Portfolio

# RESUME 👋 Hello!

I am excited to share my portfolio as a Data Analyst 👨‍💼, where I demonstrate proficiency in Python 🐍 and SQL for data manipulation and visualization. My technical skills include:

Technical Skills

• ✅ Data Analysis using pandas, NumPy, scikit-learn
• ✅ Visualization with matplotlib, Seaborn, Microsoft Power BI
• ✅ Statistical Analysis: hypothesis testing, regression analysis, A/B testing
• ✅ Data Cleaning with Advanced Excel
• ✅ Database Management: MySQL, MSSQL
• ✅ Cloud Computing Basics: AWS S3, Snowflake

On the soft skills front 💡:

• ✔️ Effective Communication
• ✔️ Problem Solving
• ✔️ Attention to Detail
• ✔️ Collaboration
• ✔️ Time Management

Projects

Ad-hoc Data Analysis

Data Pipeline: Constructed an end-to-end pipeline using AWS S3 and Snowflake for seamless data storage and analysis.

Data Cleaning: Implemented SQL-based cleaning processes to ensure data integrity before ingestion.

Visualization: Created dynamic dashboards in Power BI, enabling stakeholders to make data-driven decisions.

Healthcare Cost Analysis Using Insurance Data

Overview: Analyzed insurance data to understand factors affecting healthcare costs. Conducted data cleaning, exploratory analysis, and hypothesis testing to derive insights.

Methods and Techniques: Outlier detection, hypothesis testing (t-tests), visualization techniques.

Results and Insights: Identified correlations between age and charges, as well as differences in charges between smokers and non-smokers. Provided recommendations for promoting healthy living and awareness around smoking cessation.

Role and Contribution: Led data analysis, performed statistical tests, and generated visualizations to communicate findings effectively.

Credit Card Transaction Analysis

Overview: Analyzed credit card transaction data to derive insights into spending patterns, identify trends, and optimize business strategies.

Methods: Developed SQL queries to perform various analyses, including identifying top spending cities, determining highest spend months by card type, and analyzing transaction details for specific criteria.

Techniques Used: Aggregation, ranking, filtering, and cumulative calculations were employed to extract meaningful insights from the dataset.

Outcomes: Generated actionable insights such as identifying top spending cities, highest spend months for different card types, and analyzing spending patterns by expense type and gender.

Impact: Provided valuable insights to stakeholders for strategic decision-making, targeting marketing efforts, and optimizing business processes.

Feel free to explore these projects (links provided) that showcase my dedication towards delivering high-calibre results within stringent deadlines.

With an unquenchable thirst for learning new concepts in data analysis and staying updated with the latest trends 🔝 - I look forward to leveraging my skills further!

Thank you for your time! Looking forward to potential opportunities and fruitful discussions ahead 🤝

#DataAnalysis #Python #SQL #PowerBI #PortfolioReview