Data Analyst | SQL | Python | Power BI | Tableau | Business Intelligence | Open to Work

Suraj Sutar  • Peerlist

Delivered an end-to-end banking analytics solution on a dataset of 3000+ customer records, leveraging SQL, Python, and Power BI to transform raw data into actionable business insights. Engineered clean and reliable data by resolving encoding issues and designing derived metrics such as Total Balance and Engagement Flag, enabling accurate performance analysis. Developed advanced SQL queries to compute KPIs, perform customer segmentation, and analyze engagement using business-driven logic. Implemented window functions (RANK) to identify high-value customers. Performed exploratory data analysis (EDA) using Python (Pandas, Matplotlib, Seaborn) to uncover behavioral patterns and correlations across customer segments. Designed a multi-page interactive Power BI dashboard with KPI cards, filters, and navigation, enabling stakeholders to monitor deposits, engagement, and customer distribution in real time. Key Business Insights: • High-value customers contribute a significant share of total balance • Customers with multiple credit cards show higher engagement levels • Age group 25–40 represents the most active and valuable segment • Strong positive relationship between deposits and credit card usage Impact: Enabled data-driven decision-making by providing clear visibility into customer value, engagement trends, and financial behavior. Tools & Technologies: SQL | Python | Power BI | Excel

Banking Customer Analytics Dashboard | SQL, P

Delivered an end-to-end cryptocurrency analytics solution using real-time data from Binance and CoinGecko APIs, processing over 5,800 time-series records across multiple cryptocurrencies. Analyzed historical data spanning April 2025 to April 2026, covering major cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), XRP, and Cardano (ADA), including price (Open, High, Low, Close), trading volume, and market capitalization. Engineered a data pipeline by integrating OHLCV data from Binance with market data from CoinGecko, enabling comprehensive multi-source financial analysis. Performed data cleaning and transformation to structure time-series datasets, ensuring data quality and accuracy for downstream analysis. Conducted exploratory data analysis (EDA) using Python (Pandas, Matplotlib, Seaborn) to uncover trends, volatility patterns, and inter-asset relationships. Applied advanced financial metrics, including Sharpe Ratio and Maximum Drawdown, to evaluate risk-adjusted performance and market behavior. Developed an interactive Power BI dashboard featuring KPI cards, filters, and multi-page navigation to deliver dynamic insights into price trends, volatility, and comparative performance. Key Insights: • Bitcoin and Ethereum dominate overall market trends • High-volatility assets exhibit greater risk and deeper drawdowns • Strong inter-asset correlation limits diversification benefits • Large-cap cryptocurrencies provide superior risk-adjusted returns Impact: Enabled data-driven investment analysis by providing clear visibility into cryptocurrency performance, risk exposure, and market dynamics. Tools & Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn, Power BI, REST APIs (Binance, CoinGecko) Skills: Data Analysis | Financial Analysis | API Integration | Data Visualization | Business Intelligence

Cryptocurrency Market Analysis Dashboard

This project analyzes customer shopping behavior using SQL, Python, and Power BI to uncover purchasing patterns, customer segments, and revenue insights. The goal is to understand customer preferences and support data-driven business decisions for improving marketing and sales strategies. Tools & Technologies: SQL (MySQL) Python Pandas NumPy Matplotlib Seaborn Power BI Jupyter Notebook Project Objectives: -Analyze customer purchasing patterns -Identify high-revenue product categories -Understand customer demographics and spending behavior -Evaluate the impact of discounts and subscriptions on purchases -Segment customers based on purchase history -Build an interactive dashboard for business insights

Customer Behavior Analysis

• Performed Exploratory Data Analysis (EDA) on customer datasets to identify churn patterns and key influencing factors • Conducted data preprocessing, feature engineering, and statistical analysis using Python (Pandas, NumPy) • Created visualizations using Matplotlib and Seaborn to highlight churn drivers and retention risks • Generated insight reports to support customer retention strategies

Customer Churn Analysis

• Analyzed multi-year sales datasets using SQL to identify revenue trends and regional performance • Designed an interactive Power BI dashboard with KPIs, drill-downs, and dynamic filters • Automated data cleaning and transformation using Excel Power Query • Delivered actionable insights to support data-driven business decision-making