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sauryayan/README.md

Hi there, I'm Sauryayan 👋

Data Analyst | Python, SQL, Power BI & Azure

I am a Data Analyst with a unique background—leveraging over 8 years of experience in civil engineering and project management to drive data-informed business decisions. I specialize in taking raw, messy data and transforming it into clear, actionable business intelligence.

I don't just write queries; I know how to ask the right business questions, manage complex workflows, and communicate technical findings to non-technical stakeholders.

🛠️ My Technical Stack

  • Languages: SQL (MySQL, PostgreSQL, Window Functions, CTEs), Python (Pandas, NumPy, Regex)
  • Data Engineering & Cloud: Azure (Blob Storage, Data Factory, Synapse Analytics)
  • BI & Visualization: Power BI (DAX, Data Modeling), Matplotlib, Seaborn
  • Data Analysis: Advanced Excel (Power Query, Power Pivot, XLOOKUP), Statistical Analysis

📊 Featured Projects

1. 🛒 Diwali Sales Exploratory Data Analysis

  • Objective: Analyze holiday retail data to evaluate demographic purchasing behaviors and regional sales performance.
  • Tools Used: Python (Pandas, Matplotlib, Seaborn)
  • Highlights: Engineered median target encoding to evaluate categorical correlations, utilized Pandas to aggregate demographic spending patterns, and developed multivariate visualizations to uncover the crucial gap between high transaction volume and high average order value.

2. 🛍️ Customer Shopping Behavior Analysis

  • Objective: Identify primary demographic purchasing drivers and uncover customer shopping patterns.
  • Tools Used: Python (Pandas), SQL, Power BI
  • Highlights: Engineered a data pipeline to clean and transform datasets using Pandas (handling missing values and formatting via Regex), utilized SQL to query purchasing patterns, and developed an interactive Power BI dashboard to visualize demographic trends.

3. 🎬 Box Office Financial Correlation Analysis

  • Objective: Analyze the correlation between movie production budgets, genres, and overall profitability.
  • Tools Used: Python (Pandas, NumPy, Seaborn)
  • Highlights: Conducted in-depth Exploratory Data Analysis (EDA) on extensive movie datasets, manipulating and structuring the data before visualizing financial and genre-based correlations using Seaborn.

📫 Let's Connect!

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  1. diwali-sales-eda-python diwali-sales-eda-python Public

    A comprehensive Exploratory Data Analysis (EDA) on holiday sales data using Python to uncover demographic purchasing patterns and actionable business insights.

    Jupyter Notebook 1

  2. ecommerce-customer-behavior-analysis ecommerce-customer-behavior-analysis Public

    An end-to-end data analytics pipeline analyzing 3,900 e-commerce transactions using Python, PostgreSQL, and Power BI to uncover insights on customer segmentation, discount dependency, and revenue o…

    Jupyter Notebook

  3. movie-box-office-analysis movie-box-office-analysis Public

    Beyond the Box Office: Analyzing the mathematical relationship between movie budgets, directors, and gross revenue using Python.

    Jupyter Notebook

  4. regex-data-cleaning-toolkit regex-data-cleaning-toolkit Public

    To level up my data cleaning skills, I took on a "Regex Gauntlet"—a complex, unstructured dataset where I had to write 30 specific Regular Expression patterns in Python to extract and format the da…

    Jupyter Notebook