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. 💻🙌
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. 💻🙌
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Data Science Interview Questions 🚀
1. What is Data Science and how does it differ from Data Analytics?
2. How do you handle missing or duplicate data?
3. Explain supervised vs unsupervised learning.
4. What is overfitting and how do you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation and why is it important?
7. What are key evaluation metrics for classification models?
8. What is feature engineering? Give examples.
9. Explain principal component analysis (PCA).
10. Difference between classification and regression algorithms.
11. What is a confusion matrix?
12. Explain bagging vs boosting.
13. Describe decision trees and random forests.
14. What is gradient descent?
15. What are regularization techniques and why use them?
16. How do you handle imbalanced datasets?
17. What is hypothesis testing and p-values?
18. Explain clustering and k-means algorithm.
19. How do you handle unstructured data?
20. What is text mining and sentiment analysis?
21. How do you select important features?
22. What is ensemble learning?
23. Basics of time series analysis.
24. How do you tune hyperparameters?
25. What are activation functions in neural networks?
26. Explain transfer learning.
27. How do you deploy machine learning models?
28. What are common challenges in big data?
29. Define ROC curve and AUC score.
30. What is deep learning?
31. What is reinforcement learning?
32. What tools and libraries do you use?
33. How do you interpret model results for non-technical audiences?
34. What is dimensionality reduction?
35. Handling categorical variables in machine learning.
36. What is exploratory data analysis (EDA)?
37. Explain t-test and chi-square test.
38. How do you ensure fairness and avoid bias in models?
39. Describe a complex data problem you solved.
40. How do you stay updated with new data science trends?
React ❤️ for the detailed answers
1. What is Data Science and how does it differ from Data Analytics?
2. How do you handle missing or duplicate data?
3. Explain supervised vs unsupervised learning.
4. What is overfitting and how do you prevent it?
5. Describe the bias-variance tradeoff.
6. What is cross-validation and why is it important?
7. What are key evaluation metrics for classification models?
8. What is feature engineering? Give examples.
9. Explain principal component analysis (PCA).
10. Difference between classification and regression algorithms.
11. What is a confusion matrix?
12. Explain bagging vs boosting.
13. Describe decision trees and random forests.
14. What is gradient descent?
15. What are regularization techniques and why use them?
16. How do you handle imbalanced datasets?
17. What is hypothesis testing and p-values?
18. Explain clustering and k-means algorithm.
19. How do you handle unstructured data?
20. What is text mining and sentiment analysis?
21. How do you select important features?
22. What is ensemble learning?
23. Basics of time series analysis.
24. How do you tune hyperparameters?
25. What are activation functions in neural networks?
26. Explain transfer learning.
27. How do you deploy machine learning models?
28. What are common challenges in big data?
29. Define ROC curve and AUC score.
30. What is deep learning?
31. What is reinforcement learning?
32. What tools and libraries do you use?
33. How do you interpret model results for non-technical audiences?
34. What is dimensionality reduction?
35. Handling categorical variables in machine learning.
36. What is exploratory data analysis (EDA)?
37. Explain t-test and chi-square test.
38. How do you ensure fairness and avoid bias in models?
39. Describe a complex data problem you solved.
40. How do you stay updated with new data science trends?
React ❤️ for the detailed answers
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Data Science Interview Questions With Answers Part-1 👇
1. What is Data Science and how does it differ from Data Analytics?
Data Science is a multidisciplinary field using algorithms, statistics, and programming to extract insights and predict future trends from structured and unstructured data. It focuses on asking the big, strategic questions and uses advanced techniques like machine learning.
Data Analytics, by contrast, focuses on analyzing past data to find actionable answers to specific business questions, often using simpler statistical methods and reporting tools. Simply put, Data Science looks forward, while Data Analytics looks backward (sources,,).
————————
2. How do you handle missing or duplicate data?
⦁ Missing data: techniques include removing rows/columns, imputing values with mean/median/mode, or using predictive models.
⦁ Duplicate data: identify duplicates using functions like
————————
3. Explain supervised vs unsupervised learning.
⦁ Supervised learning uses labeled data to train models that predict outputs for new inputs (e.g., classification, regression).
⦁ Unsupervised learning finds patterns or structures in unlabeled data (e.g., clustering, dimensionality reduction).
————————
4. What is overfitting and how do you prevent it?
Overfitting is when a model captures noise or specific patterns in training data, resulting in poor generalization to unseen data. Prevention includes cross-validation, pruning, regularization, early stopping, and using simpler models.
————————
5. Describe the bias-variance tradeoff.
⦁ Bias measures error from incorrect assumptions (underfitting), while variance measures sensitivity to training data (overfitting).
⦁ The tradeoff is balancing model complexity so it generalizes well — neither too simple (high bias) nor too complex (high variance).
————————
6. What is cross-validation and why is it important?
Cross-validation divides data into subsets to train and validate models multiple times, improving performance estimation and reducing overfitting risks by ensuring the model works well on unseen data.
————————
7. What are key evaluation metrics for classification models?
Common metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix components (TP, FP, FN, TN), depending on dataset balance and business context.
————————
8. What is feature engineering? Give examples.
Feature engineering creates new input variables to improve model performance, e.g., extracting day of the week from timestamps, encoding categorical variables, normalizing numeric features, or creating interaction terms.
————————
9. Explain principal component analysis (PCA).
PCA reduces data dimensionality by transforming original features into uncorrelated principal components that capture the most variance, simplifying models while preserving information.
————————
10. Difference between classification and regression algorithms.
⦁ Classification predicts discrete labels or classes (e.g., spam/not spam).
⦁ Regression predicts continuous numerical values (e.g., house prices).
React ♥️ for Part-2
1. What is Data Science and how does it differ from Data Analytics?
Data Science is a multidisciplinary field using algorithms, statistics, and programming to extract insights and predict future trends from structured and unstructured data. It focuses on asking the big, strategic questions and uses advanced techniques like machine learning.
Data Analytics, by contrast, focuses on analyzing past data to find actionable answers to specific business questions, often using simpler statistical methods and reporting tools. Simply put, Data Science looks forward, while Data Analytics looks backward (sources,,).
————————
2. How do you handle missing or duplicate data?
⦁ Missing data: techniques include removing rows/columns, imputing values with mean/median/mode, or using predictive models.
⦁ Duplicate data: identify duplicates using functions like
duplicated() and remove or merge them depending on context. Handling depends on data quality needs and model goals.————————
3. Explain supervised vs unsupervised learning.
⦁ Supervised learning uses labeled data to train models that predict outputs for new inputs (e.g., classification, regression).
⦁ Unsupervised learning finds patterns or structures in unlabeled data (e.g., clustering, dimensionality reduction).
————————
4. What is overfitting and how do you prevent it?
Overfitting is when a model captures noise or specific patterns in training data, resulting in poor generalization to unseen data. Prevention includes cross-validation, pruning, regularization, early stopping, and using simpler models.
————————
5. Describe the bias-variance tradeoff.
⦁ Bias measures error from incorrect assumptions (underfitting), while variance measures sensitivity to training data (overfitting).
⦁ The tradeoff is balancing model complexity so it generalizes well — neither too simple (high bias) nor too complex (high variance).
————————
6. What is cross-validation and why is it important?
Cross-validation divides data into subsets to train and validate models multiple times, improving performance estimation and reducing overfitting risks by ensuring the model works well on unseen data.
————————
7. What are key evaluation metrics for classification models?
Common metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix components (TP, FP, FN, TN), depending on dataset balance and business context.
————————
8. What is feature engineering? Give examples.
Feature engineering creates new input variables to improve model performance, e.g., extracting day of the week from timestamps, encoding categorical variables, normalizing numeric features, or creating interaction terms.
————————
9. Explain principal component analysis (PCA).
PCA reduces data dimensionality by transforming original features into uncorrelated principal components that capture the most variance, simplifying models while preserving information.
————————
10. Difference between classification and regression algorithms.
⦁ Classification predicts discrete labels or classes (e.g., spam/not spam).
⦁ Regression predicts continuous numerical values (e.g., house prices).
React ♥️ for Part-2
❤12🔥1
Data Science Interview Questions With Answers Part-2
11. What is a confusion matrix?
A confusion matrix is a table used to evaluate classification models by showing true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), helping calculate accuracy, precision, recall, and F1-score.
12. Explain bagging vs boosting.
⦁ Bagging (Bootstrap Aggregating) builds multiple independent models on random data subsets and averages results to reduce variance (e.g., Random Forest).
⦁ Boosting builds models sequentially, each correcting errors of the previous to reduce bias (e.g., AdaBoost, Gradient Boosting).
13. Describe decision trees and random forests.
⦁ Decision trees split data based on feature thresholds to make predictions in a tree-like model.
⦁ Random forests are an ensemble of decision trees built on random data and feature subsets, improving accuracy and reducing overfitting.
14. What is gradient descent?
An optimization algorithm that iteratively adjusts model parameters to minimize a loss function by moving in the direction of steepest descent (gradient).
15. What are regularization techniques and why use them?
Regularization (like L1/Lasso and L2/Ridge) adds penalty terms to loss functions to prevent overfitting by constraining model complexity and shrinking coefficients.
16. How do you handle imbalanced datasets?
Methods include resampling (oversampling minority, undersampling majority), synthetic data generation (SMOTE), using appropriate evaluation metrics, and algorithms robust to imbalance.
17. What is hypothesis testing and p-values?
Hypothesis testing assesses if a claim about data is statistically significant. The p-value indicates the probability that the observed data occurred under the null hypothesis; a low p-value (<0.05) usually leads to rejecting the null.
18. Explain clustering and k-means algorithm.
Clustering groups similar data points without labels. K-means partitions data into k clusters by iteratively assigning points to nearest centroids and recalculating centroids until convergence.
19. How do you handle unstructured data?
Techniques include text processing (tokenization, stemming), image/audio processing with specialized models (CNNs, RNNs), and converting raw data into structured features for analysis.
20. What is text mining and sentiment analysis?
Text mining extracts meaningful information from text data, while sentiment analysis classifies text by emotional tone (positive, negative, neutral), often using NLP techniques.
React ♥️ for Part-3
11. What is a confusion matrix?
A confusion matrix is a table used to evaluate classification models by showing true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), helping calculate accuracy, precision, recall, and F1-score.
12. Explain bagging vs boosting.
⦁ Bagging (Bootstrap Aggregating) builds multiple independent models on random data subsets and averages results to reduce variance (e.g., Random Forest).
⦁ Boosting builds models sequentially, each correcting errors of the previous to reduce bias (e.g., AdaBoost, Gradient Boosting).
13. Describe decision trees and random forests.
⦁ Decision trees split data based on feature thresholds to make predictions in a tree-like model.
⦁ Random forests are an ensemble of decision trees built on random data and feature subsets, improving accuracy and reducing overfitting.
14. What is gradient descent?
An optimization algorithm that iteratively adjusts model parameters to minimize a loss function by moving in the direction of steepest descent (gradient).
15. What are regularization techniques and why use them?
Regularization (like L1/Lasso and L2/Ridge) adds penalty terms to loss functions to prevent overfitting by constraining model complexity and shrinking coefficients.
16. How do you handle imbalanced datasets?
Methods include resampling (oversampling minority, undersampling majority), synthetic data generation (SMOTE), using appropriate evaluation metrics, and algorithms robust to imbalance.
17. What is hypothesis testing and p-values?
Hypothesis testing assesses if a claim about data is statistically significant. The p-value indicates the probability that the observed data occurred under the null hypothesis; a low p-value (<0.05) usually leads to rejecting the null.
18. Explain clustering and k-means algorithm.
Clustering groups similar data points without labels. K-means partitions data into k clusters by iteratively assigning points to nearest centroids and recalculating centroids until convergence.
19. How do you handle unstructured data?
Techniques include text processing (tokenization, stemming), image/audio processing with specialized models (CNNs, RNNs), and converting raw data into structured features for analysis.
20. What is text mining and sentiment analysis?
Text mining extracts meaningful information from text data, while sentiment analysis classifies text by emotional tone (positive, negative, neutral), often using NLP techniques.
React ♥️ for Part-3
❤10🔥2👍1
Data Science Interview Questions With Answers Part-3
21. How do you select important features?
Techniques include statistical tests (chi-square, ANOVA), correlation analysis, feature importance from models (like tree-based algorithms), recursive feature elimination, and regularization methods.
22. What is ensemble learning?
Combining predictions from multiple models (e.g., bagging, boosting, stacking) to improve accuracy, reduce overfitting, and create more robust predictions.
23. Basics of time series analysis.
Analyzing data points collected over time considering trends, seasonality, and noise. Key methods include ARIMA, exponential smoothing, and decomposition.
24. How do you tune hyperparameters?
Using techniques like grid search, random search, or Bayesian optimization with cross-validation to find the best model parameter settings.
25. What are activation functions in neural networks?
Functions that introduce non-linearity into the model, enabling it to learn complex patterns. Examples: sigmoid, ReLU, tanh.
26. Explain transfer learning.
Using a pre-trained model on one task as a starting point for a related task, reducing training time and data needed.
27. How do you deploy machine learning models?
Methods include REST APIs, batch processing, cloud services (AWS, Azure), containerization (Docker), and monitoring after deployment.
28. What are common challenges in big data?
Handling volume, variety, velocity, data quality, storage, processing speed, and ensuring security and privacy.
29. Define ROC curve and AUC score.
ROC curve plots true positive rate vs false positive rate at various thresholds. AUC (Area Under Curve) measures overall model discrimination ability; closer to 1 is better.
30. What is deep learning?
A subset of machine learning using multi-layered neural networks (like CNNs, RNNs) to learn hierarchical feature representations from data, excelling in unstructured data tasks.
React ♥️ for Part-4
21. How do you select important features?
Techniques include statistical tests (chi-square, ANOVA), correlation analysis, feature importance from models (like tree-based algorithms), recursive feature elimination, and regularization methods.
22. What is ensemble learning?
Combining predictions from multiple models (e.g., bagging, boosting, stacking) to improve accuracy, reduce overfitting, and create more robust predictions.
23. Basics of time series analysis.
Analyzing data points collected over time considering trends, seasonality, and noise. Key methods include ARIMA, exponential smoothing, and decomposition.
24. How do you tune hyperparameters?
Using techniques like grid search, random search, or Bayesian optimization with cross-validation to find the best model parameter settings.
25. What are activation functions in neural networks?
Functions that introduce non-linearity into the model, enabling it to learn complex patterns. Examples: sigmoid, ReLU, tanh.
26. Explain transfer learning.
Using a pre-trained model on one task as a starting point for a related task, reducing training time and data needed.
27. How do you deploy machine learning models?
Methods include REST APIs, batch processing, cloud services (AWS, Azure), containerization (Docker), and monitoring after deployment.
28. What are common challenges in big data?
Handling volume, variety, velocity, data quality, storage, processing speed, and ensuring security and privacy.
29. Define ROC curve and AUC score.
ROC curve plots true positive rate vs false positive rate at various thresholds. AUC (Area Under Curve) measures overall model discrimination ability; closer to 1 is better.
30. What is deep learning?
A subset of machine learning using multi-layered neural networks (like CNNs, RNNs) to learn hierarchical feature representations from data, excelling in unstructured data tasks.
React ♥️ for Part-4
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Data Science Interview Questions Part 4:
31. What is reinforcement learning?
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards through trial and error.
32. What tools and libraries do you use?
Commonly used tools: Python, R, Jupyter Notebooks, SQL, Excel. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn.
33. How do you interpret model results for non-technical audiences?
Use simple language, visualize key insights (charts, dashboards), focus on business impact, avoid jargon, and use analogies or stories.
34. What is dimensionality reduction?
Techniques like PCA or t-SNE to reduce the number of features while preserving essential information, improving model efficiency and visualization.
35. Handling categorical variables in machine learning.
Use encoding methods like one-hot encoding, label encoding, target encoding depending on model requirements and feature cardinality.
36. What is exploratory data analysis (EDA)?
The process of summarizing main characteristics of data often using visual methods to understand patterns, spot anomalies, and test hypotheses.
37. Explain t-test and chi-square test.
⦁ t-test compares means between two groups to see if they are statistically different.
⦁ Chi-square test assesses relationships between categorical variables.
38. How do you ensure fairness and avoid bias in models?
Audit data for bias, use balanced training datasets, apply fairness-aware algorithms, monitor model outcomes, and include diverse perspectives in evaluation.
39. Describe a complex data problem you solved.
(Your personal story here, describing the problem, approach, tools used, and impact.)
40. How do you stay updated with new data science trends?
Follow blogs, research papers, online courses, attend webinars, participate in communities (Kaggle, Stack Overflow), and read newsletters.
Data science interview questions: https://t.iss.one/datasciencefun/3668
Double Tap ♥️ If This Helped You
31. What is reinforcement learning?
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards through trial and error.
32. What tools and libraries do you use?
Commonly used tools: Python, R, Jupyter Notebooks, SQL, Excel. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn.
33. How do you interpret model results for non-technical audiences?
Use simple language, visualize key insights (charts, dashboards), focus on business impact, avoid jargon, and use analogies or stories.
34. What is dimensionality reduction?
Techniques like PCA or t-SNE to reduce the number of features while preserving essential information, improving model efficiency and visualization.
35. Handling categorical variables in machine learning.
Use encoding methods like one-hot encoding, label encoding, target encoding depending on model requirements and feature cardinality.
36. What is exploratory data analysis (EDA)?
The process of summarizing main characteristics of data often using visual methods to understand patterns, spot anomalies, and test hypotheses.
37. Explain t-test and chi-square test.
⦁ t-test compares means between two groups to see if they are statistically different.
⦁ Chi-square test assesses relationships between categorical variables.
38. How do you ensure fairness and avoid bias in models?
Audit data for bias, use balanced training datasets, apply fairness-aware algorithms, monitor model outcomes, and include diverse perspectives in evaluation.
39. Describe a complex data problem you solved.
(Your personal story here, describing the problem, approach, tools used, and impact.)
40. How do you stay updated with new data science trends?
Follow blogs, research papers, online courses, attend webinars, participate in communities (Kaggle, Stack Overflow), and read newsletters.
Data science interview questions: https://t.iss.one/datasciencefun/3668
Double Tap ♥️ If This Helped You
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🌟🌍 Be part of the global science community!
Follow the UNESCO–Al Fozan International Prize for inspiring stories, breakthroughs, and opportunities in STEM (Science, Technology, Engineering, and Mathematics).
📲 Follow us here:
https://x.com/UNESCO_AlFozan/status/1955702609932902734
Follow the UNESCO–Al Fozan International Prize for inspiring stories, breakthroughs, and opportunities in STEM (Science, Technology, Engineering, and Mathematics).
📲 Follow us here:
https://x.com/UNESCO_AlFozan/status/1955702609932902734
1❤4
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🚀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 👍👍
🎯 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 👍👍
❤2🥰1
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📊 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! 🤖📈
🟢 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! 🤖📈
❤6
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
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https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Let’s Build. Let’s Grow. 💻🙌
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. 💻🙌
❤13👍1
𝟒 𝐅𝐫𝐞𝐞 𝐃𝐒𝐀 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐂𝐫𝐚𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬😍
Cracking coding interviews isn’t about luck—it’s about mastering Data Structures and Algorithms (DSA) with the right resources🖥🎖
Whether you’re aiming for FAANG, top MNCs, or fast-growing startups, having a strong foundation in DSA will set you apart🧑🎓💥
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Start today and turn your DSA fear into DSA mastery!✅️
Cracking coding interviews isn’t about luck—it’s about mastering Data Structures and Algorithms (DSA) with the right resources🖥🎖
Whether you’re aiming for FAANG, top MNCs, or fast-growing startups, having a strong foundation in DSA will set you apart🧑🎓💥
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Start today and turn your DSA fear into DSA mastery!✅️
❤1
Which of the following is essential for any well-documented data science project?
Anonymous Quiz
4%
a) Fancy UI design
3%
b) Only code files
83%
c) README file explaining problem, steps & results
10%
d) Just a model accuracy score
❤2
Your model performs well on training data but poorly on test data. What’s likely missing?
Anonymous Quiz
25%
a) Hyperparameter tuning
69%
b) Overfitting handling
3%
c) More print statements
3%
d) Fancy visualizations
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