🚀 Excited to share my latest Data Analytics Project! 📊 Customer Behavior Analysis using Python, SQL & Power BI In this project, I analyzed customer purchasing behavior to uncover actionable insights and trends. 🔍 Key Highlights: • Data cleaning and preprocessing using Python • Performed Exploratory Data Analysis (EDA) • Used SQL for querying and insights extraction • Built an interactive Power BI dashboard 📈 Key Insights: • High-value customers contribute most of the revenue • Certain product categories perform better • Seasonal trends impact purchasing behavior • Discounts influence buying frequency 📊 The dashboard helps businesses make data-driven decisions and understand customer trends. 🔗 GitHub Repository: (https://lnkd.in/g9UZmNs8) #DataAnalytics #Python #SQL #PowerBI #DataScience #Portfolio #Projects

🚀 End-to-End Customer Shopping Behavior Analysis 🎥 Watch how this interactive dashboard uncovers key customer insights in seconds I worked on a complete data analytics project using Python, SQL, and Power BI to transform raw data into meaningful business insights. 🔍 What I did: • Cleaned and transformed 3,900+ records using Python • Performed SQL analysis in PostgreSQL • Built an interactive Power BI dashboard 📊 Key Insights: • Male customers generate 2x more revenue • 80% of customers are loyal repeat buyers • High-value customers still use discounts • Young Adults contribute the highest revenue 💡 Business Impact: These insights can help businesses improve marketing strategies, optimize discounts, and focus on high-value customer segments. 📌 Tools Used: Python | PostgreSQL | Power BI 🔗 Full Project (Code + SQL + Dashboard): https://lnkd.in/gSmEVwng Would love your feedback! #DataAnalytics #SQL #PowerBI #Python #BusinessAnalytics #DataScience #AnalyticsProject Amlan Mohanty

Transforming 3,900+ Rows of Shopping Data into Business Insights 📊 I recently wrapped up an end-to-end data analytics project focused on Customer Shopping Behavior. Using a mix of Python, SQL, and Power BI, I moved from raw, messy data to a strategic dashboard that answers critical business questions. The Workflow: 🔹 Data Cleaning (Python): Handled missing ratings and engineered features like age_group. 🔹 Deep Dive (MySQL): Ran complex queries to identify that "Loyal" customers are our largest segment. 🔹 AI-Assisted Optimization: Used Microsoft Copilot to double-check my logic, optimize query performance, and ensure my Python code followed PEP 8 best practices. 🔹 Visualization (Power BI): Built an interactive dashboard to track KPIs like a $59.76 average purchase amount. Key Insight: 💡 Our "Young Adult" demographic is currently leading in revenue, suggesting a huge opportunity for targeted loyalty programs in that bracket. Check out the full project on my GitHub: https://lnkd.in/dqva36CF #DataAnalytics #Python #SQL #PowerBI #DataScience #CustomerInsights #Portfolio

🚀 Excited to share my Data Analytics Project: Customer Shopping Behavior Analysis I analyzed 3,900+ customer transactions to understand spending patterns, customer segments, and product trends. 🔧 Tools Used: Python (Pandas) | SQL | Power BI 📊 What I did: Cleaned and processed raw data using Python Performed SQL analysis to extract business insights Built an interactive Power BI dashboard 💡 Key Insights: Young adults contribute the highest revenue Loyal customers form the largest segment Non-subscribers drive the majority of total sales #DataAnalytics #Python #SQL #PowerBI #DataScience

Excited to share my latest project on Customer Behavior Analysis Dashboard 🚀 Built using Python, PostgreSQL, and Power BI, this dashboard transforms raw customer data into meaningful insights that support data-driven decision-making. 🔹 Key Highlights: Data extraction & preprocessing using Python Structured storage and querying with PostgreSQL Interactive and dynamic visualizations in Power BI Customer segmentation based on purchasing patterns Trend analysis to identify high-value customers and retention opportunities 🔹 Impact: This project helps businesses better understand customer preferences, optimize marketing strategies, and improve overall customer experience. Always open to feedback and collaboration—let’s connect and build impactful data solutions together! 💡 #DataAnalytics #Python #PostgreSQL #PowerBI #CustomerInsights #DataVisualization #BusinessIntelligence

💬 Power BI Challenge of the Day Problem: Create a Power BI model that implements a Many-to-Many Relationship with Bidirectional Filtering. Use this relationship to calculate a measure that aggregates data from two separate fact tables based on common attributes. Query: You have two fact tables, 'Sales' and 'Expenses', connected to a common dimension table 'Product'. Implement a Many-to-Many Relationship between 'Sales' and 'Expenses' through 'Product'. Create a measure called 'Total Profit' that sums the 'Amount' from 'Sales' and subtracts the 'Amount' from 'Expenses' for each product. Answer: To solve this challenge, you need to establish a Many-to-Many Relationship between the 'Sales' and 'Expenses' tables through the 'Product' table. Then, create a measure using DAX that calculates the total profit for each product by aggregating the amounts from both fact tables. Explanation: By implementing a Many-to-Many Relationship with Bidirectional Filtering, you can effectively aggregate data from multiple fact tables based on common attributes without creating redundancy in your data model. The bidirectional filtering ensures that filters applied on one side of the relationship propagate to the other side, allowing for accurate calculations. #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data

Excited to share recent projects where I combined SQL, Python, and Power BI to turn raw data into actionable insights. 🚀 • Designed robust SQL data models to ensure clean, reliable data pipelines and faster query performance. • Built Python scripts for ETL automation, data validation, and feature engineering to support advanced analytics. • Developed interactive Power BI dashboards that highlighted key KPIs, trends, and root-cause analysis for stakeholders. These projects improved decision making by reducing report turnaround time and increasing data accuracy, enabling teams to focus on strategy rather than manual data work. I enjoy bridging the gap between data engineering and business storytelling, and I’m always looking for new challenges that require technical rigor and clear communication. 🔍📊 #SQL #Python #PowerBI #DataEngineering #Analytics #BusinessIntelligence

I didn’t want my first Data Analysis project to be just another tutorial copy. So I built everything from scratch. I created an entire e-commerce dataset (Shreeji Collection) — from structuring tables to generating 6.7K+ rows of transactional data — just to understand how real business data actually behaves. Then I worked on it like a real analyst: • Used SQL to break down revenue trends and customer behavior • Cleaned raw, inconsistent data using Python (Pandas, NumPy) • Transformed analysis into interactive dashboards using Power BI What stood out to me wasn’t the tools — it was how small patterns in data can explain real business decisions. This project changed how I look at data. Not as numbers — but as signals. Still learning. Still building. 🚀 #DataAnalytics #SQL #Python #PowerBI #Projects

𝐌𝐨𝐬𝐭 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐰𝐚𝐬𝐭𝐞 𝐦𝐨𝐧𝐭𝐡𝐬 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐰𝐫𝐨𝐧𝐠 𝐭𝐨𝐨𝐥𝐬. I did too. Here's the exact 11 tools that actually get you hired in 2026 👇 (Save this — you'll thank yourself later) 𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐨𝐮𝐭  → Excel Want to level up → Python + SQL Need to impress → Tableau or Power BI The rest? Inside the carousel. 𝐖𝐡𝐢𝐜𝐡 𝐭𝐨𝐨𝐥 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠? 𝐃𝐫𝐨𝐩 𝐢𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬. 𝐋𝐞𝐭'𝐬 𝐛𝐮𝐢𝐥𝐝 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 👇 ♻️ Repost this to help someone who's just starting their data journey. Follow Navya sri Kurapati🧑💻 for weekly data, analytics & career content Book a slot :- https://lnkd.in/gfqXGEnq #DataAnalytics #DataScience #Python #SQL #Tableau #PowerBI #Excel #DataAnalyst #LearnDataScience #CareerTips #LinkedInLearning #Analytics2026 #DataTools #TechCareer 

💬 Power BI Challenge of the Day ⚙️❓ **Question:** For the given scenario, create a DAX measure that calculates the total sales amount for each product category, considering only the top 3 products based on sales amount within each category. Assume you have a "Sales" table with columns: "ProductID", "Category", "SalesAmount". ⚡️ **Answer:** ```DAX Top3ProductSales = CALCULATE( SUM('Sales'[SalesAmount]), TOPN(3, VALUES('Sales'[ProductID]), CALCULATE(SUM('Sales'[SalesAmount])) ) ``` 🔍 **Explanation:** The measure first calculates the total sales amount for each product, then uses the TOPN function to filter and return only the top 3 products based on sales amount within each category. #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data