Data Science MCQs with answers Page - 24

Here, you will find a collection of MCQ questions on Data Science. Go through these questions to enhance your preparation for upcoming examinations and interviews.

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Q. In EDA, what does the term "box" represent in a box plot?

  • (A) Median
  • (B) Mode
  • (C) Range
  • (D) Standard Deviation

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Q. What does the term "kurtosis" refer to in the context of data distribution analysis in EDA?

  • (A) The symmetry of a data distribution
  • (B) The spread of a data distribution
  • (C) The shape of a data distribution
  • (D) The presence of outliers

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Q. In EDA, what is the primary purpose of a heat map?

  • (A) To visualize the distribution of data
  • (B) To display relationships between variables
  • (C) To identify missing values
  • (D) To calculate summary statistics

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Q. What is the primary purpose of Exploratory Data Analysis (EDA) in Data Science?

  • (A) To make data more complicated
  • (B) To simplify complex data
  • (C) To discover patterns and insights in data
  • (D) To visualize data

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Q. Which of the following is NOT a common EDA technique for visualizing data distributions?

  • (A) Box Plot
  • (B) Histogram
  • (C) Scatter Plot
  • (D) Bar Chart

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Q. In EDA, what is the term for a graphical representation that displays the distribution of a continuous variable?

  • (A) Pie Chart
  • (B) Histogram
  • (C) Bar Chart
  • (D) Scatter Plot

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Q. What is the primary purpose of a scatter plot in EDA?

  • (A) To show the distribution of a single variable
  • (B) To visualize relationships between variables
  • (C) To display categorical data
  • (D) To represent time series data

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Q. In EDA, which measure of central tendency is typically represented by the height of a box in a box plot?

  • (A) Mean
  • (B) Median
  • (C) Mode
  • (D) Variance

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Q. Which EDA technique is used to identify and visualize outliers in a dataset?

  • (A) Bar Chart
  • (B) Histogram
  • (C) Box Plot
  • (D) Scatter Plot

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Q. What is the purpose of a correlation matrix in EDA?

  • (A) To visualize the distribution of data
  • (B) To display relationships between variables
  • (C) To identify missing values
  • (D) To calculate summary statistics

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