Data Visualization with Matplotlib and SQL

Slicing the Data: Visualizing Proportions with Pie Charts! 🥧📊 Day 79/100 It’s not just about how much data you have; it’s about how it’s distributed. For Day 79, I continued my Data Visualization journey by mastering the Pie Chart using Matplotlib and SQL. I wanted a way to see which research domains are trending. Instead of looking at a long list, I can now see the entire landscape in one colorful, proportional slice. Technical Highlights: 🥧 Proportional Mapping: Converting SQL GROUP BY counts into percentage-based visual segments. 🔢 Automated Percentage Logic: Using the autopct parameter to let Python handle the mathematical distribution on the fly. 🎨 Visual Aesthetics: Implementing custom color palettes and start-angles to make the charts presentation-ready. 📉 Data Summarization: Turning hundreds of individual research records into a single, high-level strategic overview. The Engineering Perspective: In CSE-AIML, we often deal with 'Class Imbalance' in datasets. Being able to quickly generate a Pie Chart allows an engineer to see if their data is biased toward one category. It’s the ultimate tool for a quick 'Health Check' on any project. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #100DaysOfCode #DataScience #Matplotlib #Python #SQL #BTech #IILM #IEEE #DataAnalytics #SoftwareEngineering #LearningInPublic #WomenInTech

𝗪𝗵𝗮𝘁 𝗮 𝗿𝗲𝗮𝗹 𝗰𝗵𝘂𝗿𝗻 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 This is my Customer Churn Intelligence Platform, built in Power BI, backed by a Python ML model (XGBoost, AUC-ROC: 0.923) and a #PostgreSQL data pipeline. Four pages: Executive Summary: #KPIs, 24-month #trend, #risk distribution, MRR waterfall #ChurnRiskRegister: every customer ranked by churn probability + revenue at risk Cohort & Retention: 12-month heatmap + segment survival curves Recovery Action Centre: priority queue for the CS team with intervention recommendations Links 👇 GitHub: https://lnkd.in/d6S--SeF Live Dashboard: https://lnkd.in/dZT3xreU Kaggle Notebook: https://lnkd.in/dx_WnT-2 Full stack: Python (XGBoost · SHAP) · SQL (PostgreSQL) · Power BI · Excel AUC-ROC: 0.923 · 10,000 customers · $886K revenue at risk identified #DataAnalytics #PowerBI #Python #SQL #CustomerSuccess #XGBoost #SHAP #PortfolioProject #DataScience #CustomerChurn #BusinessIntelligence #Python #Dashboard #MachineLearning #Excel #Sheet #Churnprevention #Dataprofessional

"When it comes to analytics, start small but think big. 📈 I often see analysts jump straight into modeling or complex algorithms—but the real magic happens in the exploration and preparation of data. Understanding trends, identifying anomalies, and cleaning data properly can unlock insights that impact business decisions significantly. In my upcoming post, I’ll share a step-by-step approach to exploratory data analysis (EDA) and building dashboards that really work. Do you usually start with EDA or jump into modeling? Would love to hear your approach!" #DataAnalytics #BusinessIntelligence #PowerBI #Tableau #SQL #Python #Insights

🚀 Learning update: Advanced Data Visualization with Matplotlib Took things a step further by exploring how to compare data more effectively using Matplotlib. 📊 The Focus Moving beyond basic plots into quantitative comparisons, distributions, and storytelling with data. 🧠 What I Learned - Built bar charts to compare values across categories (e.g., Olympic medals by country) - Created stacked bar charts to combine multiple variables in one view - Improved readability with rotated labels and legends - Used histograms to understand full data distributions, not just averages - Controlled bins and transparency to reveal hidden patterns 📈 Going Deeper - Applied error bars to show variability using standard deviation - Used boxplots to visualize median, quartiles, and outliers - Built scatter plots for bi-variate analysis (e.g., CO₂ vs temperature) - Encoded additional insights using color for comparisons and time 🎨 Visualization Matters - Explored different plot styles like ggplot and colorblind-friendly themes - Learned when to use each style depending on audience and medium - Understood the importance of accessibility in data communication 💾 Sharing & Scaling - Saved visualizations in different formats (PNG, JPG, SVG) - Controlled resolution (DPI) and figure size for different use cases - Automated visualizations using loops and dynamic data handling 💡 Key Takeaway Great data visualization is not just about showing numbers, it is about making comparisons clear, highlighting patterns, and designing for real-world use. #DataScience #MachineLearning #Python #Matplotlib #DataVisualization #LearningJourney #Datacamp #DatacampAfrica

🚀 Every Data Scientist’s journey is a staircase, not a jump. You don’t start with Dashboards. You build your way up. 📊 Excel → SQL → Data Cleaning → EDA → Statistics → Business Understanding → Visualization → Dashboards Each step matters. Skip one, and the structure gets weak. What this really teaches is simple: - Data is not about tools, it’s about thinking - Cleaning is where 70% of real work happens - EDA is where insights start speaking - Visualization is where stories are told Right now I’m focusing on building strong fundamentals step by step instead of rushing the “final output”. Because in real industry work, dashboards don’t matter if the base is weak. What step are you currently on? #DataScience #Analytics #SQL #Python #LearningJourney #EDA #DataAnalytics

Day 4: Data Visualization — Turning Data into Insights Raw data alone doesn’t tell a story. Visualization is what makes it understandable. Why visualization matters? Humans understand visuals faster than numbers. A simple chart can reveal patterns that raw data cannot. Common types of plots: * Line chart → trends over time * Bar chart → comparison between categories * Histogram → data distribution * Scatter plot → relationships between variables Simple example (Matplotlib): import matplotlib.pyplot as plt data = [10, 20, 30, 40] plt.plot(data) plt.show() With just a few lines of code, you can turn numbers into meaningful insights. Where visualization is used: * Business reports * Data analysis * Machine learning insights * Decision making Key insight: Good analysis is not just about finding insights — it’s about presenting them clearly. #DataScience #DataVisualization #Python #Matplotlib #Analytics

Excited to share my latest project: Customer Churn Analysis Dashboard! I built an end-to-end data analytics solution using Python and Power BI to analyze customer behavior and predict churn. The project focuses on identifying patterns that influence customer retention and highlighting high-risk customers for proactive decision-making. A machine learning model was used to estimate churn probability, and the results were visualized through an interactive dashboard to uncover meaningful insights. 🛠 Tools Used: Python | Machine Learning | Power BI | DAX 🔗 GitHub Project: [ https://lnkd.in/d7Cp5GDE ] This project helped me strengthen my skills in data analysis, predictive modeling, and business intelligence. #DataAnalytics #PowerBI #Python #DAX #ML

🚀 Day 14: Building My First Complete Data Analysis Workflow Today I worked on a complete mini data analysis project, combining everything I’ve learned so far in my Data Science journey. 📊 Project: Dataset Analysis using Pandas & Matplotlib 📌 What I did: ->Loaded a real dataset using Pandas ->Explored the data structure and summary ->Handled missing values ->Performed basic analysis ->Visualized results using charts 💻 Concepts Used: ->Data cleaning ->Data analysis ->Data visualization ⚠️ Challenge I faced: Handling missing data correctly and deciding what to fill required careful thinking. 💡 Example from my code: df["Age"].fillna(df["Age"].mean(), inplace=True) 📊 Key Insight: Data becomes meaningful only after cleaning and visualizing—it’s not just about numbers. 🎯 Next Step: Working on more structured projects and improving analytical thinking. 📌 Would appreciate suggestions: What should be my next step to improve as a beginner in Data Science? #Day14 #DataScience #Python #Pandas #Matplotlib #Projects #LearningJourney

🚀Day 90 of My 100 Days Data Analysis Journey 90 days in… and one thing is clear: Consistency beats clarity. At the beginning, everything felt confusing, tools, queries, concepts. But showing up daily, even on low-energy days, changed everything. From Day 1 to Day 90, here’s what this journey has really taught: • You don’t need to understand everything to start • Progress comes from doing, not overthinking • Repetition builds confidence faster than motivation • Small daily effort compounds into real skill For anyone starting data analysis: Focus less on “knowing everything” and more on: Practicing consistently Building simple things Getting comfortable with confusion What’s next: • Go deeper into SQL with real-world datasets • Start building structured SQL projects • Transition into Python for data analysis • Begin working with Power BI for visualization This is where learning turns into application. 90 days done. Now it’s time to make it count. #DataAnalytics #LearningInPublic #100DaysOfCode #SQL #Python #PowerBI

Most people jump straight to dashboards. We should start with data profiling. 📊 Step 1 in any Data Analytics project: Analyze raw datasets in Excel before cleaning. 🔍 What do we usually find? • Inconsistent values across columns • Missing data in multiple fields • Mixed data types (text + numbers) • Data integrity issues across tables 💡 Key takeaway: We should understand the data first before cleaning or building dashboards. ➡️ Next step (already covered in previous post): Data Cleaning using Python 🤔 Quick question: Do you start with data profiling or jump directly into dashboards? #DataAnalytics #Excel #Python #PowerBI #LearningInPublic