Top 20 Data Science Interview Questions with Answers

Hey, I am gonna share some Top 20 Data Science Interview Questions with Answers for better preparation. 

1. Python: 

Question 1: What is the difference between a shallow copy and a deep copy in Python? 

Answer: In Python, a shallow copy creates a new object but doesn’t clone nested objects. It references the original objects. On the other hand, a deep copy creates a completely independent copy of the object and all the objects nested within it. 

Question 2: Explain the purpose of the ‘yield’ keyword in Python. 

Answer: The ‘yield’ keyword is used in Python to turn a function into a generator. It produces a  value each time the ‘yield’ statement is encountered, allowing the function to resume from where it left off, maintaining its local state. 

Question 3: How does Python’s garbage collection work? 

Answer: Python uses automatic memory management, and its garbage collector identifies and reclaims unused memory. The most common method is reference counting, where objects are deallocated when their reference count drops to zero. Additionally, cyclic garbage collection is employed to detect and collect objects with circular references. 

Question 4: Differentiate between ‘append()’ and ‘extend()’ methods in Python. 

Answer: The ‘append()’ method adds a single element to the end of a list, while the ‘extend()’  method appends elements from an iterable (e.g., a list, tuple) to the end of the list, effectively extending its length. 

Question 5: What is the Global Interpreter Lock (GIL) in Python, and how does it impact multithreading? 

Answer: The Global Interpreter Lock (GIL) in Python is a mutex that allows only one native thread to execute Python bytecode at a time. This impacts multithreading by limiting parallel execution in multi-threaded programs, as only one thread can execute Python code at a time.  However, it doesn’t hinder multi-processing. 

 

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2. SQL: 

Question 1: What is a SQL JOIN, and explain the different types of JOINs? 

Answer: A SQL JOINS combines rows from two or more tables based on a related column. The  main types of JOINs are INNER JOIN (returns matching rows), LEFT JOIN (returns all rows 

from the left table and matching rows from the right), RIGHT JOIN (vice versa of LEFT JOIN),  and FULL OUTER JOIN (returns all rows when there is a match in either table). 

Question 2: Describe the purpose of the SQL GROUP BY clause. 

Answer: The GROUP BY clause in SQL is used to arrange identical data into groups. It works with aggregate functions like COUNT(), SUM(), AVG(), etc., allowing you to perform calculations on each group of data rather than on the entire dataset. 

Question 3: Explain the difference between UNION and UNION ALL in SQL. 

Answer: UNION and UNION ALL are used to combine results from multiple SELECT  statements. The key difference is that UNION removes duplicate rows, while UNION ALL  includes all rows, even if they are duplicates. 

Question 4: How does the SQL WHERE clause differ from the HAVING clause? 

Answer: The WHERE clause filters rows before they are grouped or aggregated, while the  HAVING clause filters the result set after grouping or aggregation based on specified conditions. 

Question 5: What is the purpose of the SQL INDEX, and how does it impact query performance? 

Answer: An SQL INDEX is a data structure that improves the speed of data retrieval operations on a database table. It works by creating a copy of part of the table data, allowing the database to locate information more quickly and reduce the number of rows that need to be examined. 

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3. Machine Learning (ML): 

Question 1: What is the bias-variance tradeoff in machine learning? 

Answer: The bias-variance tradeoff is a key concept in ML. Bias refers to the error introduced by approximating a real-world problem, and variance is the amount the estimate would change if different training data were used. The tradeoff aims to find the right level of model complexity to minimize both bias and variance, achieving optimal predictive performance. 

Question 2: Explain the difference between supervised and unsupervised learning. 

Answer: In supervised learning, the model is trained on a labeled dataset, where the algorithm learns the relationship between input features and corresponding target labels. Unsupervised learning deals with unlabeled data, aiming to discover patterns or relationships within the data without predefined outcomes. 

Question 3: What is cross-validation, and why is it important in machine learning?

Answer: Cross-validation is a technique used to assess a model’s performance by splitting the dataset into multiple subsets, training the model on some, and evaluating it on others. It helps  ensure the model generalizes well to new, unseen data and provides a more robust performance estimate. 

Question 4: Differentiate between precision and recall in the context of classification metrics. 

Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the total actual positives. Precision emphasizes the accuracy of positive predictions, while recall focuses on capturing all actual positive instances. 

Question 5: What is feature engineering in machine learning? 

Answer: Feature engineering involves selecting, transforming, or creating new features from the raw data to improve a model’s performance. It aims to highlight relevant information, reduce noise, and enhance the overall representation of the data, ultimately leading to better model predictions. 

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4. Deep Learning (DL): 

Question 1: What is the vanishing gradient problem in deep learning? 

Answer: The vanishing gradient problem occurs during backpropagation when the gradients of the loss function become extremely small, leading to negligible updates to the model’s weights.  This hinders the training of deep neural networks, especially those with many layers. 

Question 2: Explain the purpose of activation functions in deep learning. 

Answer: Activation functions introduce non-linearities to the neural network, enabling it to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid,  and Tanh, each serving different purposes in capturing and propagating information through the network. 

Question 3: What is transfer learning, and how is it applied in deep learning? 

Answer: Transfer learning involves leveraging a pre-trained neural network on a related task to improve performance on a new task. This approach saves computational resources and training time, especially when working with limited data for the target task. 

Question 4: Describe the role of dropout in neural networks. 

Answer: Dropout is a regularization technique used in neural networks to prevent overfitting.  During training, random nodes (neurons) are “dropped out” or deactivated, forcing the network to learn redundant representations and enhancing its generalization capabilities.

Question 5: What is the significance of the term “batch size” in deep learning? 

Answer: The batch size represents the number of training examples utilized in one iteration. It impacts both the speed and performance of model training. A larger batch size can lead to faster training times but may require more memory, while a smaller batch size offers slower training but potentially better convergence. 

 

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Author:-

Nishesh Gogia

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