Data Science MCQ (Multiple Choice Questions)

Data Science MCQ - Multiple Choice Questions and Answers

Our 1000+ Data Science MCQs (Multiple Choice Questions and Answers) focuses on all chapters of Data Science covering 100+ topics. You should practice these MCQs for 1 hour daily for 2-3 months. This way of systematic learning will prepare you easily for Data Science exams, contests, online tests, quizzes, MCQ-tests, viva-voce, interviews, and certifications.

Data Science Multiple Choice Questions Highlights

- 1000+ Multiple Choice Questions & Answers (MCQs) in Data Science with a detailed explanation of every question.
- These MCQs cover theoretical concepts, true-false(T/F) statements, fill-in-the-blanks and match the following style statements.
- These MCQs also cover numericals as well as diagram oriented MCQs.
- These MCQs are organized chapterwise and each Chapter is futher organized topicwise.
- Every MCQ set focuses on a specific topic of a given Chapter in Data Science Subject.

Who should Practice Data Science MCQs?

– Students who are preparing for college tests and exams such as mid-term tests and semester tests on Data Science.
- Students who are preparing for Online/Offline Tests/Contests in Data Science.
– Students who wish to sharpen their knowledge of Data Science Subject.
- Anyone preparing for Aptitude test in Data Science.
- Anyone preparing for interviews (campus/off-campus interviews, walk-in interview and company interviews).
- Anyone preparing for entrance examinations and other competitive examinations.
- All - Experienced, Freshers and College / School Students.

Data Science Chapters

Here's the list of chapters on the "Data Science" subject covering 100+ topics. You can practice the MCQs chapter by chapter starting from the 1st chapter or you can jump to any chapter of your choice.

  1. Data Science Basics and Data Scientist Toolbox
  2. Data Analysis with Python
  3. Getting Data
  4. Data Analysis and Research
  5. Statistical Inference and Regression Models
  6. Machine Learning
  7. Developing Data Products and Working with NumPy

1. Data Science Basics and Data Scientist Toolbox

The section contains multiple choice questions and answers on basics of data sciences and toolbox, workflow of CLI and git, big data analysis and experimental design.

  • Basics of Data Science
  • ToolBox Overview
  • CLI and Git Workflow-1
  • CLI and Git Workflow-2
  • Types of Questions-1
  • Types of Questions-2
  • Big Data
  • Analysis and Experimental Design
  • 2. Data Analysis with Python

    The section contains questions and answers on pandas, time deltas, python plotting, data structures and computational tools.

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  • Time Deltas
  • Plotting in Python
  • Computational Tools
  • Pandas Data Structure
  • Pandas – 1
  • Pandas – 2
  • Pandas – 3
  • 3. Getting Data

    The section contains MCQs on raw data, processed data, tidy data, web reading, API, data summarization and merging, regular expressions and text variables.

  • Raw and Processed Data
  • Tidy Data
  • Reading from Web and APIs-1
  • Reading from Web and APIs-2
  • Summarizing and Merging Data
  • Regular Expressions and Text Variables
  • 4. Data Analysis and Research

    The section contains multiple choice questions and answers on graphical devices and plotting systems, basics of reproducible research, clustering, exploratory graphs and basics of literate statistical programming.

  • Graphics Devices-1
  • Graphics Devices-2
  • Plotting Systems
  • Clustering
  • Exploratory Graphs
  • Introduction to Reproducible Research
  • knitr
  • Literate Statistical Programming – 1
  • Literate Statistical Programming – 2
  • 5. Statistical Inference and Regression Models

    The section contains questions and answers on probability and statistics, basics of statistical inference, regression models, distributions and likelihood, binary and count outcomes and residual variations.

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  • Introduction to Statistical Inference
  • Probability and Statistics
  • Common Distributions
  • Likelihood
  • Statistical Inference Concepts
  • Introduction to Regression Models
  • Residual Variation and Multivariate
  • Binary and Count Outcomes
  • 6. Machine Learning

    The section contains MCQs on caret, prediction with motivation, regression and model and cross validation.

  • Caret – 1
  • Caret – 2
  • Caret – 3
  • Prediction Motivation
  • Cross Validation
  • Predicting with Regression
  • Model Based Prediction
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    7. Developing Data Products and Working with NumPy

    The section contains multiple choice questions and answers on shiny, slidify, googleVis and numPy.

  • Shiny
  • Slidify
  • googleVis
  • NumPy – 1
  • NumPy – 2
  • If you would like to learn "Data Science" thoroughly, you should attempt to work on the complete set of 1000+ MCQs - multiple choice questions and answers mentioned above. It will immensely help anyone trying to crack an exam or an interview.

    Wish you the best in your endeavor to learn and master Data Science!

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    Manish Bhojasia - Founder & CTO at Sanfoundry
    Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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