DS INTERVIEW.pdf
16.6 MB
800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant
Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.
This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:
Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.
This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:
-Statistics
-Data Science Fundamentals
-Data Analysis
-Machine Learning
-Deep Learning
-Python & R
-Model Evaluation & Optimization
-Deployment Strategies
…and much more!
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NLP techniques every Data Science professional should know!
1. Tokenization
2. Stop words removal
3. Stemming and Lemmatization
4. Named Entity Recognition
5. TF-IDF
6. Bag of Words
1. Tokenization
2. Stop words removal
3. Stemming and Lemmatization
4. Named Entity Recognition
5. TF-IDF
6. Bag of Words
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List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javascript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
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Key trends shaping the future of web development 👇👇
1. Progressive Web Apps (PWAs): PWAs are becoming more popular as they combine the best of web and mobile apps, offering a seamless experience across platforms without needing app stores.
2. WebAssembly (Wasm): WebAssembly allows developers to run code written in different languages (C++, Rust) on the web with near-native performance, enhancing web application speed and capabilities.
3. AI-Powered Web Development: Artificial Intelligence (AI) and Machine Learning (ML) will become more integrated into web development, enabling features like chatbots, personalized content, predictive search, and automated design processes.
4. Voice Search Optimization: As voice search continues to grow, web developers will focus on optimizing websites for voice-activated queries, leading to changes in search engine optimization (SEO) practices and user experience design.
5. Serverless Architecture: Serverless computing allows developers to build and deploy applications without managing infrastructure. This reduces costs, enhances scalability, and enables faster development cycles.
6. Motion UI: Animation and micro-interactions will play a bigger role in web design. Motion UI helps create engaging, interactive experiences that can improve user engagement and satisfaction.
7. 5G and Enhanced Connectivity: With the rollout of 5G, faster internet speeds and lower latency will enable more complex, real-time applications, especially in areas like augmented reality (AR), virtual reality (VR), and IoT.
8. Blockchain Integration: Web development could integrate blockchain technology for decentralized applications (dApps), offering enhanced security, transparency, and user control over data.
9. Edge Computing: By bringing computing closer to the source of data, edge computing will reduce latency and improve the performance of web applications, especially for IoT and real-time data processing.
10. Cybersecurity Focus: As web applications handle more sensitive data, the importance of robust security practices, such as multi-factor authentication (MFA), encryption, and secure development frameworks, will grow.
1. Progressive Web Apps (PWAs): PWAs are becoming more popular as they combine the best of web and mobile apps, offering a seamless experience across platforms without needing app stores.
2. WebAssembly (Wasm): WebAssembly allows developers to run code written in different languages (C++, Rust) on the web with near-native performance, enhancing web application speed and capabilities.
3. AI-Powered Web Development: Artificial Intelligence (AI) and Machine Learning (ML) will become more integrated into web development, enabling features like chatbots, personalized content, predictive search, and automated design processes.
4. Voice Search Optimization: As voice search continues to grow, web developers will focus on optimizing websites for voice-activated queries, leading to changes in search engine optimization (SEO) practices and user experience design.
5. Serverless Architecture: Serverless computing allows developers to build and deploy applications without managing infrastructure. This reduces costs, enhances scalability, and enables faster development cycles.
6. Motion UI: Animation and micro-interactions will play a bigger role in web design. Motion UI helps create engaging, interactive experiences that can improve user engagement and satisfaction.
7. 5G and Enhanced Connectivity: With the rollout of 5G, faster internet speeds and lower latency will enable more complex, real-time applications, especially in areas like augmented reality (AR), virtual reality (VR), and IoT.
8. Blockchain Integration: Web development could integrate blockchain technology for decentralized applications (dApps), offering enhanced security, transparency, and user control over data.
9. Edge Computing: By bringing computing closer to the source of data, edge computing will reduce latency and improve the performance of web applications, especially for IoT and real-time data processing.
10. Cybersecurity Focus: As web applications handle more sensitive data, the importance of robust security practices, such as multi-factor authentication (MFA), encryption, and secure development frameworks, will grow.
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Top 10 Data Science Concepts You Should Know 🧠
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
1. Data Cleaning: Garbage In, Garbage Out. You can't build great models on messy data. Learn to spot and fix errors before you start. Seriously, this is the most important step.
2. EDA: Your Data's Secret Diary. Before you build anything, EXPLORE! Understand your data's quirks, distributions, and relationships. Visualizations are your best friend here.
3. Feature Engineering: Turning Data into Gold. Raw data is often useless. Feature engineering is how you transform it into something your models can actually learn from. Think about what the data represents.
4. Machine Learning: The Right Tool for the Job. Don't just throw algorithms at problems. Understand why you're using linear regression vs. a random forest.
5. Model Validation: Are You Lying to Yourself? Too many people build models that look great on paper but fail in the real world. Rigorous validation is essential.
6. Feature Selection: Less Can Be More. Get rid of the noise! Focusing on the most important features improves performance and interpretability.
7. Dimensionality Reduction: Simplify, Simplify, Simplify. High-dimensional data can be a nightmare. Learn techniques to reduce complexity without losing valuable information.
8. Model Optimization: Squeeze Every Last Drop. Fine-tuning your model parameters can make a huge difference. But be careful not to overfit!
9. Data Visualization: Tell a Story People Understand. Don't just dump charts on a page. Craft a narrative that highlights key insights.
10. Big Data: When Things Get Serious. If you're dealing with massive datasets, you'll need specialized tools like Hadoop and Spark. But don't start here! Master the fundamentals first.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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Core data science concepts you should know:
🔢 1. Statistics & Probability
Descriptive statistics: Mean, median, mode, standard deviation, variance
Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA
Probability distributions: Normal, Binomial, Poisson, Uniform
Bayes' Theorem
Central Limit Theorem
📊 2. Data Wrangling & Cleaning
Handling missing values
Outlier detection and treatment
Data transformation (scaling, encoding, normalization)
Feature engineering
Dealing with imbalanced data
📈 3. Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis
Correlation and covariance
Data visualization tools: Matplotlib, Seaborn, Plotly
Insights generation through visual storytelling
🤖 4. Machine Learning Fundamentals
Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN
Unsupervised Learning: K-means, hierarchical clustering, PCA
Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and overfitting/underfitting
Bias-variance tradeoff
🧠 5. Deep Learning (Basics)
Neural networks: Perceptron, MLP
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation
Gradient descent and learning rate
CNNs and RNNs (intro level)
🗃️ 6. Data Structures & Algorithms (DSA)
Arrays, lists, dictionaries, sets
Sorting and searching algorithms
Time and space complexity (Big-O notation)
Common problems: string manipulation, matrix operations, recursion
💾 7. SQL & Databases
SELECT, WHERE, GROUP BY, HAVING
JOINS (inner, left, right, full)
Subqueries and CTEs
Window functions
Indexing and normalization
📦 8. Tools & Libraries
Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch
R: dplyr, ggplot2, caret
Jupyter Notebooks for experimentation
Git and GitHub for version control
🧪 9. A/B Testing & Experimentation
Control vs. treatment group
Hypothesis formulation
Significance level, p-value interpretation
Power analysis
🌐 10. Business Acumen & Storytelling
Translating data insights into business value
Crafting narratives with data
Building dashboards (Power BI, Tableau)
Knowing KPIs and business metrics
React ❤️ for more
🔢 1. Statistics & Probability
Descriptive statistics: Mean, median, mode, standard deviation, variance
Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA
Probability distributions: Normal, Binomial, Poisson, Uniform
Bayes' Theorem
Central Limit Theorem
📊 2. Data Wrangling & Cleaning
Handling missing values
Outlier detection and treatment
Data transformation (scaling, encoding, normalization)
Feature engineering
Dealing with imbalanced data
📈 3. Exploratory Data Analysis (EDA)
Univariate, bivariate, and multivariate analysis
Correlation and covariance
Data visualization tools: Matplotlib, Seaborn, Plotly
Insights generation through visual storytelling
🤖 4. Machine Learning Fundamentals
Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN
Unsupervised Learning: K-means, hierarchical clustering, PCA
Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and overfitting/underfitting
Bias-variance tradeoff
🧠 5. Deep Learning (Basics)
Neural networks: Perceptron, MLP
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation
Gradient descent and learning rate
CNNs and RNNs (intro level)
🗃️ 6. Data Structures & Algorithms (DSA)
Arrays, lists, dictionaries, sets
Sorting and searching algorithms
Time and space complexity (Big-O notation)
Common problems: string manipulation, matrix operations, recursion
💾 7. SQL & Databases
SELECT, WHERE, GROUP BY, HAVING
JOINS (inner, left, right, full)
Subqueries and CTEs
Window functions
Indexing and normalization
📦 8. Tools & Libraries
Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch
R: dplyr, ggplot2, caret
Jupyter Notebooks for experimentation
Git and GitHub for version control
🧪 9. A/B Testing & Experimentation
Control vs. treatment group
Hypothesis formulation
Significance level, p-value interpretation
Power analysis
🌐 10. Business Acumen & Storytelling
Translating data insights into business value
Crafting narratives with data
Building dashboards (Power BI, Tableau)
Knowing KPIs and business metrics
React ❤️ for more
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Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview?
I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.
The right time to start applying for data analyst positions depends on a few factors:
1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.
2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.
3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.
4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.
Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.
Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.
Hope it helps :)
I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.
The right time to start applying for data analyst positions depends on a few factors:
1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.
2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.
3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.
4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.
Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.
Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.
Hope it helps :)
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