Mastering Pie Charts with Matplotlib and SQL

Turning SQL into Art: Data Visualization! 📈🎨 Day 78/100 Numbers tell a story, but graphs make people listen. For Day 78, I officially moved from the 'Backend' to the 'Frontend' of data. After mastering how to store and query information in SQL, today I learned how to visualize it using Matplotlib. It’s one thing to see a 9.0 GPA in a table, but seeing it as the highest peak on a bar chart is a completely different feeling! Technical Highlights: 📈 Data Mapping: Extracting relational data from SQLite and transforming it into Python lists for plotting. 🎨 Visual Customization: Mastering labels, grid lines, and axis scaling to make data human-readable and professional. 🏛️ Full-Pipeline Integration: Connecting the Database layer to the Visualization layer the foundation of any Business Intelligence tool. 📊 Categorical Comparison: Using bar charts to instantly identify outliers and top performers in a dataset. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #100DaysOfCode #DataVisualization #Matplotlib #Python #SQL #BTech #IILM #DataScience #AIML #SoftwareEngineering #LearningInPublic #WomenInTech

⌛️Week 7 – Data Science Bootcamp Digital Skola Progress This week I learned: 1. Sampling Methods → Understanding how to collect representative data. 2. Hypothesis Testing → Learning the logic of null vs. alternative hypotheses, p-values, and error types. 3. ⁠A/B Testing → Learn how to make A/B Testing 4. ⁠Python Visualization (Matplotlib, Seaborn, Plotly) → Studying how charts and plots communicate data clearly. 5. ⁠BI Tools (Google Data Studio and Power BI) → Reviewing dashboards as conceptual tools for organizing insights. I’ve summarized my full learning progress in the slides attached. Feel free to check them out and see what I’ve learned this week 🙌 #DigitalSkola #LearningProgressReview #DataScience

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

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

Most stats courses start with formulas. This one starts with intuition. Every data role interview tests the same core: when to use median vs mean, how to read variance, whether a z-score flags an outlier, and what skewness is doing to your analysis. If those questions feel fuzzy, the problem isn't the math — it's that you learned the formulas before you learned the picture. LDS Statistics Foundations is a free 4-module course built around interactive animations and Python examples. The tagline on the page is "build statistical intuition, not formula memorization" — and that's exactly how it's structured. What you'll cover in roughly 4 hours: → Finding the Center — mean, median, mode, and when each one lies to you → Measuring the Spread — range, quartiles, variance, standard deviation, box plots → Understanding Distributions — the normal curve, the 68-95-99.7 rule, z-scores → Understanding Skewness — why data gets lopsided and how to spot misleading statistics Completely free, runs in your browser, no Python install needed. The animations do the heavy lifting — you watch the mean get dragged around by an outlier, watch a z-score light up, watch a distribution skew in real time. Start here: https://lnkd.in/eVJhn6ka #DataScience #Statistics #Analytics #CareerGrowth #LetsDataScience

🚀 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 5/100 📊 Continuing to learn DAX, I started noticing a few common mistakes that actually affect results (and yes, I’ve made them too 😅) Here are 5 that already changed how I approach it 👇 ❌ 1. Using calculated columns instead of measures Calculated columns are computed once at the row level and stay static Measures are calculated dynamically and depend on filters 👉 if you’re building visuals, you almost always need measures ❌ 2. Ignoring filter context In DAX, results always depend on context The same measure can return different values across visuals 👉 without understanding context, it’s easy to misinterpret numbers ❌ 3. Using SUM instead of CALCULATE SUM just aggregates values CALCULATE modifies context and defines how the calculation works 👉 this is where most business logic actually happens ❌ 4. Using "/" instead of DIVIDE The "/" operator can break when dividing by zero DIVIDE handles this safely and allows fallback values 👉 small detail, but critical for reliability ❌ 5. Not using VAR Without VAR, formulas become long and hard to read VAR lets you store intermediate results and simplify logic 👉 this makes a huge difference in complex calculations The more I learn DAX, the more I see - it’s not about memorizing functions, it’s about understanding how data behaves in context 🧠 Which of these was the most painful for you? 👀 #dataanalytics #powerbi #dax #businessintelligence #analytics #datascience #learningjourney #100daychallenge #dataskills #powerbideveloper #analyticscommunity #careergrowth #techcareer #datavisualization #dataanalysis #sql #python #productanalytics #businessanalytics #opentowork

Cleaning data is not the boring part of Data Analytics. It’s one of the most important parts. A lot of beginners want to jump directly into dashboards and visualizations. But if your data is messy, your insights will be misleading. Before analysis, always check for: ✅ Missing values ✅ Duplicate records ✅ Incorrect formats ✅ Outliers ✅ Inconsistent entries Because no matter how good your dashboard looks…bad data will always lead to bad decisions. Clean data builds trustworthy analysis. 📊 #DataAnalytics #DataCleaning #DataAnalyst #SQL #Excel #Python #PowerBI #Analytics #LearningInPublic

"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