Debugging and Building a Complete Data System with Python and SQL

A SQL query that calls itself! Sounds like a bug. It's actually a superpower: 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗧𝗘𝘀! A recursive CTE is a Common Table Expression that selects from itself. Seems tricky, but it's actually not that difficult! Imagine you have this company hierarchy: Alice (CEO) ↳ Bob and Charlie report to Alice ↳ David and Eve report to Bob ↳ Frank and Grace report to Charlie You can build this entire corporate tree with SQL! Showing each employee and their level in the hierarchy. Behind the SQL scenes: 1️⃣ The base case gets the top of the hierarchy (Alice). 2️⃣ Then the CTE calls itself to find everyone reporting to her. 3️⃣ Each new “generation” of employees adds one more level. 4️⃣ The process continues until there are no more subordinates. That’s recursion! 🔄 The most powerful way to handle hierarchical data. 𝟭𝟬𝟬 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝟯𝟬𝟬 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗘𝘅𝗰𝗲𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 + 𝗙𝗼𝗿𝗺𝘂𝗹𝗮 𝗦𝗵𝗲𝗲𝘁 𝟭𝟱𝟬 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 (𝗡𝘂𝗺𝗣𝘆 + 𝗣𝗮𝗻𝗱𝗮𝘀 + 𝗠𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯) 𝟭𝟬𝟬 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗗𝗔𝗫 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 + 𝗡𝗼𝘁𝗲𝘀 𝟭𝟬𝟬 𝗧𝗼𝗽 𝗛𝗥 𝗥𝗼𝘂𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 𝟭𝟬𝟬 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤&𝗔 + 𝗡𝗼𝘁𝗲𝘀 𝗥𝗲𝘀𝘂𝗺𝗲 𝗚𝘂𝗶𝗱𝗲 + 𝟳𝟬𝟬 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗦𝗶𝘁𝗲𝘀 𝗚𝗲𝘁 𝗔𝗰𝗰𝗲𝘀𝘀 𝗛𝗲𝗿𝗲: https://lnkd.in/dyBfCTjK #datascience #data #dataanalysis #sql #python #pandas #excel #powerbi

From Database to Dashboard: Mastered Data Exporting! 📤📊 Day 72/100 Data is only useful if the right people can read it. For Day 72, I tackled Data Portability. While SQL is perfect for storage, sometimes you need to get that data into the hands of someone who doesn't speak code. I built a Python utility that queries a relational database and exports the entire result set into a professional CSV (Comma Separated Values) report. Technical Highlights: 📤 Automated Extraction: Using Python's csv module to bridge the gap between SQLite and Excel-friendly formats. 📋 Dynamic Metadata: Programmatically retrieving column headers using cursor.description to ensure the report is perfectly labeled. 💾 Streamlined Writing: Using writerows() for efficient, bulk-data transfer from memory to disk. 🛡️ Data Governance: Creating a 'Snapshot' system to backup records before performing destructive operations. The Professional Edge: As an engineer, building the database is only half the job. The other half is ensuring that the data is accessible, portable, and ready for analysis in tools like Excel or Tableau. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #SQL #DataAnalysis #100DaysOfCode #BTech #IILM #Python #SoftwareEngineering #DataEngineering #Excel #LearningInPublic #WomenInTech

👉 Most data analysts waste hours every week on tasks that could be automated. From my experience working with data, one thing became very clear: Manual work is the biggest bottleneck. • exporting data • cleaning it repeatedly • rebuilding the same reports It’s not only time-consuming - it also increases the risk of errors. So I started focusing more on automation. Even simple steps like: ✔️ using SQL for targeted extraction ✔️ using Python for data cleaning ✔️ standardizing reporting structures made a significant difference. 💡 Insight Automation doesn’t just save time - it changes how you think about data. 👉 What’s one task you’d automate in your workflow? #dataanalytics #sql #automation #powerbi

Ever wondered if your forecast is failing because of the model or just bad data? If your forecast is always too high or vice versa and you are stuck with high inventory costs that kill your cash flow, try this: There is a free tool in python called ydata-profiling. With that, you can check the health of your SCM data with just a few lines of code. Don't worry, you don't need to know how to code. It's copy-paste and you're good to go. What you get: - How many duplicates - How many missing values - Correlation - Heatmap and more I use this as my first step to plan my approach for every Data Validation project. Try it out. If you need support or have any questions, let me know. Copy: # 1. Install requirements first !pip install ydata-profiling pandas import pandas as pd from ydata_profiling import ProfileReport # 2. Load your data # INSERT YOUR FILE PATH HERE (e.g., 'C:/Users/Downloads/data.csv') path_in = 'INSERT_YOUR_FILE_PATH_HERE' df = pd.read_csv(path_in) # 3. Generate the report profile = ProfileReport(df, title="Data Health Report", explorative=True) # 4. Save the result # INSERT THE FOLDER WHERE YOU WANT TO SAVE THE REPORT path_out = 'INSERT_EXPORT_PATH_HERE/report.html' profile.to_file(path_out) print("Done!")

📊 Just came across a really handy SQL Cheatsheet—perfect for quick revision and brushing up core concepts! It neatly covers: 🔹 DQL basics like SELECT, WHERE, GROUP BY, ORDER BY 🔹 Joins (INNER, LEFT, RIGHT, FULL) with simple visuals 🔹 Window functions like ROW_NUMBER(), RANK(), LAG(), LEAD() 🔹 Aggregations (SUM, AVG, COUNT, etc.) 🔹 DML & DDL operations (INSERT, UPDATE, DELETE, CREATE) What I like most is how it simplifies complex topics like joins and window functions into something visual and easy to recall. Whether you're preparing for interviews, working with data, or just revising fundamentals—this is a great quick reference. 💡 Tip: Don’t just memorize—practice writing queries to really understand how these concepts work together. #SQL #DataAnalytics #Learning #TechSkills #DataScience #Programming

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

🧹 Day 3/7 – Data Cleaning = Data Quality Before validating… clean your data. Focused on: 🔹 Data inspection (info, describe) 🔹 Handling missing values 🔹 Filtering datasets 🔹 Removing duplicates 💡 Sample code snippets: Data Inspection: print(df.info()) print(df.describe()) 🎯 Understand data before validating it. Handling Missing Values: df.fillna(0, inplace=True) 🎯 Missing data = common ETL issue Filtering Data: df[df["age"] > 18] 🎯 Apply business rules easily Removing Duplicates: df.drop_duplicates(inplace=True) 🎯 Ensures clean datasets 🎯 Key takeaway: Bad data in = bad insights out. Cleaning is not optional. #DataCleaning #DataQuality #Python #Analytics #ETL

🚀 Day 5/10 — Optimization Series SQL Performance Tips (Write Faster Queries) 👉 Basics are done. 👉 Now we move from working code → optimized code. You write a query… It gives correct results… But it’s slow on large data 😐 👉 That’s where optimization matters. 🔹 1. Avoid SELECT * SELECT * FROM employees; ❌ SELECT name, salary FROM employees; ✅ 👉 Fetch only what you need 🔹 2. Filter Early SELECT * FROM employees WHERE department = 'IT'; 👉 Reduces data before processing 🔹 3. Use Proper Indexes 👉 Index frequently used columns 👉 Improves query speed 🔹 4. Avoid Unnecessary Joins 👉 Join only required tables 👉 Extra joins = extra cost 🔹 5. Limit Data SELECT * FROM employees LIMIT 10; 👉 Useful for testing & performance 🔹 Why This Matters Faster execution Reduced resource usage Scalable queries 🔹 Real Insight 👉 SQL performance is not just about correctness 👉 It’s about efficiency 💡 Quick Summary Small changes → big performance impact 💡 Something to remember A correct query gives results… An optimized query gives results faster. #SQL #Python #DataEngineering #LearningInPublic #TechLearning

🔁 Dynamic Column Creation in Power Query using List.Accumulate I recently worked on transforming a comma-separated “Skills” column into structured flags — without hardcoding column names. Using List.Accumulate: • Iterates through a list of skills • Adds a new column in each iteration • Checks if the skill exists in each row Iteration flow: Start → Base Table Step 1 → Add "sql" column Step 2 → Add "python" column Step 3 → Add "power bi" column Behind the scenes: Text.Split converts text to a list List.MatchesAny checks for existence ✅ Result: Scalable, dynamic, and clean transformation This pattern is incredibly useful for turning unstructured text into analytical-ready data. #PowerQuery #PowerBI #DataTransformation #Mlanguage #DataAnalytics

🚀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