Customer Behavior Data Analytics Project with Python SQL Power BI

🚀 From Raw Data to Insights: My Journey into Data Analytics with Power BI Most data is useless… until you ask the right questions. Over the past few weeks, I’ve been diving deep into Power BI and Data Analytics, and here’s what I realized: 👉 It’s not about charts. 👉 It’s about solving business problems. 📊 What I’ve built so far: • Interactive dashboards • Data cleaning using Power Query • Data modeling with relationships • DAX measures for dynamic insights 💡 One interesting insight I discovered: Small changes in data visualization (like using the right chart or filter) can completely change decision-making. 🧠 Skills I’m sharpening: Power BI | SQL | Excel | Python (Pandas, NumPy) | Data Visualization 📌 My goal: To become a Data Analyst who doesn’t just show data—but tells stories with it. 💬 I’d love your feedback: What’s one skill every Data Analyst should master in 2026? #PowerBI #DataAnalytics #DataScience #BusinessIntelligence #SQL #Python #LearningInPublic #CareerGrowth #OpenToWork

📊 Customer Churn Analysis Project 🚀 I recently completed an end-to-end data analysis project to understand customer churn behavior and identify key factors affecting retention. 🔍 Using Python for data cleaning and exploratory analysis, and Tableau for visualization, I uncovered several important insights: • 📉 26.54% of customers churned  • ⚡ Customers with month-to-month contracts showed the highest churn  • 💳 Electronic check users had higher churn rates  • ⏳ Customers in early tenure (0–10 months) were most likely to leave 👉 Key takeaway: Customer churn is highest in the early lifecycle stage, making onboarding and early engagement critical for retention. 📈 I also built an interactive Tableau dashboard to visualize these insights and make them actionable. 🔗 GitHub Repository:  https://lnkd.in/dtGMs6Gz 🔗 Tableau Dashboard:  https://lnkd.in/dR4XnfzM I would love to hear your feedback! #DataAnalytics #Tableau #Python #DataScience #EDA #BusinessAnalytics #OpenToWork

After reviewing 100+ job descriptions for analytics roles, I've found that Excel, SQL, Python/R, and statistical knowledge are key. Tools like Power BI and Tableau are essential, along with strong problem-solving and communication skills. Master these to boost your chances of landing your first analytics job. Tool 1: Power BI (Equivalent Topics in Tableau) 👉 Data Modelling Basics (with Best Practices) 👉 Power Query, Power Pivot (Data Cleaning and Modelling) 👉 Filter and Row Context 👉 Basic M-Language and Intermediate DAX Functions 👉 Measures and Calculated Columns 👉 Types of Charts/Visuals (and Their Use Cases) 👉 Advanced Tooltips, Drill Through Feature 👉 Bookmarks, Filters/Slicers (for Creating Buttons/Page Navigation) 👉 Power BI Service Basics (Schedule Refresh, License Types, Workspace Roles, etc.) Tool 2: SQL (with Any One RDBMS Tool) 👉 SQL Server/MySQL/PostgreSQL (Choose Any One RDBMS) 👉 Database Fundamentals (Primary Key, Foreign Key, Relationships, Cardinality, etc.) 👉 DDL, DML Statements (Commonly Used Ones) 👉 Joins and Unions (Multiple Table Queries) 👉 Views and Stored Procedures 👉 Window Functions (Rank, DenseRank, RowNumber, Lead, Lag) 👉 Basic Select Queries (Single Table Queries) 👉 Subqueries and CTEs Tool 3: MS-Excel (Google Sheets Knowledge is a Plus) 👉 Pivot Tables, Pivot Charts 👉 Various Charts and Their Formatting 👉 Lookups (VLOOKUP, XLOOKUP, HLOOKUP and Their Use Cases) 👉 Conditional Formatting 👉 Major Excel Functions/Formulas (Text, Numeric, Logical Functions) 👉 Basic VBA/Macro 👉 Power Query, Power Pivot Tool 4: Python (Equivalent Topics in R) 👉 Pandas 👉 Matplotlib 👉 Python Libraries/IDEs (e.g., Jupyter Notebook) 👉 Numpy 👉 Python Basic Syntax 👉 Scikit-learn Suggested Learning Combinations for Entry-Level Roles: ➡ Excel + SQL + Power BI (or Tableau) ➡ Excel + SQL + Python (or R) Tip: Master any 3 of these tools to secure an entry-level role, and then upskill on the 4th one after landing the job. Krish Naik Dhaval Patel Sanjay Chandra #dataanalytics #powerbi #sql #python #excel

End-to-End Customer Behavior Analysis | Python • SQL • Power BI I’m excited to share my recent data analytics project where I analyzed customer shopping behavior to solve a real-world business problem: “How can a retail company use data to improve revenue, customer retention, and marketing strategies?” Project Workflow: Data Cleaning & Feature Engineering using Python Data Analysis using SQL (PostgreSQL) Interactive Dashboard using Power BI Business Insights & Recommendations Key Insights: Male customers generate nearly 2x more revenue than female customers Young adults are the highest revenue-contributing segment 73% of customers are non-subscribers, but spending is similar → strong conversion opportunity Business Recommendations: Target non-subscribers with personalized campaigns Introduce loyalty programs to retain repeat customers Optimize discount strategies for better profitability Focus marketing on high-performing customer segments Tools Used: Python | SQL | Power BI GitHub Repository: [https://lnkd.in/dFqf8RB7] This project helped me understand how to transform raw data into actionable business insights and build an end-to-end analytics pipeline. 💬 I’d love your feedback! #DataAnalytics #PowerBI #SQL #Python #DataScience #Projects #OpenToWork

🚀 Kickstart Your Career in Data Analytics! In today’s digital world, data skills are the key to professional growth. Our Data Analyst Program is designed for both IT and non-IT professionals, helping you build confidence with modern analytical tools and techniques. 📊 What You’ll Learn: Python for Data Analysis Excel for Business Insights Power BI & Tableau for Visualization SQL for Database Management 🧠 Empower yourself with digital literacy and transform data into impactful decisions. 🧰 Tools Included: Pandas | NumPy | Seaborn | Matplotlib | SciPy | scikit-learn | Jupyter | Power BI | Excel | Oracle Database 💼 Take the next step towards becoming a skilled Data Analyst and open doors to exciting career opportunities! #DataAnalytics #Python #PowerBI #Excel #SQL #Tableau #DataAnalystCourse #CareerGrowth #LearnDataAnalysis #digitalskills

4 tools I use daily as a Data Analyst: 1️⃣ SQL — to extract exactly what the business needs 2️⃣ Power BI & Looker Studio — to turn data into clear visual insights 3️⃣ Python — to automate repetitive tasks and save time 4️⃣ Excel & Google Sheets — to clean, organize, and present data efficiently These tools cover 90% of real-world data analyst work. Which tool do you find most valuable? 👇 #DataAnalyst #SQL #PowerBI #Python #Excel #DataAnalytics #Analytics #OpenToWork

Excited to share my latest Data Analytics Project: Customer Shopping Behavior Analysis I completed an end-to-end analytics project using Python, SQL (PostgreSQL), and Power BI to study customer purchase behavior from 3,900+ transactions. 🔹 Project Highlights: • Cleaned and transformed raw data using Python • Performed SQL-based business analysis • Built an interactive Power BI dashboard • Generated actionable insights for business growth Key Findings: • Male customers generated higher revenue • Loyal customers formed the largest segment • Young adults contributed the highest revenue • Express shipping users spent more on average • Discounts significantly influenced selected product sales Github: https://lnkd.in/gd8B-Q9C #DataAnalytics #Python #SQL #PowerBI #DataAnalyst #OpenToWork #Recruiters #seekingopportunities

I am currently learning Data Analytics and one thing I had to figure out on my own was : where do I even begin? So if you are just starting out like me, here is the roadmap I am following in 2026. ✔ Step 1 - Excel: The best starting point. Formulas, Pivot Tables and data cleaning. Builds your foundation before anything else. ✔ Step 2 - SQL: Learning to pull and query data from databases. Every analyst role asks for this. ✔ Step 3 - Data Visualisation: Power BI or Tableau. Because analysing data is only half the job; presenting it clearly is the other half. ✔ Step 4 - Python (Basics): Pandas and NumPy for handling data. You don't need to be a developer, just comfortable with the basics. ✔ Step 5 - Statistics: Mean, median, correlation, distributions. Tools make more sense once you understand the numbers behind them. ✔ Step 6 - Real Projects: Working on actual datasets to build a portfolio. This is what makes your profile stand out. ✔ Step 7 - Communication: Being able to explain your findings to someone non-technical. Often the most underrated skill. Still on this journey myself, but sharing it as I go. 🚀 If you are on the same path, let's connect and grow together! #DataAnalytics #DataAnalyst #LearningInPublic #CareerGrowth #SQL #Excel #PowerBI #Python #2026Goals

🎯 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

🚀 Data Analytics Project: Customer Behaviour Analysis (Python | SQL | Power BI) I recently built an end-to-end data analytics project to understand how customer behaviour impacts business revenue and decision-making. 🔍 Key Insights: 📊 Young adults contribute the highest share of revenue, making them a key target segment. 🚚 Customers using express shipping tend to have higher average spending. 💳 A large portion of loyal customers are not subscribed—highlighting a strong opportunity for conversion. 🛍️ Certain product categories rely heavily on discounts to drive sales volume. 📈 Customer purchasing patterns vary significantly across categories and demographics. 💡 Key Business Recommendations: • Target high-value segments (young adults) with personalized marketing • Promote subscription plans to loyal customers to improve retention   • Optimise shipping strategies to maximize revenue • Reduce dependency on discounts by improving product positioning  ⚙️ What I did: ✔ Cleaned and transformed raw data using Python (Pandas)   ✔ Performed SQL analysis in PostgreSQL to extract business insights   ✔ Built an interactive Power BI dashboard with dynamic filters and KPIs  🔗 GitHub Project: https://lnkd.in/gQ276Tp4 This project helped me strengthen my skills in data analysis, SQL, and dashboarding. #DataAnalytics #DataScience #Python #SQL #PostgreSQL #PowerBI #BusinessAnalytics #DataVisualization #DataAnalyst #AnalyticsProject #Dashboard #KPI #Insights #EDA #FeatureEngineering #DataCleaning #DataPreprocessing #BusinessIntelligence #DataDriven #Tech #Learning #PortfolioProject #EndToEndProject #DataProjects #AnalyticsLife