Exploring Data Science Concepts

What is the purpose of data cleaning in data science?

• The purpose of data cleaning in data science is to improve data quality for accurate analysis.

• To enhance data visualization techniques.

• To automate data collection processes.

• To increase the size of the dataset.

Which technique is commonly used for handling missing data?

• Deletion

• Substitution

• Imputation

• Aggregation

What is the difference between supervised and unsupervised learning?

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning is faster than unsupervised learning regardless of data size.

• Supervised learning requires no data for training, while unsupervised learning requires labeled data.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

Name a popular algorithm used for classification tasks.

• Support Vector Machine

• Linear Regression

• K-Means Clustering

• Decision Tree

What is the role of a confusion matrix in machine learning?

• To calculate the accuracy of a regression model.

• To determine the optimal number of clusters in clustering algorithms.

• To visualize the distribution of data points in a dataset.

• The role of a confusion matrix in machine learning is to evaluate the performance of a classification model by summarizing its correct and incorrect predictions.

Which library is widely used for data visualization in Python?

• NumPy

• Seaborn

• Matplotlib

• Pandas

What is the purpose of exploratory data analysis (EDA)?

• The purpose of exploratory data analysis (EDA) is to understand the data and uncover underlying patterns.

• To clean and preprocess data for machine learning.

• To visualize data in a static format.

• To perform complex statistical modeling.

What statistical method is used to determine the relationship between two variables?

• Regression analysis

• Hypothesis testing

• Correlation analysis

• Descriptive statistics

What is the difference between Type I and Type II errors?

• Type I error is a correct rejection; Type II error is an incorrect acceptance.

• Type I error occurs when the null hypothesis is accepted; Type II error occurs when it is rejected.

• Type I error is a false positive; Type II error is a false negative.

• Type I error is a false negative; Type II error is a false positive.

What is a common technique for feature selection in predictive modeling?

• Recursive Feature Elimination (RFE)

• Principal Component Analysis (PCA)

• Linear Regression

• K-Means Clustering