Excited to share my latest Data Analytics project where I built an interactive Customer Behavior Dashboard using Power BI! Project Highlights: Analyzed customer data to uncover purchasing patterns and behavior Performed data cleaning, transformation, and EDA using Python, Executed SQL queries (PostgreSQL) for deeper insights and Designed a dynamic Power BI dashboard for visualization. 📊 Key Insights: Total Customers: 648 Average Rating: 3.72 Average Purchase: $58.63 Subscription trends and category-wise revenue breakdown Sales & revenue analysis across age groups Tools Used: Python | SQL (PostgreSQL) | Power BI | Excel This project helped me strengthen my end-to-end data analytics workflow—from raw data to actionable insights and storytelling through dashboards. Open to feedback and suggestions! #DataAnalytics #PowerBI #SQL #Python #DataVisualization #BusinessIntelligence #PortfolioProject

🚀 Excited to Share My Latest Data Analytics Project! I have successfully built an Automated Data Cleaning & Outlier Detection Pipeline using Python and Power BI. 🔧 Project Highlights: • Developed an end-to-end automated data pipeline • Implemented data cleaning (duplicates, missing values, negative values) • Applied IQR-based outlier detection technique • Generated automated charts and data quality reports • Built an interactive Power BI dashboard for business insights • Designed a structured project workflow using modular Python scripts 📊 Tools & Technologies Used: Python | Pandas | NumPy | Matplotlib | YAML | Logging | Power BI 📈 Key Features: ✅ Automated raw data processing ✅ Dynamic visualization generation ✅ Outlier detection using statistical methods ✅ Clean project structure with modular pipeline ✅ Business-ready Power BI dashboards 🔗 GitHub Repository: https://lnkd.in/g8CUN7F2 📊 Power BI Dashboard Insights Included: • Revenue Trends • Top Countries Analysis • Product Performance • Customer Behavior • Data Quality Improvements This project helped me strengthen my skills in data preprocessing, automation, and visualization, which are essential for real-world data analytics roles. I look forward to feedback and suggestions from the community! #DataAnalytics #Python #PowerBI #DataCleaning #OutlierDetection #ETL #DataVisualization #AnalyticsProject #PortfolioProject

📊 Customer Behavior Dashboard | Power BI + Python + SQL Project Excited to share my latest Customer Behavior Dashboard, where I combined Python, SQL, and Power BI to analyze customer purchasing behavior and generate actionable business insights. This project demonstrates how raw data can be cleaned, analyzed, and transformed into interactive dashboards to support data-driven decision-making. 🔍 Key Insights from the Dashboard: • 👥 3.9K Customers analyzed • 💰 $59.76 Average Purchase Amount • ⭐ 3.75 Average Review Rating • 🛍️ Clothing category generated the highest revenue • 📈 Young Adults contributed the highest sales among age groups • 📦 Interactive filters for Subscription Status, Gender, Category, and Shipping Type 🛠️ Tools & Technologies Used: • 🐍 Python (Data Cleaning, EDA using Pandas & Matplotlib) • 🗄️ SQL (Data querying & business insights extraction) • 📊 Power BI (Dashboard development & visualization) • ⚡ DAX (KPIs & calculated measures) • 🔄 Power Query (Data transformation) 📌 Key Skills Demonstrated: • Data Cleaning & Preprocessing • SQL-based Data Analysis • Exploratory Data Analysis (EDA) • Dashboard Design & Data Storytelling • Business Insights Generation 🔗 GitHub Repository: https://lnkd.in/gNaTHYba I’m actively building end-to-end data analytics projects to strengthen my portfolio. #PowerBI #Python #SQL #DataAnalytics #DataAnalyst #BusinesAnalysis #DataVisualization #learningjourney #PowerQuery #AnalyticsPortfolio #DataScience

💬 Power BI Challenge of the Day 📝❓ Question You are working on a Power BI project that involves handling complex relationships. The dataset includes a many-to-many relationship between two tables, and you need to implement bidirectional filtering to accurately analyze the data. How would you approach setting up bidirectional filtering for many-to-many relationships in Power BI? 💡 Answer To set up bidirectional filtering for many-to-many relationships in Power BI, you can use DAX functions like "USERELATIONSHIP" and "CROSSFILTER" to specify the direction of the relationship and enable bidirectional filtering between the tables involved in the relationship. ✨ Explanation When dealing with many-to-many relationships in Power BI, bidirectional filtering is essential for proper data analysis. By using DAX functions like "USERELATIONSHIP" and "CROSSFILTER," you can establish the necessary relationships between tables and ensure that filtering works correctly in both directions. This approach helps in handling complex relationships effectively in Power BI. 🛠️ Example (for ease of understanding) ```DAX EVALUATE SUMMARIZECOLUMNS ( 'Table1'[Column1], 'Table2'[Column2], "Total Sales", SUM('Sales'[Amount]), "Total Quantity", SUM('Sales'[Quantity]) ) USERELATIONSHIP ( 'Table1'[Column1], 'BridgeTable'[Column1] ) USERELATIONSHIP ( 'Table2'[Column2], 'BridgeTable'[Column2] ) CROSSFILTER ( 'Table1'[Column1], 'BridgeTable'[Column1], BOTH ) CROSSFILTER ( 'Table2'[Column2], 'BridgeTable'[Column2], BOTH ) ``` In this example, we are setting up bidirectional filtering for a many-to-many relationship between 'Table1', 'Table2', and 'BridgeTable' using DAX functions to ensure accurate data analysis. #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data

Supply Chain Analysis Project | Python + SQL + Power BI I’m excited to share my recent project on Supply Chain Analysis, where I analyzed sales and logistics data to uncover key business insights and performance trends. Project Highlights: • Built an end-to-end data analysis workflow • Performed SQL-based analysis using SQLite within Python • Created an interactive Power BI dashboard Tools & Technologies: Python (Pandas) | SQL (SQLite) | Power BI Key Insights: • The West region generates the highest sales • Technology category contributes the most revenue • A small number of products drive a large share of total sales • Standard shipping mode is the most frequently used Dashboard Features: • KPI Cards (Total Sales, Profit, Orders) • Sales Trend Over Time • Sales by Region & Category • Segment & Shipping Analysis • Top 10 Products by Sales What I Learned: This project helped me understand how combining Python, SQL, and data visualization can deliver actionable insights for supply chain and business optimization. GitHub Repository: https://lnkd.in/gYSGG8pu I’d love to hear your feedback and suggestions! #DataAnalytics #SupplyChain #PowerBI #Python #SQL #BusinessIntelligence #AnalyticsProject #LearningJourney

🎯 Functions used by data analyst 🎯 📊 Data Cleaning & Transformation: Using SQL, Excel, and Python (Pandas) to prepare and clean datasets. No insights without clean data! 📈 Exploratory Data Analysis (EDA): Leveraging Python, R, or Power BI/Tableau to explore patterns, trends, and outliers. 📌 Data Visualization: Creating interactive dashboards with Tableau, Power BI, or Looker to tell compelling stories. 🧠 Statistical Analysis: Applying hypothesis testing and regression for deeper insights. 📥 Data Extraction: Writing complex SQL queries to pull data from PostgreSQL or MySQL. 💬 Communication: Turning insights into reports for teams using PowerPoint, Notion, or Confluence. 💡 Whether it’s solving business problems or optimizing processes, data is at the center of decision-making. 📌 Save this post for your next study session. 💬 Comment "DATA" if you want the PDF version! 🔁 Repost to help others in your network grow! 📌All credit goes to the original creator of the material, Shared here for learning purposes only. #DataAnalytics #SQL #PowerBI #Python #Tableau

New Project: Customer Shopping Behavior Analysis -- Analyzed 3,900 customer transactions using Python, SQL, and Power BI to identify patterns in: ✔ Customer segmentation ✔ Subscription behavior ✔ Product preferences ✔ Discount dependency ✔ Revenue drivers Key work completed: -- Cleaned and transformed raw data using Python -- Loaded data into PostgreSQL for business analysis -- Wrote SQL queries to solve business questions -- Built an interactive Power BI dashboard -- Provided strategic recommendations for retention and revenue growth -- One interesting finding: Subscribers showed stronger spending behavior than non-subscribers, indicating potential growth through loyalty programs. Tools Used: Python | PostgreSQL | Power BI Feedback from data professionals is welcome. #DataAnalytics #Python #SQL #PowerBI #BusinessIntelligence #DataAnalyst #AnalyticsPortfolio #Hiring #Data #DataAnalysis #Business

🚀 Just Completed: Customer Behavior & Sales Analysis Project I recently worked on an end-to-end data analysis project focused on understanding customer behavior and uncovering actionable business insights. 🔍 What I did: Built a full ETL pipeline using Python for data cleaning and preprocessing Performed feature engineering (age segmentation, purchase frequency transformation) Stored and managed data in a MySQL database Wrote advanced SQL queries to extract key insights Developed an interactive Power BI dashboard 📊 Key Insights: Identified high-value customers who use discounts but still spend above average Found top-performing products based on customer ratings Analyzed the impact of subscription models on revenue Compared spending behavior across shipping types and age groups 💡 Business Impact: Opportunities to increase subscription conversion Better targeting of high-value customer segments Data-driven decisions for product and pricing strategies 🛠️ Tools Used: Python | MySQL | Power BI | Excel This project reflects how raw data can be transformed into meaningful insights that drive smarter business decisions. you can check out the full project on my GitHub repository here: https://lnkd.in/dspuEz_E #DataAnalysis #DataAnalytics #SQL #PowerBI #Python #BusinessIntelligence #DataScience #ETL

💬 Power BI Challenge of the Day Problem: You are working on a Power BI project that involves optimizing the performance of DAX measures. One of the key areas for improvement is the usage of variables in DAX calculations. Create a complex DAX measure that incorporates multiple variables for better readability and performance. Query: Create a DAX measure named "Total Sales with Discounts" that calculates the total sales amount after applying discounts. Use variables to store intermediate results for better optimization. 💡 Answer ``` Total Sales with Discounts = VAR TotalSales = SUM('Sales'[SalesAmount]) VAR TotalDiscount = SUM('Sales'[DiscountAmount]) VAR TotalSalesDiscounted = TotalSales - TotalDiscount RETURN TotalSalesDiscounted ``` ✨ Explanation: In this DAX measure: - `TotalSales` calculates the sum of sales amount. - `TotalDiscount` calculates the sum of discount amount. - `TotalSalesDiscounted` subtracts the total discount from total sales to get the final sales amount after discounts. Using variables improves readability and can optimize performance by reducing redundant calculations. 🛠️ Example Total Sales with Discounts = VAR TotalSales = SUM('Sales'[SalesAmount]) VAR TotalDiscount = SUM('Sales'[DiscountAmount]) VAR TotalSalesDiscounted = TotalSales - TotalDiscount RETURN TotalSalesDiscounted #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data

Most people clean data. I built a pipeline that does it automatically, loads it into PostgreSQL, and visualises it in Power BI — end to end. Here's what my latest project covers 👇 I analysed 3,900 retail customer records to answer 10 real business questions: — Do subscribers actually spend more? — Which products get discounts the most — and does it hurt revenue? — Who are the loyal customers driving the most revenue? The answers were surprising. 🔧 What I built: → Python ETL pipeline with group-aware null imputation & feature engineering → PostgreSQL database loaded via SQLAlchemy (CTEs, Window Functions, subqueries) → Interactive Power BI dashboard with live KPIs 📊 Key finding: A significant segment of discount users still pays above the average purchase amount — discounts don't always mean lower margins. This is the kind of analysis I'd bring to any data team from day one. 🛠 Python · SQL · PostgreSQL · Power BI · SQLAlchemy Github:-https://lnkd.in/eDQusQWq #DataAnalyst #DataAnalytics #Python #SQL #PowerBI #PostgreSQL #ETL #Pandas #OpenToWork #DataPortfolio #PortfolioProject #BuildInPublic #LondonJobs #TechCareers #DataDriven #BusinessIntelligence #GitHub #DataScience #CareerInData #HiringAlert