Best 100+ Data Science MCQ With Revision Notes

Data Science MCQ – As a data scientist use algorithmic, data mining, artificial intelligence, machine learning, and statistical technologies to extract, analyze and interpret vast volumes of data from various sources so that organizations can use it.

Data Science MCQ With Revision Notes

Importance of Data Science:

• Informed Decision-Making: When organizations rely on data driven insights rather than emotion they can use data science to make better decisions.
• Improved Efficiency: It can increase efficiency by streamlining processes and allocating resources more effectively.
• Competitive Advantage: Businesses might gain an edge over rivals by utilizing data for product development and customer interaction.
• Risk Reduction: Data analysis can be used to identify future problems and stop them.
• Innovation: Data science encourages innovation by identifying patterns and trends that lead to new products and services.

What is descriptive analysis, Diagnostic analysis, Predictive analysis and Prescriptive analysis in data science?

• Descriptive Analysis: In descriptive analysis, historical data is simplified and examined to provide a broad overview of past events. It usually employs techniques like data visualization and summary statistics to help people understand what happened.
• Diagnostic Analysis: The objective of diagnostic analysis is to determine the reasons behind historical occurrences or trends. More thorough data analysis is needed to uncover the causes of historical patterns and anomalies.
• Predictive Analysis: Predictive analysis employs statistical techniques and historical data to anticipate future events. Based on the patterns and connections uncovered in the data, it creates predictive models.
• Prescriptive Analysis: Prescriptive analysis goes beyond predictive analysis by making recommendations for methods to enhance outcomes. It provides useful advice and suggests ways for making decisions in order to get the desired results.

What are the benefits of data science?

• Informed Decision-Making: Data-driven insights enable better decision-making.
• Enhanced Efficiency: Resource allocation and process optimization.
• Increased Productivity: Automation of procedures and tasks will lead to higher productivity.
• Competitive Advantage: Staying ahead of the competition and data-driven innovation are competitive advantages.
• Risk Mitigation: Risk mitigation is the process of identifying and controlling potential risks.
• Personalization: Creating products and services especially for each client.
• Cost Reduction: In order to cut expenses, cost-cutting possibilities must be identified.
• Scalability: The ability to efficiently manage enormous amounts of data.
• Innovation: Innovation is the advancement of innovation through data-based results.

What is the data science process?

The data science process typically involves the following steps:

• Data Collection
• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Building and Training
• Model Evaluation
• Deployment and Implementation
• Monitoring and Maintenance

Data Science MCQ

1. Which of the following is a part of Data Science?
   a. Data Collection
   b. Data Analysis
   c. Data Visualization
   d. Data Cleaning

2. Which action is followed by a data scientist after collecting the data?
   a. Data Storage
   b. Data Cleaning
   c. Data Visualization
   d. Data Preprocessing

3. Which of the following is NOT a data science application?
   a. Predicting Stock Prices
   b. Image Recognition
   c. Generating Random Numbers
   d. Fraud Detection

4. Which model is frequently used as the benchmark for data analysis?
   a. Support Vector Machine
   b. Decision Tree
   c. Linear Regression
   d. Random Forest

5. Which language is commonly used in data science?
   a. Java
   b. C++
   c. R
   d. Python

6. Which action follows the collection of the data is carried out by a data scientist?
   a. Data Cleaning
   b. Data Integration
   c. Data Replication
   d. All of the above

7. Which one of the following focuses the identification of properties in the data?
   a. Data mining
   b. Big Data
   c. Data wrangling
   d. Machine Learning

8. Data can be categorized into _______ groups.
   a. 1
   b. 2
   c. 3
   d. 4

9. A structured data representation is known as __________.
   a. Database table
   b. Functions
   c. Data preparation
   d. Data frame

10. To tell Python that we want to activate the mean function from the Numpy package, we write __ in front of the mean.
    a. npm.
    b. np.
    c. ng.
    d. ngm.

11. Which of the following machine learning algorithms depends on the concept of bagging?
    a. K-means
    b. Naive Bayes
    c. Random Forest
    d. Support Vector Machine

12. Which of the following is essential components of data science?
    a. Data Collection, Data Cleaning, Data Analysis
    b. Data Visualization, Data Modeling, Data Deployment
    c. Data Storage, Data Retrieval, Data Deletion
    d. Data Mining, Data Entry, Data Replication

13. What step in the data science process are NOT included?
    a. Data Collection
    b. Data Analysis
    c. Quantum Computing
    d. Data Visualization

14. How many groups can data be categorized into?
    a. One
    b. Two
    c. Three
    d. Four

15. Unstructured data is not organized.
    a. True
    b. False

16. Column representation of data is know as __________.
    a. Horizontal
    b. Diagonal
    c. Vertical
    d. Top

17. Only one time raw data can be processed.
    a. True
    b. False

18. What is the common goal of statistical modeling?
    a. Inference
    b. Summarizing
    c. Subsetting
    d. None of the above

19. Census data is analysis when the causal data is accured.
    a. True
    b. False

20. Which of the following models serves as the industry standard when it comes to data analysis?
    a. Inferential
    b. Descriptive
    c. Causal
    d. All of the above

21. Which of the following is a revision control system?
    a. Git
    b. Numpy
    c. Scipy
    d. Slidify

22. Which of the following is disadvantage of decision trees.
    a. They can easily overfit the data.
    b. They are not suitable for classification.
    c. They are computationally expensive.
    d. They have high bias and low variance.

23. Which of the following is not a part of supervised learning?
    a. Linear Regression
    b. K-means Clustering
    c. Decision Tree Classification
    d. Support Vector Machine

24. Determine the clustering technique that handles data variance.
    a. Hierarchical Clustering
    b. K-means Clustering
    c. DBSCAN
    d. Agglomerative Clustering

25. Which of the following options focuses on the discovery of unknown properties in the data.
    a. Supervised Learning
    b. Unsupervised Learning
    c. Reinforcement Learning
    d. Deep Learning

26. Inference engines work on the ____________ principle.
    a. Inductive Reasoning
    b. Deductive Reasoning
    c. Abductive Reasoning
    d. Bayesian Reasoning

27. Components of an expert system are?
    a. Knowledge Base, Inference Engine, User Interface
    b. Data Storage, Data Processing, Data Visualization
    c. Sensors, Actuators, Logic Gates
    d. Data Mining, Machine Learning, Data Cleaning

28. How many different kinds of observing environments exist?
    a. One
    b. Two
    c. Three
    d. Four

29. What is another term for data dredging?
    a. Data Snooping
    b. Data Mining
    c. Data Analysis
    d. Data Cleansing

30. Which of the following algorithms uses the least memory out of the options provided?
    a. Random Forest
    b. Decision Tree
    c. k-Nearest Neighbors (k-NN)
    d. Naive Bayes

31. What are different machine learning methods?
    a. Supervised Learning, Unsupervised Learning, Reinforcement Learning
    b. Data Cleaning, Data Analysis, Data Visualization
    c. Neural Networks, Decision Trees, Regression
    d. Linear Algebra, Calculus, Statistics

32. The different types of machine learning are?
    a. Regression, Classification, Clustering
    b. Data Cleaning, Data Analysis, Data Visualization
    c. Neural Networks, Decision Trees, Random Forest
    d. Supervised Learning, Unsupervised Learning, Reinforcement Learning

33. Which generation of computers are related with artificial intelligence?
    a. First Generation
    b. Second Generation
    c. Third Generation
    d. Fifth Generation

34. Which of the following is essential data science skill?
    a. Data Collection
    b. Data Analysis
    c. Data Visualization
    d. Data Cleaning

35. Which action follows the collection of the data is carried out by a data scientist?
    a. Data Storage
    b. Data Cleaning
    c. Data Visualization
    d. Data Preprocessing

36. Which of the following is NOT a data science application?
    a. Predicting Stock Prices
    b. Image Recognition
    c. Generating Random Numbers
    d. Fraud Detection

37. What is the main objective of data preprocessing in data science?
    a. To make the data fit on a single computer
    b. To remove outliers from the data
    c. To transform raw data into a usable format
    d. To create visualizations of the data

38. Which of the following Python libraries is most frequently used for data analysis and manipulation?
    a. TensorFlow
    b. Keras
    c. Pandas
    d. Matplotlib

39. What is the acronym for PEAS?
    a. Programming, Engineering, Algorithms, Systems
    b. Performance measure, Environment, Actuators, Sensors
    c. Processing, Evaluation, Analysis, Synthesis
    d. Programming, Evaluation, Algorithms, Synthesis

40. Which of the foloowing model usally a gold standard for data analysis.
    a. Logistic Regression
    b. Decision Tree
    c. Linear Regression
    d. Naive Bayes

41. Data fishing is also known as ____________.
    a. Data Snooping
    b. Data Mining
    c. Data Analysis
    d. Data Cleansing

42. CLI stands for____________.
    a. Command Line Instruction
    b. Command Line Integration
    c. Command Line Interface
    d. Command Line Interpretation

43. Time differences represented in various units are referred to as time deltas.
    a. True
    b. False

44. Which of the following DOES NOT constitute an appropriate data science application in the healthcare industry?
    a. Predicting Disease Outcomes
    b. Drug Discovery
    c. Image-Based Diagnosis
    d. Stock Market Prediction

45. Identify which CLI command is incorrect.
    a. cd myfolder
    b. ls -l
    c. RUN app.py
    d. mkdir newfolder

46. Total principles of analytical graphs that exist are ______________.
    a. Five
    b. Seven
    c. The number may vary
    d. Ten

47. Knowledge in AI represented as ____________.
    a. Rules
    b. Equations
    c. Images
    d. Colors

48. Which of the SGD variations below depends on both momentum and adaptive learning?
    a. Stochastic Gradient Descent (SGD)
    b. AdaGrad
    c. Adam (Adaptive Moment Estimation)
    d. RMSprop

49. Which output of an activation function is zero-centered?
    a. Sigmoid
    b. ReLU (Rectified Linear Unit)
    c. Tanh (Hyperbolic Tangent)
    d. Leaky ReLU

50. Which of the following logic operations cannot be carried out by a two-input perceptron?
    a. AND
    b. OR
    c. NOT
    d. XOR

51. Which of the following method used to train and test the model based on data point in ML.
    a. Validation data
    b. Test data
    c. Training data
    d. Unlabeled data

52. Which of the following represents a machine learning classification problem?
    a. Predicting stock prices
    b. Image recognition
    c. Sentiment analysis
    d. Regression analysis

53. What does “overfitting” mean in machine learning?
    a. The model performs on the training data but poorly on new or unseen data.
    b. The model has few parameters.
    c. The model cannot fit on the training data.
    d. The model performs equally well on training and test data.

54. For classification and regression tasks which of the following Bayes theorem is used in Machine Learning algorithm?
    a. k-Nearest Neighbors (k-NN)
    b. Decision Trees
    c. Naive Bayes
    d. Support Vector Machines (SVM)

55. What is the main objective of machine learning dimensionality reduction techniques?
    a. To increase the number of features in the data
    b. To reduce the number of features in the data while preserving important information
    c. To make the data more complex
    d. To create new features from existing ones

56. What does “SQL” stand for when referring to databases and data science?
    a. Structured Query Language
    b. Sequential Query Logic
    c. Simple Query Layer
    d. Standardized Query Line

57. Which type of data having a fixed data structure with rows and columns?
    a. Unstructured data
    b. Semi-structured data
    c. Structured data
    d. NoSQL data

58. Which Machine Learning Library is not a part of python.
    a. NumPy
    b. Scikit-learn
    c. TensorFlow
    d. Matplotlib

59. What is the main objective for collecting data for data analysis?
    a. To increase the size of the dataset
    b. To reduce the dimensionality of the dataset
    c. To select a representative subset of the data
    d. To remove missing values from the dataset

60. In data science, what is the main objective of data preprocessing?
    a. To collect more data
    b. To visualize data
    c. To prepare and clean data for analysis
    d. To build machine learning models

61. Which programming language is used in data science for data analysis and data manipulation?
    a. Java
    b. Python
    c. C++
    d. Ruby

62. What does exploratory data analysis (EDA) do in data science?
    a. To build predictive models
    b. To visualize data
    c. To clean data
    d. To deploy machine learning algorithms

63. Which of the following is not a of data type in Data Science?
    a. Integer
    b. Float
    c. String
    d. Loop

64. What does data science use to translate category data into numerical values?
    a. Data visualization
    b. Data preprocessing
    c. Data transformation
    d. Data exploration

65. Which statistical metric in data science best captures the central tendency of a dataset?
    a. Standard deviation
    b. Range
    c. Mean
    d. Variance

66. What is the function of feature engineering in data science?
    a. To design new machine learning algorithms
    b. To create visualizations
    c. To transform raw data into informative features for modeling
    d. To build data pipelines

67. What is the most popular data visualization tool in data science for producing interactive and dynamic visualizations?
    a. Matplotlib
    b. Seaborn
    c. Tableau
    d. Pandas

68. What is machine learning’s main objective in data science?
    a. To explore data
    b. To build predictive models and make predictions
    c. To clean and preprocess data
    d. To visualize data

69. Which of the following supervised learning algorithms is utilized in data science for classification tasks?
    a. k-Means
    b. Principal Component Analysis (PCA)
    c. Random Forest
    d. Hierarchical Clustering

70. What is the main goal of data science clustering algorithms?
    a. To classify data into predefined categories
    b. To reduce the dimensionality of data
    c. To group similar data points based on their characteristics
    d. To perform regression analysis

71. Which Python data structure is frequently used in data science to store and manipulate tabular data?
    a. List
    b. Dictionary
    c. DataFrame (from pandas)
    d. Array

72. What is the main objective of hypothesis testing in data science?
    a. To make predictions
    b. To explore data
    c. To test if a hypothesis about a population is supported by sample data
    d. To perform clustering

73. Which data science method includes developing a model on one set of data and analyzing its performance on an other, separate set of data?
    a. Cross check validation
    b. Feature validation
    c. Hypothesis validation
    d. Holdout validation
    

75. Which data science method uses existing data patterns to fill missing values in a dataset?
    a. Feature selection
    b. Data visualization
    c. Data cleaning
    d. Missing data imputation

76. In natural language processing applications use which of the following text categorization and sentiment analysis algorithms?
    a. k-Means
    b. Linear Regression
    c. Support Vector Machine (SVM)
    d. Naive Bayes

77. What is the main objective of dimensionality reduction methods in data science similar to Principal Component Analysis (PCA)?
    a. To increase the number of features
    b. To add noise to the data
    c. To reduce the dimensionality of data while preserving important information
    d. To overfit the data

78. Which data science procedure involves to converting data into a format appropriate for modeling or analysis?
    a. Feature engineering
    b. Data preprocessing
    c. Data visualization
    d. Hypothesis testing

79. What is the main objective of time series analysis in data science?
    a. To classify data
    b. To predict future values based on past observations
    c. To perform clustering
    d. To visualize data

80. Which data science method divides a dataset into training and testing sets to assess the performance of a model?
    a. Feature engineering
    b. Cross-validation
    c. Hypothesis testing
    d. Train-test split

81. What is the objective of cross-validation in data science?
    a. To preprocess data
    b. To perform clustering
    c. To evaluate the performance of a machine learning model on multiple subsets of the data
    d. To visualize data

82. What is a data scientist’s main objective when performing A/B testing?
    a. To visualize data
    b. To explore data
    c. To test the impact of a change or treatment on a specific metric
    d. To perform clustering

83. What data science method evaluates the significance of characteristics in a machine learning model?
    a. Hypothesis testing
    b. Feature selection
    c. Data cleaning
    d. Cross-validation

84. What is the main objective of anomaly detection in data science?
    a. To identify unusual or suspicious patterns in data
    b. To clean and preprocess data
    c. To perform regression analysis
    d. To visualize data

85. What data science method reduces the influence of outliers in a dataset?
    a. Data visualization
    b. Data cleaning
    c. Data transformation
    d. Robust scaling

86. In data science, what is the main objective of data transformation?
    a. To increase the dimensionality of data
    b. To add noise to the data
    c. To convert data into a more suitable format for analysis or modeling
    d. To perform feature engineering

87. What role does a histogram play in data science?
    a. To visualize data
    b. To evaluate model performance
    c. To preprocess data
    d. To perform clustering

88. Which data science method includes identifying relationships or trends in massive datasets?
    a. Clustering
    b. Association rule mining
    c. Time series analysis
    d. Data cleaning

89. In data science, what is the main objective of data imputation?
    a. To introduce noise to the data
    b. To visualize data
    c. To replace missing values in a dataset
    d. To perform clustering

90. What is the main objective of data integration in data science?
    a. To divide a dataset into training and testing sets
    b. To preprocess data
    c. To combine data from multiple sources into a unified dataset
    d. To perform feature engineering

91. Which of the following is a standard R library for data analysis and manipulation?
    a. Pandas
    b. Scikit-Learn
    c. ggplot2
    d. Keras

92. What is the main reason that data augmentation is used in data science, particularly in computer vision tasks?
    a. To increase the size of the dataset
    b. To reduce model complexity
    c. To perform feature engineering
    d. To remove outliers from the data

93. What is the main objective of time complexity analysis in data science?
    a. To explore data
    b. To evaluate model performance
    c. To analyze the efficiency of algorithms in terms of their running time
    d. To visualize data

94. Which of the following approaches is typically used to handle class imbalance in data science classification tasks?
    a. Oversampling the majority class
    b. Undersampling the minority class
    c. Both A and B
    d. Neither A nor B

95. In data science, what is the main objective of data munging (data wrangling)?
    a. To create data visualizations
    b. To clean and prepare raw data for analysis
    c. To perform feature selection
    d. To evaluate model performance

96. What is the main objective of k-Means clustering in data science?
    a. To perform regression analysis
    b. To classify data into predefined categories
    c. To group similar data points based on their characteristics
    d. To visualize data

97. Which of the following is a standard Python library for data science and machine learning?
    a. NumPy
    b. TensorFlow
    c. Matplotlib
    d. All of the above

98. What is the main objective of data science time series forecasting?
    a. To explore data
    b. To visualize data
    c. To predict future values based on past observations
    d. To perform clustering

99. Which of the following is a typical algorithm used for data science regression tasks?
    a. k-Means
    b. Decision Tree
    c. Naive Bayes
    d. Logistic Regression

Chapterwise MCQs on Artificial Intelligence

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