About Me
Hi! I'm Ashraf and I'm a data enthusiast diving into the world of data analytics.I focus on creating reports and visualizations for data-driven businesses.I’m laser-focused on creating business insights that drive decision making, and I help companies ⚡automate⚡ reports that connect to all their data.
Skills
Excel | Power BI | Microsoft Fabric | SQL | Python
Data Visualization
Business Analytics
Report Development
Presentation Skills
Featured Projects
POWER BI | PYTHON | End-to-End Customer Churn Analysis and Prediction
This end-to-end data analytics project identifies and predicts customer churn using a full data pipeline that combines SQL, Power BI, and machine learning (Random Forest)
EXCEL Automated and Interactive Sales Analysis System
This project addresses the challenges of scattered, unstructured sales data from multiple store locations and transforms it into a self-service analytics system—streamlining data entry, cleaning, statistical analysis, and interactive dashboard reporting
SQL | PYTHON | POWER BI | Sales & Customer Insights Dashboard with Sentiment Analysis
This project uncovers what drives conversion rates, how different types of content influence customer engagement, and highlights recurring feedback themes to support data-driven product and marketing decisions
Professional Certifications
My growing list of proprietary, exam-based certifications.
Microsoft Certified Power BI Data Analyst (PL-300) | Issued 2025
Microsoft Certified Faric Analytics Engineer (DP-600) | Issued 2025
Datacamp Data Analyst | Issued 2025
Microsoft Certified Azure Data Fundamentals (DP-900) | Issued 2025
EXCEL | Automated and Interactive Sales Analysis System
An end-to-end data analytics project that combines relational databases, Python-powered sentiment analysis, and Power BI interactive dashboards to extract actionable insights from sales, customer, and marketing data.This project uncovers what drives conversion rates, how different types of content influence customer engagement, and highlights recurring feedback themes to support data-driven product and marketing decisions.In today’s competitive business environment, retaining customers is crucial for long-term success. Churn analysis is a key technique used to understand and reduce this customer attrition. It involves examining customer data to identify patterns and reasons behind customer departures. By using advanced data analytics and machine learning, businesses can predict which customers are at risk of leaving and understand the factors driving their decisions. This knowledge allows companies to take proactive steps to improve customer satisfaction and loyalty.Project TargetCreate an entire ETL process in a database & a Power BI dashboard to utilize the Customer Data and achieve below goals:Visualize & Analyse Customer Data at below levels
Demographic
Geographic
Payment & Account Info
Services
Study Churner Profile & Identify Areas for Implementing Marketing Campaigns
Identify a Method to Predict Future Churners🔍 Project Overview
Customer and sales data were collected in Excel files and imported into a SQL Server database. After cleaning and exploratory analysis in SQL, the data was processed in Python (Jupyter Notebook) to perform sentiment analysis on customer reviews. Insights were then visualized through a Power BI dashboard, with a final presentation prepared for stakeholders in PowerPoint for easy interpretation.🧠 Key Features
- ✅ SQL-based ETL pipeline to clean and explore customer and sales data
- 🧹 Exploratory Data Analysis (EDA) in SQL on demographics, payments, services, and geographic patterns
- 💬 Sentiment analysis on customer reviews using Python (NLTK / TextBlob / custom logic)
📊 Interactive Power BI dashboards with slicers and visuals to:
Identify key factors affecting conversion rates
Determine which types of content drive the most engagement
Visualize sentiment trends and customer behavior
- 🎯 Actionable recommendations for marketing strategies and product improvements
- 📽️ Final insights delivered via a stakeholder-friendly PowerPoint presentation📈 Insights Delivered
- Key conversion influencers identified across regions and customer types
- Positive and negative review patterns summarized to guide service enhancement
- Data-backed recommendations to improve content strategy and engagement
- Clear prioritization of marketing campaign targets based on churn and sentiment⚙️ Tools & Technologies
SQL Server – Data import, cleaning, EDA
Python (Jupyter Notebook) – Sentiment analysis (TextBlob/NLTK)
Power BI – Dashboarding and visual analytics
Excel – Initial data source
PowerPoint – Final presentation for stakeholders🚀 Project Impact
- 📊 Enabled strategic marketing recommendations through multi-level conversion analysis
- 💬 Enhanced customer understanding via text mining of reviews
- 📉 Provided clear direction for content and product strategy backed by real data
EXCEL | Automated and Interactive Sales Analysis System
A complete end-to-end sales analysis and reporting solution built entirely in Microsoft Excel. This project addresses the challenges of scattered, unstructured sales data from multiple store locations and transforms it into a self-service analytics system—streamlining data entry, cleaning, statistical analysis, and interactive dashboard reporting.🔍 Project Overview
Manual reporting was time-consuming and error-prone, with sales data coming from various stores in inconsistent formats. This Excel-based solution automates the entire process:Data Entry: A user-friendly form restricts input to validated fields, reducing errors and ensuring consistency.
Automation: Behind-the-scenes data cleaning and processing happens instantly on form submission.
Analytics: Includes built-in statistical analysis (descriptive stats, trend analysis, t-tests) for deeper insight.
Visualization: An interactive dashboard enables users to explore sales trends, performance, and key metrics with just a few clicks.🧠 Features- ✅ Locked & Validated Sales Entry Form
- ⚙️ Automated Data Cleaning & Processing (VBA & Formulas)
- 📈 Built-in Descriptive & Inferential Statistics
- 📊 Dynamic Dashboard with PivotTables, PivotCharts & Slicers
- 🚀 80% Reduction in Manual Reporting Time📊 Dashboard InsightsThe dashboard enables users to:- Track revenue trends over time
- Compare store and regional performance
- Analyze sales growth and product category impact
- Explore actionable insights for pricing and inventory strategy🛠️ Tools & Technologies- Microsoft Excel
- Advanced Formulas
- VBA Macros
- PivotTables & PivotCharts
- Data Validation
- Applied Statistics
- Data Cleaning & Automation
- Dashboard Design📈 Impact- 🕒 Reduced reporting time by over 80%
- 📉 Minimized manual errors with validated data entry
- 🎯 Helped business teams make data-driven decisions faster
SQL | PYTHON | POWER BI | Sales & Customer Insights Dashboard with Sentiment Analysis
This project uncovers what drives conversion rates, how different types of content influence customer engagement, and highlights recurring feedback themes to support data-driven product and marketing decisions.🔍 Project OverviewCustomer and sales data were collected in Excel files and imported into a SQL Server database. After cleaning and exploratory analysis in SQL, the data was processed in Python (Jupyter Notebook) to perform sentiment analysis on customer reviews. Insights were then visualized through a Power BI dashboard, with a final presentation prepared for stakeholders in PowerPoint for easy interpretation.🧠 Key Features- ✅ SQL-based ETL pipeline to clean and explore customer and sales data
- 🧹 Exploratory Data Analysis (EDA) in SQL on demographics, payments, services, and geographic patterns
- 💬 Sentiment analysis on customer reviews using Python (NLTK / TextBlob / custom logic)
- 📊 Interactive Power BI dashboards with slicers and visuals to:
Identify key factors affecting conversion rates
Determine which types of content drive the most engagement
Visualize sentiment trends and customer behavior
- 🎯 Actionable recommendations for marketing strategies and product improvements
- 📽️ Final insights delivered via a stakeholder-friendly PowerPoint presentation📈 Insights Delivered
- Key conversion influencers identified across regions and customer types
- Positive and negative review patterns summarized to guide service enhancement
- Data-backed recommendations to improve content strategy and engagement
- Clear prioritization of marketing campaign targets based on churn and sentiment⚙️ Tools & Technologies
- SQL Server – Data import, cleaning, EDA
- Python (Jupyter Notebook) – Sentiment analysis (TextBlob/NLTK)
- Power BI – Dashboarding and visual analytics
- Excel – Initial data source
- PowerPoint – Final presentation for stakeholders🚀 Project Impact
- 📊 Enabled strategic marketing recommendations through multi-level conversion analysis
- 💬 Enhanced customer understanding via text mining of reviews
- 📉 Provided clear direction for content and product strategy backed by real data