10 Common Interview Questions and Answers as a Data Scientist

Data Science is the most sexiest technology among the leading and most popular technologies in the world today. Most of the established organizations are hiring professionals in this field. With the high demand and low availability of these professionals, Data Scientists are the highest-paid professionals in the developed countries. I am planning to post several articles in this connection. Here is a list of 10 common interview questions and answer as a Data Scientist:

1). What is data science?

Ans: Data Science is an interdisciplinary field that uses statistical and computational methods to extract insights and knowledge from data.

2). What is the difference between supervised and unsupervised learning?

Ans: Supervised learning is where you have input and output data and the algorithm learns to map inputs to outputs. Unsupervised learning is where you only have input data and the algorithm must find patterns or relationships within the data.

3). What are the steps involved in the data science process?

Ans: 1) Problem definition, 2) Data collection, 3) Data exploration, 4) Data preparation, 5) Modeling, 6) Evaluation, and 7) Deployment.

4). What are the common libraries used in Python ?

Ans: Numpy, Pandas, Matplotlib, Seaborn, Sklearn, Tensorflow, Keras.

5). What is the curse of dimensionality?

Ans: The curse of dimensionality refers to the challenges in processing high-dimensional data, such as difficulty in visualization, increased computational complexity, and decreased model performance.

6). What is overfitting in machine learning?

Ans: Overfitting is a common problem in machine learning where a model has high accuracy on the training data but poor accuracy on new, unseen data, due to model fitting too closely to the noise in the training data.

7). What is regularization and how does it help in avoiding overfitting?

Ans: Regularization is a method to reduce model complexity by adding a penalty term to the loss function. This helps in avoiding overfitting by preventing the model from fitting too closely to the training data.

8). What is cross-validation and why is it important?

Ans: Cross-validation is a process of splitting the data into multiple parts, training the model on different parts, and evaluating its performance on the remaining parts. It helps in reducing the risk of overfitting and ensuring the model generalizes well to new data.

9). How do you handle missing values in your data?

Ans: Missing values can be handled using techniques like mean/median/mode imputation, regression imputation, multiple imputation, etc. depending on the data and problem context.

10). How do you deal with class imbalance in your data?

Ans: Class imbalance can be addressed using techniques like oversampling, undersampling, and synthetic data generation.

In this tutorial, I tried to brief some common interview questions and answers as a Data Scientist. Hope you have enjoyed the tutorial. If you want to get updated, like my facebook page https://www.facebook.com/LearningBigDataAnalytics and stay connected.

Add a Comment