Data Science MCQs with answers Page - 9

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. What is the primary role of a Data Scientist in a business context?

  • (A) Conducting market research
  • (B) Developing marketing campaigns
  • (C) Extracting data from databases
  • (D) Leveraging data for insights

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Q. What is the primary objective of exploratory data analysis (EDA) in Data Science?

  • (A) To make predictions
  • (B) To find patterns
  • (C) To summarize data
  • (D) To transform data

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Q. Which of the following is NOT typically considered a part of the Data Science process?

  • (A) Data Collection
  • (B) Data Visualization
  • (C) Data Cleaning
  • (D) Software Development

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Q. Which of the following command is used to squash the commits?

  • (A) rebase
  • (B) squash
  • (C) boot
  • (D) all of the mentioned

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Q. Which of the following analysis is usually modeled by deterministic set of equations?

  • (A) Predictive
  • (B) Data booting
  • (C) Mechanistic
  • (D) All of the mentioned

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Q. Which of the following data mining technique is used to uncover patterns in data?

  • (A) Data bagging
  • (B) There is one and only flag for every command in CLI
  • (C) Data merging
  • (D) Data Dredging

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Q. Which library in Python is commonly used for data manipulation and analysis in Data Science?

  • (A) Pandas
  • (B) Matplotlib
  • (C) Scikit-Learn
  • (D) TensorFlow

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Q. What is the term for the process of cleaning and organizing data into a structured format suitable for analysis?

  • (A) Data Transformation
  • (B) Data Visualization
  • (C) Data Aggregation
  • (D) Data Extraction

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Q. In Data Science, what is the primary purpose of data visualization?

  • (A) To make data more confusing
  • (B) To obscure patterns in data
  • (C) To help communicate insights from data
  • (D) To complicate data

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Q. Which step in the Data Science process involves assessing the quality of collected data?

  • (A) Data Collection
  • (B) Data Cleaning
  • (C) Data Validation
  • (D) Data Visualization

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