In this article, we will provide you the PDF of **50+ MCQs with Answers on Data Science**. This MCQ will help you score good marks in the final exam. These all **Data Science MCQs** are prepared by experts at heavycoding.com.

# Multiple Choice Questions on Data Science with Answers

**Data science is a multidisciplinary** field that involves the extraction of knowledge and insights from data using various techniques and methods, including statistical analysis, machine learning, and data visualization. It involves the application of scientific and statistical methods to extract insights from large, complex datasets and to uncover patterns, trends, and correlations in the data. **Data science** can be applied to a wide range of fields, including business, healthcare, social sciences, and many others, to help organizations make informed decisions, identify opportunities, and improve their performance.

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**What is Data Science?**A. The process of extracting valuable insights from data

B. The process of designing a data storage system

C. The process of creating data visualizations

D. The process of building predictive models

Answer: A

**Which of the following is not a type of data?**A. Quantitative data

B. Qualitative data

C. Categorical data

D. Statistical data

Answer: D

**What is the main purpose of data preprocessing?**A. To make the data easier to read

B. To make the data easier to visualize

C. To make the data more useful for analysis

D. To make the data more interesting

Answer: C

**What is a histogram?**A. A bar graph that shows the distribution of categorical data

B. A plot of two variables that shows their correlation

C. A plot of a single variable that shows its distribution

D. A plot of a function that shows its shape

Answer: C

**What is supervised learning?**A. Learning from labeled data

B. Learning from unlabeled data

C. Learning from text data

D. Learning from image data

Answer: A

**What is unsupervised learning?**A. Learning from labeled data

B. Learning from unlabeled data

C. Learning from text data

D. Learning from image data

Answer: B

**What is the purpose of clustering?**A. To group similar data points together

B. To separate dissimilar data points

C. To reduce the dimensionality of data

D. To visualize high-dimensional data

Answer: A

**What is the purpose of dimensionality reduction?**A. To group similar data points together

B. To separate dissimilar data points

C. To reduce the dimensionality of data

D. To visualize high-dimensional data

Answer: C

**Which of the following is not a dimensionality reduction technique?**A. Principal Component Analysis (PCA)

B. Linear Discriminant Analysis (LDA)

C. k-Nearest Neighbors (k-NN)

D. t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer: C

**What is a decision tree?**A. A tree-like model of decisions and their possible consequences

B. A tree that shows the distribution of categorical data

C. A plot of a single variable that shows its distribution

D. A plot of a function that shows its shape

Answer: A

**What is a random forest?**A. A group of decision trees

B. A group of random data points

C. A group of data scientists

D. A group of data visualizations

Answer: A

**What is overfitting?**A. When a model is too simple and fails to capture the complexity of the data

B. When a model is too complex and fits the noise in the data

C. When a model is just right and captures the essence of the data

D. When a model is not trained on enough data

Answer: B

**What is cross-validation?**A. A technique for validating a model by testing it on new data

B. A technique for validating a model by testing it on the same data

C. A technique for validating a model by randomly selecting data points

D. A technique for validating a model by fitting it to a subset of the data

Answer: A

**What is regularization?**A. A technique for preventing overfitting by adding a penalty term to the model’s objective function

B. A technique for preventing underfitting by adding more data to the training set

C. A technique for preventing bias by randomly selecting data points

D. A technique for reducing the variance of a model by decreasing the number of parameters

Answer: A

**What is hyperparameter tuning?**A. The process of selecting the best hyperparameters for a model

B. The process of selecting the best features for a model

C. The process of selecting the best algorithm for a model

D. The process of selecting the best data preprocessing technique for a model

Answer: A

**What is a confusion matrix?**A. A matrix that shows the true positives, true negatives, false positives, and false negatives of a classifier

B. A matrix that shows the correlation between two variables

C. A matrix that shows the distribution of categorical data

D. A matrix that shows the number of data points in each cluster

Answer: A

**What is precision?**A. The ratio of true positives to true negatives

B. The ratio of true positives to false positives

C. The ratio of false positives to true negatives

D. The ratio of false positives to false negatives

Answer: B

**What is recall?**A. The ratio of true positives to true negatives

B. The ratio of true positives to false positives

C. The ratio of false positives to true negatives

D. The ratio of true positives to false negatives

Answer: D

**What is F1 score?**A. The harmonic mean of precision and recall

B. The arithmetic mean of precision and recall

C. The geometric mean of precision and recall

D. The maximum of precision and recall

Answer: A

**What is ROC curve?**A. A plot of the true positive rate versus the false positive rate at different classification thresholds

B. A plot of two variables that shows their correlation

C. A plot of a single variable that shows its distribution

D. A plot of a function that shows its shape

Answer: A

**What is AUC?**A. The area under the ROC curve

B. The area under the precision-recall curve

C. The area under the cumulative distribution function

D. The area under the density function

Answer: A

**What is a support vector machine (SVM)?**A. A classifier that finds the hyperplane that maximally separates the data

B. A classifier that finds the centroid of each class

C. A classifier that finds the principal components of the data

D. A classifier that finds the decision tree that minimizes the information gain

Answer: A

**What is a neural network?**A. A network of artificial neurons that can learn from data

B. A network of real neurons that can learn from data

C. A network of decision trees that can learn from data

D. A network of support vector machines that can learn from data

Answer: A

**What is a convolutional neural network (CNN)?**A. A type of neural network that can recognize patterns in images

B. A type of neural network that can recognize patterns in text

C. A type of neural network that can recognize patterns in sound

D. A type of neural network that can recognize patterns in time series data

Answer: A