Data Science Quiz

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Question 1

What is the primary goal of exploratory data analysis (EDA)?

  • Making predictions

  • Summarizing data characteristics

  • Testing hypotheses

  • Model deployment

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Question 2

What does the term "outlier" refer to in statistics?

  • The mean of a dataset

  • Unusual or extreme values in a dataset

  • The mode of a dataset

  • The standard deviation of a dataset

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Question 3

In statistics, what is the purpose of the standard deviation?

  • Measuring central tendency

  • Describing the spread or dispersion of data

  • Identifying outliers

  • Calculating probabilities

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Question 4

What is the key difference between correlation and causation?

  • Correlation measures the strength of a relationship, causation implies a cause-and-effect relationship

  • Correlation and causation are synonymous terms

  • Correlation implies a cause-and-effect relationship, causation measures the strength of a relationship

  • Correlation and causation have no relationship

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Question 5

What is the purpose of a histogram in data visualization?

  • Displaying hierarchical relationships

  • Comparing multiple datasets

  • Showing the distribution of a single variable

  • Representing geographical data

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Question 6

What is the primary purpose of a scatter plot?

  • Showing the distribution of a single variable

  • Representing hierarchical relationships

  • Visualizing the relationship between two variables

  • Displaying the summary statistics of a dataset

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Question 7

In statistics, what does the term "mean" refer to?

  • The middle value of a dataset

  • The most frequently occurring value in a dataset

  • The sum of all values divided by the number of values

  • The range of values in a dataset

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Question 8

What is the purpose of the term "one-hot encoding" in machine learning?

  • Handling missing values

  • Scaling numerical features

  • Encoding categorical variables into binary vectors

  • Reducing dimensionality

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Question 9

How do you calculate the median of a dataset?

  • Sorting the data and selecting the middle value

  • Finding the average of the smallest and largest values

  • Summing all values and dividing by the number of values

  • Identifying the most frequently occurring value

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Question 10

What is the purpose of the term "cross-validation" in machine learning?

  • Reducing model complexity

  • Assessing a model's performance on multiple subsets of data

  • Combining predictions from multiple models

  • Enhancing model interpretability

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There are 26 questions to complete.

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