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@rationalgoai: Solo founder? You don't have to build alone. #startuptok #buildinpublic

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Sunday 04 October 2026 13:56:39 GMT
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🐻 dễ thương🥰🥰💚 #taehyung #kimtaehyung #bts #bangtan #btsarmy
🐻 dễ thương🥰🥰💚 #taehyung #kimtaehyung #bts #bangtan #btsarmy
A professional Data Science project is more than a Jupyter Notebook. If you want your project to look organized, reproducible, and production-ready, use a structure like this 👇 🗂️ Recommended Structure data-science-project/ │ ├── data/ │ ├── raw/ │ ├── processed/ │ └── external/ │ ├── notebooks/ │ ├── 01_data_collection.ipynb │ ├── 02_data_cleaning.ipynb │ ├── 03_eda.ipynb │ └── 04_modeling.ipynb │ ├── src/ │ ├── data/ │ │ └── make_dataset.py │ ├── features/ │ │ └── build_features.py │ ├── models/ │ │ ├── train.py │ │ └── predict.py │ └── visualization/ │ └── plots.py │ ├── models/ │ └── trained_model.pkl │ ├── tests/ │ └── test_model.py │ ├── reports/ │ └── figures/ │ ├── app/ │ └── app.py │ ├── requirements.txt ├── README.md ├── .gitignore └── config.yaml 🔍 What Does Each Folder Do? 📁 data/ Stores raw, processed, and external datasets. 📓 notebooks/ Used for exploration, experimentation, EDA, and initial modeling. ⚙️ src/ Contains reusable Python code for data processing, feature engineering, modeling, and visualization. 🤖 models/ Stores trained model files and artifacts. 🧪 tests/ Contains tests to make sure your code and ML pipeline work correctly. 📊 reports/ Stores generated charts, figures, and analysis reports. 🌐 app/ Contains the application or API used to serve your model. 📄 README.md Explains the project, setup, methodology, results, and how to run it. 📦 requirements.txt Lists the Python dependencies required to reproduce the project. 🔄 PROFESSIONAL DATA SCIENCE WORKFLOW Problem Definition ⬇️ Data Collection ⬇️ Data Cleaning ⬇️ EDA ⬇️ Feature Engineering ⬇️ Model Training ⬇️ Evaluation ⬇️ Experiment Tracking ⬇️ Model Deployment ⬇️ Monitoring & Maintenance 💡 Remember ❌ final_project.ipynb with everything inside ✅ Organized project with reusable code, documentation, testing, and deployment A good Data Scientist doesn’t just build a model. They build a reproducible system around the model. 🚀 📌 Save this structure for your next Data Science project. #DataScience #Python #MachineLearning #creatorsearchinsights #datascience
A professional Data Science project is more than a Jupyter Notebook. If you want your project to look organized, reproducible, and production-ready, use a structure like this 👇 🗂️ Recommended Structure data-science-project/ │ ├── data/ │ ├── raw/ │ ├── processed/ │ └── external/ │ ├── notebooks/ │ ├── 01_data_collection.ipynb │ ├── 02_data_cleaning.ipynb │ ├── 03_eda.ipynb │ └── 04_modeling.ipynb │ ├── src/ │ ├── data/ │ │ └── make_dataset.py │ ├── features/ │ │ └── build_features.py │ ├── models/ │ │ ├── train.py │ │ └── predict.py │ └── visualization/ │ └── plots.py │ ├── models/ │ └── trained_model.pkl │ ├── tests/ │ └── test_model.py │ ├── reports/ │ └── figures/ │ ├── app/ │ └── app.py │ ├── requirements.txt ├── README.md ├── .gitignore └── config.yaml 🔍 What Does Each Folder Do? 📁 data/ Stores raw, processed, and external datasets. 📓 notebooks/ Used for exploration, experimentation, EDA, and initial modeling. ⚙️ src/ Contains reusable Python code for data processing, feature engineering, modeling, and visualization. 🤖 models/ Stores trained model files and artifacts. 🧪 tests/ Contains tests to make sure your code and ML pipeline work correctly. 📊 reports/ Stores generated charts, figures, and analysis reports. 🌐 app/ Contains the application or API used to serve your model. 📄 README.md Explains the project, setup, methodology, results, and how to run it. 📦 requirements.txt Lists the Python dependencies required to reproduce the project. 🔄 PROFESSIONAL DATA SCIENCE WORKFLOW Problem Definition ⬇️ Data Collection ⬇️ Data Cleaning ⬇️ EDA ⬇️ Feature Engineering ⬇️ Model Training ⬇️ Evaluation ⬇️ Experiment Tracking ⬇️ Model Deployment ⬇️ Monitoring & Maintenance 💡 Remember ❌ final_project.ipynb with everything inside ✅ Organized project with reusable code, documentation, testing, and deployment A good Data Scientist doesn’t just build a model. They build a reproducible system around the model. 🚀 📌 Save this structure for your next Data Science project. #DataScience #Python #MachineLearning #creatorsearchinsights #datascience
Lưu ngay những mẹo xử lý cực hữu ích khi không may bị côn trùng cắn hoặc động vật tấn công để bảo vệ sức khỏe cho bản thân và gia đình nhé!  #MeoHay #MeoVatCuocSong #SucKhoe #AnToanMoiNgay #KinhNghiemHay
Lưu ngay những mẹo xử lý cực hữu ích khi không may bị côn trùng cắn hoặc động vật tấn công để bảo vệ sức khỏe cho bản thân và gia đình nhé!  #MeoHay #MeoVatCuocSong #SucKhoe #AnToanMoiNgay #KinhNghiemHay
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Want your Data Analyst portfolio to stand out? Skip another Titanic dataset. Build projects that answer real business questions and demonstrate how you turn messy data into decisions. 📊 💼 BUSINESS & FINANCE 1️⃣ Small Business Profit Leak Detector Find where a business is losing money through products, discounts, returns, and operating costs. 2️⃣ Customer Lifetime Value Dashboard Analyze customer spending, purchase frequency, retention, and estimated lifetime value. 3️⃣ Subscription Churn Intelligence Identify why customers cancel subscriptions and discover the segments most likely to churn. 4️⃣ Pricing & Discount Impact Analysis Determine whether discounts actually increase revenue or simply reduce profit. 5️⃣ Cash Flow Forecasting Dashboard Analyze historical income and expenses to identify future cash-flow risks. 🛒 E-COMMERCE & PRODUCT 6️⃣ Cart Abandonment Analysis Analyze where customers drop out of the purchasing funnel. 7️⃣ Product Return Root-Cause Analysis Discover which products, categories, locations, or customer segments generate the most returns. 8️⃣ E-Commerce Delivery Performance Analyze delivery delays by city, courier, product category, season, and order type. 9️⃣ Product Recommendation Performance Measure whether recommendations actually improve conversion, revenue, and average order value. 🔟 Inventory Stockout Analyzer Find products frequently going out of stock and estimate potential lost sales. 👥 CUSTOMERS & PEOPLE 1️⃣1️⃣ Customer Segmentation Engine Group customers based on behavior, spending, frequency, and engagement. 1️⃣2️⃣ Employee Attrition Intelligence Dashboard Analyze why employees leave and identify departments or employee profiles with higher risk. 1️⃣3️⃣ Customer Complaint Intelligence Analyze support tickets to identify recurring problems, response times, and customer pain points. 1️⃣4️⃣ Marketing Campaign ROI Analyzer Compare campaigns based on spending, conversions, revenue, CAC, and ROI. 1️⃣5️⃣ Customer Journey Analytics Track the journey from: Ad → Website → Signup → Product View → Purchase → Repeat Purchase 🌍 REAL-WORLD & SOCIAL IMPACT 1️⃣6️⃣ City Traffic Intelligence Dashboard 🚦 Analyze traffic volume, accident hotspots, peak hours, and congestion patterns. 1️⃣7️⃣ Food Waste Analytics 🍱 Analyze food waste by location, day, product, and quantity to identify where waste can be reduced. 1️⃣8️⃣ Energy Consumption Intelligence ⚡ Analyze electricity usage by hour, day, season, location, and appliance. 1️⃣9️⃣ Public Transport Performance 🚌 Analyze delays, passenger demand, routes, peak hours, and service reliability. 2️⃣0️⃣ Economic Intelligence Dashboard 📈 Combine indicators such as inflation, interest rates, exchange rates, employment, imports, exports, and GDP to identify economic trends. 🧠 MAKE YOUR PROJECT STAND OUT Don’t stop at: ❌ Cleaning data ❌ Making a few charts ❌ Training a model ❌ Uploading a notebook Instead build: Raw Data ⬇️ SQL / Python Cleaning ⬇️ EDA ⬇️ Business Questions ⬇️ KPI Development ⬇️ Dashboard ⬇️ Insights ⬇️ Business Recommendations 🏆 THE BEST PORTFOLIO PROJECT Your project should answer: “So what?” Not just: “Sales increased by 15%.” But: “Sales increased by 15%, primarily because of repeat customers in Region A. However, the 20% discount campaign reduced profit margin by 6%, suggesting the campaign should be redesigned.” That’s Data Analysis. 📌 Build projects that show you can move from DATA → INSIGHT → DECISION. #DataAnalytics #DataAnalyst #DataScience #creatorsearchinsights #machinelearningengineer
Want your Data Analyst portfolio to stand out? Skip another Titanic dataset. Build projects that answer real business questions and demonstrate how you turn messy data into decisions. 📊 💼 BUSINESS & FINANCE 1️⃣ Small Business Profit Leak Detector Find where a business is losing money through products, discounts, returns, and operating costs. 2️⃣ Customer Lifetime Value Dashboard Analyze customer spending, purchase frequency, retention, and estimated lifetime value. 3️⃣ Subscription Churn Intelligence Identify why customers cancel subscriptions and discover the segments most likely to churn. 4️⃣ Pricing & Discount Impact Analysis Determine whether discounts actually increase revenue or simply reduce profit. 5️⃣ Cash Flow Forecasting Dashboard Analyze historical income and expenses to identify future cash-flow risks. 🛒 E-COMMERCE & PRODUCT 6️⃣ Cart Abandonment Analysis Analyze where customers drop out of the purchasing funnel. 7️⃣ Product Return Root-Cause Analysis Discover which products, categories, locations, or customer segments generate the most returns. 8️⃣ E-Commerce Delivery Performance Analyze delivery delays by city, courier, product category, season, and order type. 9️⃣ Product Recommendation Performance Measure whether recommendations actually improve conversion, revenue, and average order value. 🔟 Inventory Stockout Analyzer Find products frequently going out of stock and estimate potential lost sales. 👥 CUSTOMERS & PEOPLE 1️⃣1️⃣ Customer Segmentation Engine Group customers based on behavior, spending, frequency, and engagement. 1️⃣2️⃣ Employee Attrition Intelligence Dashboard Analyze why employees leave and identify departments or employee profiles with higher risk. 1️⃣3️⃣ Customer Complaint Intelligence Analyze support tickets to identify recurring problems, response times, and customer pain points. 1️⃣4️⃣ Marketing Campaign ROI Analyzer Compare campaigns based on spending, conversions, revenue, CAC, and ROI. 1️⃣5️⃣ Customer Journey Analytics Track the journey from: Ad → Website → Signup → Product View → Purchase → Repeat Purchase 🌍 REAL-WORLD & SOCIAL IMPACT 1️⃣6️⃣ City Traffic Intelligence Dashboard 🚦 Analyze traffic volume, accident hotspots, peak hours, and congestion patterns. 1️⃣7️⃣ Food Waste Analytics 🍱 Analyze food waste by location, day, product, and quantity to identify where waste can be reduced. 1️⃣8️⃣ Energy Consumption Intelligence ⚡ Analyze electricity usage by hour, day, season, location, and appliance. 1️⃣9️⃣ Public Transport Performance 🚌 Analyze delays, passenger demand, routes, peak hours, and service reliability. 2️⃣0️⃣ Economic Intelligence Dashboard 📈 Combine indicators such as inflation, interest rates, exchange rates, employment, imports, exports, and GDP to identify economic trends. 🧠 MAKE YOUR PROJECT STAND OUT Don’t stop at: ❌ Cleaning data ❌ Making a few charts ❌ Training a model ❌ Uploading a notebook Instead build: Raw Data ⬇️ SQL / Python Cleaning ⬇️ EDA ⬇️ Business Questions ⬇️ KPI Development ⬇️ Dashboard ⬇️ Insights ⬇️ Business Recommendations 🏆 THE BEST PORTFOLIO PROJECT Your project should answer: “So what?” Not just: “Sales increased by 15%.” But: “Sales increased by 15%, primarily because of repeat customers in Region A. However, the 20% discount campaign reduced profit margin by 6%, suggesting the campaign should be redesigned.” That’s Data Analysis. 📌 Build projects that show you can move from DATA → INSIGHT → DECISION. #DataAnalytics #DataAnalyst #DataScience #creatorsearchinsights #machinelearningengineer

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