🚀Here are 5 fresh Project ideas for Data Analysts 👇

🎯 𝗔𝗶𝗿𝗯𝗻𝗯 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 🏠
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata

💡This dataset describes the listing activity of homestays in New York City

🎯 𝗧𝗼𝗽 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 𝘀𝗼𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝟮𝟬𝟭𝟬-𝟮𝟬𝟭𝟵 🎵

https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year

🎯𝗪𝗮𝗹𝗺𝗮𝗿𝘁 𝗦𝘁𝗼𝗿𝗲 𝗦𝗮𝗹𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 📈

https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
💡Use historical markdown data to predict store sales

🎯 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝗠𝗼𝘃𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗩 𝗦𝗵𝗼𝘄𝘀 📺

https://www.kaggle.com/datasets/shivamb/netflix-shows
💡Listings of movies and tv shows on Netflix - Regularly Updated

🎯𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗷𝗼𝗯𝘀 𝗹𝗶𝘀𝘁𝗶𝗻𝗴𝘀 💼

https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
💡More than 8400 rows of data analyst jobs from USA, Canada and Africa.

ENJOY LEARNING 👍👍

📊 Data Science Project Ideas to Practice & Master Your Skills ✅

🟢 Beginner Level
• Titanic Survival Prediction (Logistic Regression)
• House Price Prediction (Linear Regression)
• Exploratory Data Analysis on IPL or Netflix Dataset
• Customer Segmentation (K-Means Clustering)
• Weather Data Visualization

🟡 Intermediate Level
• Sentiment Analysis on Tweets
• Credit Card Fraud Detection
• Time Series Forecasting (Stock or Sales Data)
• Image Classification using CNN (Fashion MNIST)
• Recommendation System for Movies/Products

🔴 Advanced Level
• End-to-End Machine Learning Pipeline with Deployment
• NLP Chatbot using Transformers
• Real-Time Dashboard with Streamlit + ML
• Anomaly Detection in Network Traffic
• A/B Testing & Business Decision Modeling

💬 Double Tap ❤️ for more! 🤖📈

Guys, Big Announcement!

We’ve officially hit 2.5 Million followers — and it’s time to level up together! ❤️

I’m launching a Python Projects Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step, hands-on journey — where you’ll build useful Python projects with clear code, explanations, and mini-quizzes!

Here’s what we’ll cover:

🔹 Week 1: Python Mini Projects (Daily Practice)
⦁ Calculator
⦁ To-Do List (CLI)
⦁ Number Guessing Game
⦁ Unit Converter
⦁ Digital Clock

🔹 Week 2: Data Handling & APIs
⦁ Read/Write CSV & Excel files
⦁ JSON parsing
⦁ API Calls using Requests
⦁ Weather App using OpenWeather API
⦁ Currency Converter using Real-time API

🔹 Week 3: Automation with Python
⦁ File Organizer Script
⦁ Email Sender
⦁ WhatsApp Automation
⦁ PDF Merger
⦁ Excel Report Generator

🔹 Week 4: Data Analysis with Pandas & Matplotlib
⦁ Load & Clean CSV
⦁ Data Aggregation
⦁ Data Visualization
⦁ Trend Analysis
⦁ Dashboard Basics

🔹 Week 5: AI & ML Projects (Beginner Friendly)
⦁ Predict House Prices
⦁ Email Spam Classifier
⦁ Sentiment Analysis
⦁ Image Classification (Intro)
⦁ Basic Chatbot

📌 Each project includes: 
✅ Problem Statement 
✅ Code with explanation 
✅ Sample input/output 
✅ Learning outcome 
✅ Mini quiz

💬 React ❤️ if you're ready to build some projects together!

You can access it for free here
👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Let’s Build. Let’s Grow. 💻🙌

𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝘁 𝗠𝗔𝗔𝗡𝗚 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀🔥🔥

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;


2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.


3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;


4. What is the difference between WHERE and HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;


5. How do you count the number of records in a table?

SELECT COUNT(*) FROM table_name;

This query counts all the records in the specified table.

6. How do you calculate average, sum, minimum, and maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;


7. What is a subquery, and how do you use it?

Subquery: A query nested inside another query

SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);




Till then keep learning and keep exploring 🙌

🎓 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗪𝗶𝘁𝗵 𝗚𝗼𝘃𝗲𝗿𝗻𝗺𝗲𝗻𝘁-𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 😍

Industry-approved Certifications to enhance employability

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Earn industry-recognized certificates and boost your career 🚀

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https://pdlink.in/3ImMFAB
 
Get the Govt. of India Incentives on course completion🏆

✅ Resume Tips for Data Science Roles 📄💼

Your resume is your first impression — make it clear, concise, and confident with these tips:

1. Keep It One Page (for beginners)
⦁ Recruiters spend 6–10 seconds glancing through.
⦁ Use crisp bullet points, no long paragraphs.
⦁ Focus on relevant data science experience.

2. Strong Summary at the Top 
Example: 
“Aspiring Data Scientist with hands-on experience in Python, Pandas, and Machine Learning. Built 5+ real-world projects including house price prediction and sentiment analysis.”

3. Highlight Technical Skills 
Separate Skills section:
⦁ Languages: Python, SQL
⦁ Libraries: Pandas, NumPy, Matplotlib, Scikit-learn
⦁ Tools: Jupyter, VS Code, Git, Tableau
⦁ Concepts: EDA, Regression, Classification, Data Cleaning

4. Showcase Projects (with results) 
Each project: 2–3 bullet points
⦁ “Built linear regression model predicting house prices with 85% accuracy using Scikit-learn.”
⦁ “Cleaned & visualized 10K+ rows of sales data with Pandas & Seaborn.” 
  Include GitHub links.

5. Education & Certifications 
Include:
⦁ Degree (any field)
⦁ Online certifications (Coursera, Kaggle, etc.)
⦁ Mention course projects or capstones

6. Quantify Everything 
Instead of “Analyzed data”, write: 
“Analyzed 20K+ customer rows to identify churn factors, improving model performance by 12%.”

7. Customize for Each Job
⦁ Match keywords from job descriptions.
⦁ Use role-specific terms like “classification model,” “data pipeline.”

💬 React ❤️ for more!

Data Science Learning Series: 
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/998

Learn Python: 
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗧𝗼𝗽 𝟭% 𝗼𝗳 𝘁𝗵𝗲 𝗧𝗲𝗰𝗵 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆😍

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✅ 𝟮𝟬𝟬𝟬+ Students Placed

🔗 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰👇:-

 https://pdlink.in/4hO7rWY

Hurry! Limited Seats Available🏃‍♂️

List of Python Project Ideas💡👨🏻‍💻🐍 -

Beginner Projects

🔹 Calculator
🔹 To-Do List
🔹 Number Guessing Game
🔹 Basic Web Scraper
🔹 Password Generator
🔹 Flashcard Quizzer
🔹 Simple Chatbot
🔹 Weather App
🔹 Unit Converter
🔹 Rock-Paper-Scissors Game

Intermediate Projects

🔸 Personal Diary
🔸 Web Scraping Tool
🔸 Expense Tracker
🔸 Flask Blog
🔸 Image Gallery
🔸 Chat Application
🔸 API Wrapper
🔸 Markdown to HTML Converter
🔸 Command-Line Pomodoro Timer
🔸 Basic Game with Pygame

Advanced Projects

🔺 Social Media Dashboard
🔺 Machine Learning Model
🔺 Data Visualization Tool
🔺 Portfolio Website
🔺 Blockchain Simulation
🔺 Chatbot with NLP
🔺 Multi-user Blog Platform
🔺 Automated Web Tester
🔺 File Organizer

Roadmap for AI Engineers

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 + 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 – 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻😍

Unlock the Power of Generative AI & ML - 100% Free Certification Course

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Enroll Today for Free & Get Certified 🎓

You're an upcoming data scientist?
This is for you.

The key to success isn't hoarding every tutorial and course.
It's about taking that first, decisive step.
Start small. Start now.

I remember feeling paralyzed by options:
Coursera, Udacity, bootcamps, blogs...
Where to begin?

Then my mentor gave me one piece of advice:

"Stop planning. Start doing.
Pick the shortest video you can find.
Watch it. Now."

It was tough love, but it worked.

I chose a 3-minute intro to pandas.
Then a quick matplotlib demo.
Suddenly, I was building momentum.

Each bite-sized lesson built my confidence.
Every "I did it!" moment sparked joy.
I was no longer overwhelmed—I was excited.

So here's my advice for you:

1. Find a 5-minute data science video. Any topic.
2. Watch it before you finish your coffee.
3. Do one thing you learned. Anything.

Remember:
A messy start beats a perfect plan
Every. Single. Time.

🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.

In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.

Includes guided lectures, videos, and code.
𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.

👉 Apply now: https://go.readytensor.ai/cert-549-agentic-ai-certification

Advanced Data Science Concepts 🚀

1️⃣ Feature Engineering & Selection

Handling Missing Values – Imputation techniques (mean, median, KNN).

Encoding Categorical Variables – One-Hot Encoding, Label Encoding, Target Encoding.

Scaling & Normalization – StandardScaler, MinMaxScaler, RobustScaler.

Dimensionality Reduction – PCA, t-SNE, UMAP, LDA.


2️⃣ Machine Learning Optimization

Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization.

Model Validation – Cross-validation, Bootstrapping.

Class Imbalance Handling – SMOTE, Oversampling, Undersampling.

Ensemble Learning – Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.


3️⃣ Deep Learning & Neural Networks

Neural Network Architectures – CNNs, RNNs, Transformers.

Activation Functions – ReLU, Sigmoid, Tanh, Softmax.

Optimization Algorithms – SGD, Adam, RMSprop.

Transfer Learning – Pre-trained models like BERT, GPT, ResNet.


4️⃣ Time Series Analysis

Forecasting Models – ARIMA, SARIMA, Prophet.

Feature Engineering for Time Series – Lag features, Rolling statistics.

Anomaly Detection – Isolation Forest, Autoencoders.


5️⃣ NLP (Natural Language Processing)

Text Preprocessing – Tokenization, Stemming, Lemmatization.

Word Embeddings – Word2Vec, GloVe, FastText.

Sequence Models – LSTMs, Transformers, BERT.

Text Classification & Sentiment Analysis – TF-IDF, Attention Mechanism.


6️⃣ Computer Vision

Image Processing – OpenCV, PIL.

Object Detection – YOLO, Faster R-CNN, SSD.

Image Segmentation – U-Net, Mask R-CNN.


7️⃣ Reinforcement Learning

Markov Decision Process (MDP) – Reward-based learning.

Q-Learning & Deep Q-Networks (DQN) – Policy improvement techniques.

Multi-Agent RL – Competitive and cooperative learning.


8️⃣ MLOps & Model Deployment

Model Monitoring & Versioning – MLflow, DVC.

Cloud ML Services – AWS SageMaker, GCP AI Platform.

API Deployment – Flask, FastAPI, TensorFlow Serving.


Like if you want detailed explanation on each topic ❤️

Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Hope this helps you 😊

📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲 😍

🔥 Learn Data Analytics with Real-time Projects ,Hands-on Tools

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