Flashcards

Data Analysis

Extracting insights from data

Techniques and methodologies used for analyzing large data sets to discover meaningful insights.

5 minutes 5 Questions

Data Analysis in the context of Big Data Science involves the systematic examination of data to extract meaningful patterns, insights, and conclusions. This process begins with collecting large volumes of structured and unstructured data from various sources such as databases, APIs, websites, senso…

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Big Data Scientist - Data Analysis Example Questions

Test your knowledge of Data Analysis

Question 1

What is k-means clustering?

K-means clustering is a method to analyze time series data. K-means clustering is a method to group dissimilar data points together. K-means clustering is a method to analyze the relationship between variables in a dataset. K-means clustering is a method to group similar data points together. K-means clustering is a method to predict future outcomes. K-means clustering is a method to address missing data in a dataset.

Correct Answer: K-means clustering is a method to group similar data points together.

K-means clustering is a method to group similar data points together based on distance measurements. It is often used in exploratory data analysis.

Question 2

What is the difference between a correlation matrix and a covariance matrix?

A correlation matrix is used to measure the similarity between two variables while a covariance matrix is used to measure the dissimilarity. A correlation matrix measures the strength of the linear relationship between two variables while a covariance matrix measures the degree of variability between two variables. A correlation matrix is suitable for use with non-linear data while a covariance matrix is suitable for use with linear data. A correlation matrix takes into account the mean of the variables while a covariance matrix does not. A correlation matrix measures the degree of variability between two variables while a covariance matrix measures the strength of the linear relationship between them.

Correct Answer: A correlation matrix measures the strength of the linear relationship between two variables while a covariance matrix measures the degree of variability between two variables.

A correlation matrix is used to measure the strength of the linear relationship between two variables by computing the correlation coefficient between them. A covariance matrix, on the other hand, measures the degree of variability between two variables, indicating whether the variables fluctuate in the same direction (positive covariance) or in opposite directions (negative covariance). The key difference between these two matrices is that a correlation matrix measures the strength of the linear relationship between two variables whereas a covariance matrix measures the degree of variability between them.

Question 3

What is the difference between regression and classification?

Classification estimates the probability of a particular class. Regression estimates continuous values. Classification is used for unsupervised learning. Regression is used for supervised learning. Regression estimates discrete values. Classification estimates continuous values. Regression estimates the probability of a particular class. Classification estimates continuous values. Regression is used for unsupervised learning. Classification is used for supervised learning. Regression estimates continuous values. Classification estimates discrete values.

Correct Answer: Regression estimates continuous values. Classification estimates discrete values.

Regression is used to estimate the relationship between a dependent variable and one or more independent variables. Classification is used to estimate the probability of a categorical outcome.

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