Websites to find Free Project Datasets 👆

𝗧𝗼𝗽 𝟰 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 😍

These FREE resources are all you need to go from beginner to confident analyst! 💻📊

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✅ Resume-worthy skills

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Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice!


1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview

2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand

3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction

4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data

5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction

6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris

7. Titanic Dataset: https://www.kaggle.com/c/titanic

8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality

9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data

11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud

13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows

14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new

15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting

16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19

17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness

18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams

20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140

21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer


💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍

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Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

By following these tips, you can be well-prepared for your next data science interview. Good luck!

𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍

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𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍

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Here are 10 project ideas to work on for Data Analytics

1. Customer Churn Prediction: Predict customer churn for subnoscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.

And this is how you can work on

Here’s a compact list of free resources for working on data analytics projects:

1. Datasets
• Kaggle Datasets: Wide range of datasets and community discussions.
• UCI Machine Learning Repository: Great for educational datasets.
• Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
• YouTube: Channels like Data School and freeCodeCamp for tutorials.
• 365DataScience: Data Science & AI Related Courses
3. Tools
• Google Colab: Free Jupyter Notebooks for Python coding.
• Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
• Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
• Data Analytics on Medium: Project guides and tutorials.

ENJOY LEARNING ✅️✅️

#datascienceprojects

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍

SQL seems tough, right? 😩

These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊

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Python Roadmap: 🗺

📂 Basics
 ∟📂 Data Types & Variables
 ∟📂 Operators & Expressions
 ∟📂 Control Flow (if, loops)
  ∟📂 Functions & Modules
   ∟📂 File Handling
    ∟📂 OOP (Classes & Objects)
     ∟📂 Exception Handling
      
∟📂 Advanced Topics (Decorators, Generators)
 ∟📂 Libraries (NumPy, Pandas, Matplotlib)
 ∟📂 Web Scraping / API Integration
 ∟📂 Frameworks (Flask/Django)
  ∟📂 Automation & Scripting
   ∟📂 Projects
    ∟ ✅ Apply For Job

Like if you need a detailed explanation step-by-step ❤️

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

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10 Python Libraries Every AI Engineer Should Know

1. Hugging Face Transformers
A powerful library for using and fine-tuning pre-trained transformer models for NLP. Learn more: Hugging Face NLP Course

2. Ollama
A framework for running and managing open-source LLMs locally with ease. Learn video: Ollama Course

3. OpenAI Python SDK
The official toolkit for integrating OpenAI models into Python applications. Learn more: The official developer quickstart guide

4. Anthropic SDK
A client library for seamless interaction with Claude and other Anthropic models. Learn more: Anthropic Python SDK

5. LangChain
A framework for building LLM applications with modular and extensible components. Learn more: DeepLearning.AI

6. LlamaIndex
A toolkit for integrating custom data sources with LLMs for better retrieval. Learn more: Building Agentic RAG with LlamaIndex

7. SQLAlchemy
A Python SQL toolkit and ORM for efficient and maintainable database interactions. Learn more: SQLAlchemy Unified Tutorial

8. ChromaDB
An open-source vector database optimized for AI-powered search and retrieval. Learn more: Getting Started - Chroma Docs

9. Weaviate
A cloud-native vector search engine for efficient semantic search at scale. Learn more: 101T Work with: Text data

10. Weights & Biases
A platform for tracking, visualizing, and optimizing ML experiments.
Learn more: Effective MLOps: Model Development

#artificialintelligence

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍

Power BI Isn’t Just a Tool—It’s a Career Game-Changer🚀

Whether you’re a student, a working professional, or switching careers, learning Power BI can set you apart in the competitive world of data analytics📊

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Your Analytics Journey Starts Now✅️

Exploratory Data Analysis ( EDA)

𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

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5 frequently Asked SQL Interview Questions with Answers in Data Engineering interviews:
𝐃𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲 - 𝐌𝐞𝐝𝐢𝐮𝐦

⚫️Determine the Top 5 Products with the Highest Revenue in Each Category.
Schema: Products (ProductID, Name, CategoryID), Sales (SaleID, ProductID, Amount)

WITH ProductRevenue AS (
SELECT p.ProductID,
p.Name,
p.CategoryID,
SUM(s.Amount) AS TotalRevenue,
RANK() OVER (PARTITION BY p.CategoryID ORDER BY SUM(s.Amount) DESC) AS RevenueRank
FROM Products p
JOIN Sales s ON p.ProductID = s.ProductID
GROUP BY p.ProductID, p.Name, p.CategoryID
)
SELECT ProductID, Name, CategoryID, TotalRevenue
FROM ProductRevenue
WHERE RevenueRank <= 5;

⚫️ Identify Employees with Increasing Sales for Four Consecutive Quarters.
Schema: Sales (EmployeeID, SaleDate, Amount)

WITH QuarterlySales AS (
SELECT EmployeeID,
DATE_TRUNC('quarter', SaleDate) AS Quarter,
SUM(Amount) AS QuarterlyAmount
FROM Sales
GROUP BY EmployeeID, DATE_TRUNC('quarter', SaleDate)
),
SalesTrend AS (
SELECT EmployeeID,
Quarter,
QuarterlyAmount,
LAG(QuarterlyAmount, 1) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter1,
LAG(QuarterlyAmount, 2) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter2,
LAG(QuarterlyAmount, 3) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter3
FROM QuarterlySales
)
SELECT EmployeeID, Quarter, QuarterlyAmount
FROM SalesTrend
WHERE QuarterlyAmount > PrevQuarter1 AND PrevQuarter1 > PrevQuarter2 AND PrevQuarter2 > PrevQuarter3;

⚫️ List Customers Who Made Purchases in Each of the Last Three Years.
Schema: Orders (OrderID, CustomerID, OrderDate)

WITH YearlyOrders AS (
SELECT CustomerID,
EXTRACT(YEAR FROM OrderDate) AS OrderYear
FROM Orders
GROUP BY CustomerID, EXTRACT(YEAR FROM OrderDate)
),
RecentYears AS (
SELECT DISTINCT OrderYear
FROM Orders
WHERE OrderDate >= CURRENT_DATE - INTERVAL '3 years'
),
CustomerYearlyOrders AS (
SELECT CustomerID,
COUNT(DISTINCT OrderYear) AS YearCount
FROM YearlyOrders
WHERE OrderYear IN (SELECT OrderYear FROM RecentYears)
GROUP BY CustomerID
)
SELECT CustomerID
FROM CustomerYearlyOrders
WHERE YearCount = 3;


⚫️ Find the Third Lowest Price for Each Product Category.
Schema: Products (ProductID, Name, CategoryID, Price)

WITH RankedPrices AS (
SELECT CategoryID,
Price,
DENSE_RANK() OVER (PARTITION BY CategoryID ORDER BY Price ASC) AS PriceRank
FROM Products
)
SELECT CategoryID, Price
FROM RankedPrices
WHERE PriceRank = 3;

⚫️ Identify Products with Total Sales Exceeding a Specified Threshold Over the Last 30 Days.
Schema: Sales (SaleID, ProductID, SaleDate, Amount)

WITH RecentSales AS (
SELECT ProductID,
SUM(Amount) AS TotalSales
FROM Sales
WHERE SaleDate >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY ProductID
)
SELECT ProductID, TotalSales
FROM RecentSales
WHERE TotalSales > 200;

Here you can find essential Interview Resources👇
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Hope it helps :)

𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜😍

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