Applying Data Engineering Concepts to a Simple Project

You don't need to be a developer to use Python. You just need 5 scripts that eliminate the boring parts of your work. [swipe for all 5 →] After 11 years in Data Field closely working with operation team, here are the 5 automations every ops person should learn: 1️⃣ Automated Email Reports — pandas + smtplib 2️⃣ Data Cleaning & Merging — pandas + openpyxl 3️⃣ Inventory Reconciliation — pandas + sqlalchemy 4️⃣ Competitive Price Tracking — requests + BeautifulSoup 5️⃣ KPI Breach Alerts — slack_sdk + schedule Learning path: Week 1-2: Python basics + pandas Week 3: Connect to databases Week 4: Build your first automated report Total: 2-4 weeks of weekend learning → 100+ hours saved every quarter. Which of these would save you the most time? 👇 #Python #Automation #Operations #DataAnalytics #Productivity #CareerGrowth

Just deployed my first data app and honestly it felt like a whole journey 🚀 I built a Product Information Quality Dashboard using Python and Streamlit that analyzes how complete product data is and flags items that might confuse customers. Some of the skills I got to work with: 🐍 Python 🗄️ SQLAlchemy & SQLite for database management 📊 Pandas for data handling and exports ⚙️ Streamlit for building the interactive dashboard 🔧 Git & GitHub for version control ☁️ Deployed live on Streamlit Community Cloud Still learning and building every day — but seeing something you built actually live on the internet hits different 💪 Feedback always welcome! Check it out 👇 🔗 Live App: https://lnkd.in/gaiu6Src 💻 GitHub: https://lnkd.in/gm-TeghV #Python #DataScience #Streamlit #buildinpublic #DataAnalytics #LearningInPublic

Excited to share my project: CSV Data Analyzer App 📊 I built an interactive web application using Python and Streamlit that allows users to upload CSV files and instantly generate insights without writing code. This project focuses on simplifying Exploratory Data Analysis (EDA) for beginners and students. 🔍 Key Features: ✔ Upload CSV files easily ✔ View dataset overview (rows, columns, cells) ✔ Detect missing values ✔ Generate statistical insights ✔ Interactive and user-friendly interface 🛠️ Tech Stack: Python | Streamlit Live demo:https://lnkd.in/gSiGat8h 💻 GitHub Repository:https://lnkd.in/gQU_cK22 🎯 I’m continuously improving this project by adding visualizations and advanced analytics features. I would really appreciate your feedback! 😊 #Python #DataScience #Streamlit #Projects #OpenToWork #Learning #GitHub

✨ Turning boring tables into stunning visuals just got easier! If you work with data, you know that presentation matters just as much as analysis. Great Tables by Posit PBC isn’t just another tool, it redefines how data tables should look. Here’s why it stands out: 🚀 Beautiful by default No more plain, hard-to-read tables. You get clean, polished outputs with minimal effort. 🎯 Highly customizable From styling to formatting, you can fine-tune everything to match your needs. 🐍 Works seamlessly with Python Perfect for data workflows, especially if you're already using pandas. 📈 Built for storytelling Great tables help you highlight insights, not just display data. Whether you're building reports, dashboards, or sharing insights with stakeholders, this tool helps your data speak clearly and look great doing it. Definitely worth checking out if you care about both data and design 👇 https://lnkd.in/eShM4gZu #DataScience #Python #DataVisualization #Analytics #OpenSource #DataEngineering

Python Loops: Iteration Simplified 🔁 Ever felt like you're repeating yourself in code? That’s where Python Loops come to the rescue. Understanding the logic between FOR and WHILE loops is a fundamental step for any data professional looking to automate their workflow. The Breakdown: • FOR Loops: These are your go-to when you have a definite number of iterations. Whether you're iterating through a list of column names or a specific range of values, the for loop handles the sequence beautifully. • WHILE Loops: These are all about conditions. The code keeps running as long as a specific condition remains True. This is perfect for scenarios where you don't know exactly how many times you'll need to run the logic until a certain threshold is met. Why this matters for Data Analysts: While we often rely on vectorized operations in Python (like Pandas), understanding the raw logic of loops helps when: 1. Automating API calls that require pagination. 2. Web scraping through multiple pages. 3. Building complex logic inside custom Power BI transformations or advanced SQL stored procedures. Mastering these flowcharts is the key to writing cleaner, more efficient scripts! #Python #CodingLogic #DataAnalytics #Automation #ProgrammingBasics #PythonLoops #SQL #PowerBI #Codebasics

“Day 7 – I built a dashboard using Python + Flask " Today I worked on: web_app.py : It will <<>> Convert DataFrame → HTML table Generate chart image Send both to frontend WHAT BUILT TODAY A real report system Exactly what SaaS tools do Facing challenge: Problems: “UnboundLocalError: cannot access local variable 'file'. Solved: Indent issues solved. TypeError: 'method' object is not subscriptable. Solved: Using square brackets [ ] on function (method). FileNotFoundError: static//chart.png Solved: put HTML and templates path into app initializer that easily get file location. import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) app = Flask( __name__, template_folder=os.path.join(BASE_DIR, '..', 'templates'), static_folder=os.path.join(BASE_DIR, '..', 'static') ) What I learned : Show full student table Show topper + insights Display chart inside browser I am documenting my journey to becoming a Data Scientist while building real-world projects. #DataScience #Python #SaaS #Automation #Analytics #BuildInPublic

📅 Day 6 of My Data Analytics Journey 🚀 Today I focused on understanding some essential Python concepts: 🔹 range() function 🔹 len() function 🔹 List methods like sort() and append() 🔹 Difference between functions and methods 🔍 What I learned: • range() → used to generate sequences (mostly in loops) • len() → returns the length of a list or string • Functions → independent reusable blocks of code • Methods → functions that belong to objects (like lists) 💻 Practice Code: # Using range and len numbers = list(range(1, 6)) print("Numbers:", numbers) print("Length:", len(numbers)) # List methods numbers.append(10) # add element numbers.sort() # sort list print("Updated List:", numbers) # Function example def greet(name): return "Hello " + name print(greet("Jitesh")) 💡 Key Insight: Understanding functions and methods makes coding more structured and helps in efficient data handling. 📈 Building strong fundamentals step by step. 🤝 Open to connecting with others on a similar journey! #Day6 #Python #DataAnalytics #LearningInPublic #Consistency #CareerGrowth

🚀 Built a Python Project: Corporate Data Analyzer Most business users struggle to analyze raw data efficiently without technical tools. So I built a simple desktop application to solve this problem. 💡 What it does: • Import CSV / Excel data • Perform GroupBy & aggregations (sum, mean, max, etc.) • Generate interactive charts (Bar, Line, Pie) • Export reports (Excel/CSV) • Export charts as PNG 🛠 Tech Stack: Python | Pandas | Tkinter | NumPy | Matplotlib 📊 This project helped me improve: ✔ Data analysis using Pandas ✔ GUI development using Tkinter ✔ Data visualization using Matplotlib ✔ Building end-to-end real-world tools 🔗 GitHub Repository: https://lnkd.in/giyeMwRd I’d really appreciate your feedback and suggestions! #Python #DataAnalytics #Projects #GitHub #Learning #DataScience #Portfolio #OpenToWork

I Tracked My Expenses Using Python & NumPy — Here's What ₹38,940 Taught Me About My Spending Habits I built a Personal Finance Tracker using just Python and NumPy — no Pandas, no fancy libraries. Here's what I discovered about my own spending 👇 The project started simple: a CSV file with 50 transactions across 3 months. But when I ran the numbers through NumPy, the insights hit different. What the data revealed: • Shopping eats 40% of my budget — with just 6 transactions • My Top 5 purchases alone = 36% of total spending • Average spend (₹779) vs Median (₹465) — proof that a few big buys skew everything • 56% of money goes to just 11 "high-tier" transactions What I actually built: → Read raw CSV data using Python's csv module → Converted everything to NumPy arrays for fast computation → Used np.sum(), np.mean(), np.max(), np.median(), np.std() → Boolean masking to filter by category & month → np.argsort() to rank top expenses → np.percentile() for distribution analysis → A formatted summary report printed right to the console. Key takeaway: You don't need complex tools to get powerful insights. NumPy + a CSV file + curiosity = real, actionable data about your life. Watch the screen recording below to see the full report output! This is Week 1 of my Python data journey. Next stop: Pandas & Matplotlib. #NumPy #DataAnalysis #PersonalFinance #LearningInPublic #PythonProjects #BuildInPublic #Python #DataScience #CodeNewbie #Programming #TechTwitter #DataDriven #100DaysOfCode #FinanceTracker

📅 Day 73 of #100DaysOfCode — and today the data told a story I didn't expect! Today's focus: data visualization with Matplotlib using real StackOverflow data on programming language popularity from 2008 to 2020. Here's what I worked through today: 🔧 Renamed DataFrame columns using the names parameter in read_csv() for cleaner, more readable data 📅 Converted messy datetime strings into proper pandas datetime objects — a crucial data cleaning step before any time series analysis 🔍 Used groupby() + sum() + idxmax() to identify the most popular programming language of all time by total posts (spoiler: JavaScript 👑) 📊 Filtered DataFrames using boolean indexing to isolate specific languages for visualization 📈 Plotted time series data with Matplotlib — first a single language, then overlaid two languages on the same chart The most compelling insight? The chart says it all: 🔵 Java peaked around 2013-2014 and has been declining ever since 🟠 Python has been on a relentless rise — and by 2020, it's not even close The numbers don't lie. If you're wondering whether to learn Python, the StackOverflow community already voted with their questions. Onward to Day 74! 💪 #Python #Pandas #Matplotlib #DataVisualization #100DaysOfCode #DataScience #ContinuousLearning #MicrosoftFabric