Data Science Interview Questions With Answers Part-1 ๐
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,,).
โโโโโโโโ
2. How do you handle missing or duplicate data?
โฆ Missing data: techniques include removing rows/columns, imputing values with mean/median/mode, or using predictive models.
โฆ Duplicate data: identify duplicates using functions like
โโโโโโโโ
3. Explain supervised vs unsupervised learning.
โฆ Supervised learning uses labeled data to train models that predict outputs for new inputs (e.g., classification, regression).
โฆ Unsupervised learning finds patterns or structures in unlabeled data (e.g., clustering, dimensionality reduction).
โโโโโโโโ
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.
โโโโโโโโ
5. Describe the bias-variance tradeoff.
โฆ Bias measures error from incorrect assumptions (underfitting), while variance measures sensitivity to training data (overfitting).
โฆ The tradeoff is balancing model complexity so it generalizes well โ neither too simple (high bias) nor too complex (high variance).
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
10. Difference between classification and regression algorithms.
โฆ Classification predicts discrete labels or classes (e.g., spam/not spam).
โฆ Regression predicts continuous numerical values (e.g., house prices).
React โฅ๏ธ 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,,).
โโโโโโโโ
2. How do you handle missing or duplicate data?
โฆ Missing data: techniques include removing rows/columns, imputing values with mean/median/mode, or using predictive models.
โฆ 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.โโโโโโโโ
3. Explain supervised vs unsupervised learning.
โฆ Supervised learning uses labeled data to train models that predict outputs for new inputs (e.g., classification, regression).
โฆ Unsupervised learning finds patterns or structures in unlabeled data (e.g., clustering, dimensionality reduction).
โโโโโโโโ
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.
โโโโโโโโ
5. Describe the bias-variance tradeoff.
โฆ Bias measures error from incorrect assumptions (underfitting), while variance measures sensitivity to training data (overfitting).
โฆ The tradeoff is balancing model complexity so it generalizes well โ neither too simple (high bias) nor too complex (high variance).
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
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.
โโโโโโโโ
10. Difference between classification and regression algorithms.
โฆ Classification predicts discrete labels or classes (e.g., spam/not spam).
โฆ Regression predicts continuous numerical values (e.g., house prices).
React โฅ๏ธ for Part-2
โค13๐1๐ฅ1
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.
โฆ Bagging (Bootstrap Aggregating) builds multiple independent models on random data subsets and averages results to reduce variance (e.g., Random Forest).
โฆ 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.
โฆ Decision trees split data based on feature thresholds to make predictions in a tree-like model.
โฆ 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 โฅ๏ธ 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.
โฆ Bagging (Bootstrap Aggregating) builds multiple independent models on random data subsets and averages results to reduce variance (e.g., Random Forest).
โฆ 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.
โฆ Decision trees split data based on feature thresholds to make predictions in a tree-like model.
โฆ 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 โฅ๏ธ for Part-3
โค10๐2๐ฅ2๐1
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 โฅ๏ธ 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 โฅ๏ธ 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.
โฆ t-test compares means between two groups to see if they are statistically different.
โฆ 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 โฅ๏ธ 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.
โฆ t-test compares means between two groups to see if they are statistically different.
โฆ 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 โฅ๏ธ If This Helped You
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Follow the UNESCOโAl Fozan International Prize for inspiring stories, breakthroughs, and opportunities in STEM (Science, Technology, Engineering, and Mathematics).
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๐Here are 5 fresh Project ideas for Data Analysts ๐
๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
ENJOY LEARNING ๐๐
๐ฏ ๐๐ถ๐ฟ๐ฏ๐ป๐ฏ ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐
https://www.kaggle.com/datasets/arianazmoudeh/airbnbopendata
๐กThis dataset describes the listing activity of homestays in New York City
๐ฏ ๐ง๐ผ๐ฝ ๐ฆ๐ฝ๐ผ๐๐ถ๐ณ๐ ๐๐ผ๐ป๐ด๐ ๐ณ๐ฟ๐ผ๐บ ๐ฎ๐ฌ๐ญ๐ฌ-๐ฎ๐ฌ๐ญ๐ต ๐ต
https://www.kaggle.com/datasets/leonardopena/top-spotify-songs-from-20102019-by-year
๐ฏ๐ช๐ฎ๐น๐บ๐ฎ๐ฟ๐ ๐ฆ๐๐ผ๐ฟ๐ฒ ๐ฆ๐ฎ๐น๐ฒ๐ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด ๐
https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data
๐กUse historical markdown data to predict store sales
๐ฏ ๐ก๐ฒ๐๐ณ๐น๐ถ๐ ๐ ๐ผ๐๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ฉ ๐ฆ๐ต๐ผ๐๐ ๐บ
https://www.kaggle.com/datasets/shivamb/netflix-shows
๐กListings of movies and tv shows on Netflix - Regularly Updated
๐ฏ๐๐ถ๐ป๐ธ๐ฒ๐ฑ๐๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ ๐ท๐ผ๐ฏ๐ ๐น๐ถ๐๐๐ถ๐ป๐ด๐ ๐ผ
https://www.kaggle.com/datasets/cedricaubin/linkedin-data-analyst-jobs-listings
๐กMore than 8400 rows of data analyst jobs from USA, Canada and Africa.
ENJOY LEARNING ๐๐
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๐ Data Science Project Ideas to Practice & Master Your Skills โ
๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
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โข A/B Testing & Business Decision Modeling
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๐ข Beginner Level
โข Titanic Survival Prediction (Logistic Regression)
โข House Price Prediction (Linear Regression)
โข Exploratory Data Analysis on IPL or Netflix Dataset
โข Customer Segmentation (K-Means Clustering)
โข Weather Data Visualization
๐ก Intermediate Level
โข Sentiment Analysis on Tweets
โข Credit Card Fraud Detection
โข Time Series Forecasting (Stock or Sales Data)
โข Image Classification using CNN (Fashion MNIST)
โข Recommendation System for Movies/Products
๐ด Advanced Level
โข End-to-End Machine Learning Pipeline with Deployment
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๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
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โฆ Number Guessing Game
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โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
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Letโs Build. Letโs Grow. ๐ป๐
Weโve officially hit 2.5 Million followers โ and itโs time to level up together! โค๏ธ
Iโm launching a Python Projects Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step, hands-on journey โ where youโll build useful Python projects with clear code, explanations, and mini-quizzes!
Hereโs what weโll cover:
๐น Week 1: Python Mini Projects (Daily Practice)
โฆ Calculator
โฆ To-Do List (CLI)
โฆ Number Guessing Game
โฆ Unit Converter
โฆ Digital Clock
๐น Week 2: Data Handling & APIs
โฆ Read/Write CSV & Excel files
โฆ JSON parsing
โฆ API Calls using Requests
โฆ Weather App using OpenWeather API
โฆ Currency Converter using Real-time API
๐น Week 3: Automation with Python
โฆ File Organizer Script
โฆ Email Sender
โฆ WhatsApp Automation
โฆ PDF Merger
โฆ Excel Report Generator
๐น Week 4: Data Analysis with Pandas & Matplotlib
โฆ Load & Clean CSV
โฆ Data Aggregation
โฆ Data Visualization
โฆ Trend Analysis
โฆ Dashboard Basics
๐น Week 5: AI & ML Projects (Beginner Friendly)
โฆ Predict House Prices
โฆ Email Spam Classifier
โฆ Sentiment Analysis
โฆ Image Classification (Intro)
โฆ Basic Chatbot
๐ Each project includes:
โ Problem Statement
โ Code with explanation
โ Sample input/output
โ Learning outcome
โ Mini quiz
๐ฌ React โค๏ธ if you're ready to build some projects together!
You can access it for free here
๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Letโs Build. Letโs Grow. ๐ป๐
โค13๐1
๐ ๐
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โค1
Which of the following is essential for any well-documented data science project?
Anonymous Quiz
5%
a) Fancy UI design
3%
b) Only code files
84%
c) README file explaining problem, steps & results
8%
d) Just a model accuracy score
โค2
Your model performs well on training data but poorly on test data. Whatโs likely missing?
Anonymous Quiz
25%
a) Hyperparameter tuning
68%
b) Overfitting handling
4%
c) More print statements
3%
d) Fancy visualizations
โค1
Which file should you upload along with your Jupyter Notebook to make your project reproducible?
Anonymous Quiz
8%
a) Screenshot of results
14%
b) Excel output file
74%
c) requirements.txt or environment.yml
4%
d) A video walkthrough
โค1
Which step is often skipped but highly recommended when presenting a project?
Anonymous Quiz
27%
a) Exploratory Data Analysis
38%
b) Writing comments in code
26%
c) Explaining business impact or value
9%
d) Printing all columns of the dataset
โค2
Which of the following is NOT a recommended practice when uploading a data science project to GitHub?*
Anonymous Quiz
16%
A) Including a well-written README.md with setup and usage instructions
69%
B) Uploading large raw datasets directly into the repository
7%
C) Organizing code into modular scripts under a src/ folder
8%
D) Providing a requirements.txt or environment.yml for dependencies
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