Transforming Shopping Data into Business Insights with Python and Power BI

Excited to share a data analytics project I recently worked on! 📊 Customer Shopping Behavior Analysis Tools: SQL | Python | Power BI In this project, I analyzed over 3,900 customer transactions using SQL, Python, and Power BI to uncover meaningful business insights and trends. Key contributions: • Performed data cleaning and feature engineering using Python (Pandas) • Designed and queried a PostgreSQL database using SQL • Built interactive dashboards in Power BI for data visualization Key insights: • Subscription customers spend 68% more and demonstrate higher loyalty • Female customers contribute slightly higher revenue • Express shipping users show higher average transaction values. This project enhanced my ability to translate raw data into actionable business insights. 🔗 Project Link: https://lnkd.in/gTmmXjbS #DataAnalytics #SQL #Python #PowerBI #BusinessIntelligence #DataDriven

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

https://lnkd.in/dc4xdegR Ever wondered what drives customer shopping behavior? 🤔 I explored this question by analyzing 3,900+ transactions across multiple product categories. Here’s what stood out: 📌 A small segment of customers drives the majority of revenue 📌 Non-subscribers contribute 73% of total sales 📌 Discounts significantly influence buying decisions for certain products 📌 Loyal customers dominate — but new customer acquisition is low To visualize these insights, I built an interactive dashboard using Power BI. 🛠 Tools used: Python, MySQL, Power BI This project helped me strengthen my skills in data cleaning, SQL analysis, and business storytelling. here the GitHub repo- https://lnkd.in/dc4xdegR Check out the presentation and let me know your thoughts! #DataAnalytics #SQL #Python #PowerBI #Learning #DataProjects

🚀 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

💬 Power BI Challenge of the Day ⚙️❓ **Problem:** Create a Power BI model with a Many-to-Many Relationship and implement Bidirectional Filtering to handle complex data relationships efficiently. 💡 **Answer:** To solve this challenge, you will need to create a Many-to-Many Relationship between two tables using an intermediate table and enable Bidirectional Filtering to propagate filters in both directions. ✨ **Explanation:** Many-to-Many Relationships in Power BI are common when multiple records in one table can be related to multiple records in another table. By using an intermediate table, you can establish this relationship. Bidirectional Filtering allows both tables to filter each other based on selections made in either table. 🛠️ **Example:** Assume you have tables: 'Products', 'Orders', and 'ProductOrders' as the intermediate table. 'Products' and 'Orders' have a many-to-many relationship through 'ProductOrders'. Enabling Bidirectional Filtering ensures that selecting a product filters related orders and vice versa. #Hashtags #PowerBIChallenge #PowerInterview #LearnPowerBi #LearnSQL #TechJobs #DataAnalytics #DataScience #BigData #DataAnalyst #MachineLearning #Python #SQL #Tableau #DataVisualization #DataEngineering #ArtificialIntelligence #CloudComputing #BusinessIntelligence #Data

"Ever thought about how data analytics is reshaping industries? In my journey, I've seen SQL, Power BI, and Python turn data into actionable insights. The global big data market is expected to reach $1,176.57 billion by 2034. Here's how you can leverage these tools: 1. Use SQL to query and manage data efficiently. 2. Create interactive dashboards with Power BI for real-time insights. 3. Automate data processes with Python to focus on strategy. What challenges have you faced in data-driven decision-making? #DataAnalytics #PowerBI #SQL #DataScience #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

Just wrapped up my first Power BI project. Analyzed a survey of 630 data professionals to see what the field actually looks like, and a few things genuinely surprised me: Python isn't just popular it's miles ahead of everything else. Data Scientists earn the most, but the gap isn't as massive as people think. Salary happiness scored 4.27/10. People are not satisfied with what they're making. Breaking in? 42% said "neither easy nor difficult." Honest answer, honestly. Built the whole thing in Power BI, cleaned the data in Power Query, wrote custom measures in DAX. Small project. Real learning. More coming. GitHub link in the comments 👇 #PowerBI #DataAnalytics #DataVisualization #Portfolio

🚀 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

The Tools That Turn Data Into Decisions 🧠📊 There’s a strange kind of silence in businesses that don’t use the right tools… A silence where data exists, but nothing truly speaks. Because data alone isn’t enough. Without the right tools, it stays scattered, misunderstood… almost invisible. And this is where everything shifts. Data analysis tools are not just technical assets — they are what give meaning to complexity, what turn raw information into something you can actually act on. 🔍 Tools like Excel help structure and explore data 📈 Power BI & Tableau transform numbers into powerful visual stories ⚙️ SQL allows you to dive deeper and extract exactly what matters 🐍 Python brings automation and advanced analysis to another level Each tool has a role. Each one uncovers a different layer of truth. Together, they don’t just show you what is happening… They show you why. ✨ They help you detect inefficiencies before they grow ✨ They reveal trends you wouldn’t notice otherwise ✨ They support faster, smarter, more confident decisions In a world driven by speed and competition, using the right tools isn’t a technical choice anymore… it’s a strategic one. Because at the end of the day, it’s not about having data. It’s about knowing how to make it speak. 🔥 #DataAnalysis #PowerBI #Tableau #SQL #Python #Excel #BusinessIntelligence #Analytics