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Data Science Informatics

Quiz by Robert

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10 questions
Show answers
  • Q1
    Which of the following techniques is primarily used for supervised learning in data science?
    Principal Component Analysis
    Linear Regression
    K-means Clustering
    Random Sampling
    30s
  • Q2
    In data visualization, which of the following is commonly used to represent the distribution of a dataset?
    Line Graph
    Pie Chart
    Bar Chart
    Histogram
    30s
  • Q3
    What is the primary purpose of using normalization in data preprocessing?
    To increase dataset size
    To remove outliers
    To scale features to a similar range
    To convert categorical data to numerical
    30s
  • Q4
    Which algorithm is commonly used for classification tasks in machine learning?
    Principal Component Analysis
    K-means Clustering
    Support Vector Machine
    Linear Regression
    30s
  • Q5
    What type of data does a confusion matrix evaluate in machine learning?
    Data completeness
    Model training time
    Predicted vs actual classifications
    Feature correlations
    30s
  • Q6
    Which of the following methods is used to handle missing data in a dataset?
    Feature Selection
    Imputation
    Normalization
    Data Augmentation
    30s
  • Q7
    What is the effect of overfitting in a machine learning model?
    The model performs well on training data but poorly on unseen data
    The model is too simple to capture the data trends
    The model generalizes well to new data
    The model has high bias
    30s
  • Q8
    Which programming language is widely used for data analysis and data science?
    HTML
    Python
    Java
    C++
    30s
  • Q9
    What is the purpose of feature engineering in machine learning?
    To visualize data distributions
    To select the best machine learning model
    To tune hyperparameters
    To create new input features from existing data
    30s
  • Q10
    Which method is commonly used for dimensionality reduction in high-dimensional datasets?
    K-means Clustering
    Principal Component Analysis
    Linear Regression
    Decision Trees
    30s

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