Sabbir Hossain - Data Analyst | ContraWork by Sabbir Hossain
Sabbir Hossain

Sabbir Hossain

Data Analyst |Turning Data into Actionable Business Insights

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Dhaka, Bangladesh

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Dhaka, Bangladesh

Cover image for Global Terrorism Data Analysis &
Global Terrorism Data Analysis & Power BI Dashboard Project Description An end-to-end data analytics project focused on analyzing global terrorism data to identify trends, patterns, and regional variations across countries and time periods. What I did: • Cleaned and explored the dataset using Python • Performed data transformation and analysis using Python & SQL • Stored and queried structured data using PostgreSQL • Conducted analytical queries to identify trends and patterns • Built an interactive Power BI dashboard • Created KPI cards, maps, trend analysis, and comparative visualizations • Added dynamic filters for decade, year, country, region, and attack type Key Areas Analyzed: 🌍 Attacks by country and region 📅 Yearly and decade-wise trends ⚠️ Attack severity distribution 🎯 Attack type analysis 📊 Regional comparisons 🗺️ Geographic distribution of attacks Tools: Python • PostgreSQL • SQL • Power BI Workflow: Raw Data → Python → PostgreSQL → SQL Analysis → Power BI → Interactive Insights Outcome: The project demonstrates an end-to-end analytics workflow, transforming a large and complex dataset into an interactive dashboard that makes trends, geographic patterns, attack types, and severity levels easier to explore and understand.
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Cover image for E-Commerce Sales & Profit Analysis
E-Commerce Sales & Profit Analysis | Power BI Dashboard Project Description An end-to-end e-commerce analytics project focused on understanding sales performance, profitability, customer behavior, and regional trends. I analyzed 5,000+ orders to identify key business trends and performance drivers. Key Areas Analyzed: 📈 Total Sales & Profit 📅 Monthly Sales Trends 👥 Customer Segment Performance 🌍 Regional Sales Analysis 📦 Product Performance 💰 Profit Margin by Category 💳 Payment Method Analysis What I did: • Cleaned and prepared the dataset using Excel • Performed SQL-based analysis using PostgreSQL • Built an interactive Power BI dashboard • Created KPIs and visualizations to track business performance • Analyzed sales, profit, customers, products, and regional performance • Generated business insights from the analyzed data Tools: Excel • PostgreSQL • SQL • Power BI Workflow: Raw Data → Excel → PostgreSQL → SQL Analysis → Power BI → Business Insights Outcome: The dashboard provides a clear view of e-commerce performance and helps identify sales trends, profitable categories, customer segments, and areas for business improvement.
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Cover image for Customer Sales Intelligence & Profitability
Customer Sales Intelligence & Profitability Analysis Project Description I analyzed 10,000 orders and 1,498 customers to identify the customers, products, and segments driving sales and profitability. Key Insights: 📈 High-value customers contributed approximately 59.6% of total sales ⚠️ 267 At-Risk customers had previously generated approximately 129.82M in sales, highlighting a strong win-back opportunity 💰 High sales did not always mean high profitability 📊 Compared product-level sales, profit, and profit margins to identify more profitable opportunities 🎯 Used customer segmentation and RFM analysis to identify valuable and at-risk customers What I delivered: • Data cleaning and exploratory analysis using Python • Data storage and analysis using PostgreSQL & SQL • Customer segmentation and profitability analysis • A 4-page interactive Power BI dashboard • Business recommendations based on customer and product performance Tools: Python • PostgreSQL • SQL • Power BI • Excel Workflow: Raw Data → Python → PostgreSQL → SQL Analysis → Customer Segmentation → Power BI → Business Insights Outcome: The analysis transformed raw sales data into actionable insights for customer retention, win-back campaigns, product profitability, and revenue growth.
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Cover image for Advanced HR Workforce & Employee
Advanced HR Workforce & Employee Attrition Analytics An end-to-end HR analytics project focused on understanding employee attrition, workforce patterns, employee experience, and retention-related factors using a fictional HR dataset. What I did: Cleaned, explored, and transformed data using Python Performed data quality checks and advanced analysis using PostgreSQL & SQL Developed KPIs and attrition metrics using DAX Built an interactive Power BI dashboard Analyzed attrition by department, job role, overtime, salary, age, tenure, and promotion history Created employee risk segmentation to identify higher-risk groups Explored multi-factor relationships influencing employee attrition Tools: Python • PostgreSQL • SQL • Power BI • DAX • Excel Workflow: Raw Data → Python → PostgreSQL → SQL Analysis → Power BI → Business Insights Outcome: The project demonstrates an end-to-end data analytics workflow and transforms raw HR data into actionable insights that can support workforce and retention decisions.
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