, influencing strategic business decisions.
NeoData Solutions | Austin, TX | June 2050 – December 2053
Conducted trend analysis of supply chain operations, leading to a 12% reduction in logistics costs.
Wrote complex SQL queries to extract and analyze datasets for monthly performance reviews.
Developed comprehensive Power BI reports, streamlining communication with stakeholders.
Collaborated with marketing teams to refine customer segmentation, improving campaign ROI by 18%.
Bachelor of Science in Data Science
University of Future Knowledge | San Francisco, CA | Graduated May 2050
Certified Analytics Professional (CAP) | January 2052
Google Data Analytics Professional Certificate | March 2051
Tableau Desktop Specialist | December 2050
Dynamic Sales Forecasting Tool
Designed a time-series forecasting tool using Python (ARIMA, Prophet) to predict sales trends, improving inventory management accuracy by 20%.
Customer Retention Dashboard
Created a Tableau dashboard to visualize customer churn rates, enabling targeted retention strategies that improved renewal rates by 15%.
Languages: SQL, Python (Pandas, NumPy, Matplotlib), R
Visualization: Tableau, Power BI, Advanced Excel
Databases: MySQL, PostgreSQL, Google BigQuery
Other Tools: Jupyter Notebook, AWS Redshift, Git
Available upon request.
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