Data Science Section A Quiz
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Questions and Answers

What is the measure of central tendency that represents the most frequently occurring value in a dataset?

  • Median
  • Mode (correct)
  • Mean
  • Range

    • If a dataset has an even number of observations, how is the median determined?

  • The maximum value
  • The mean of the two middle values (correct)
  • The last middle value
  • The first middle value

    • Which of the following is not a measure of dispersion?

  • Range
  • Variance
  • Mode (correct)
  • Standard deviation

    • What is the range of a dataset?

    <p>The difference between the highest and lowest values</p> Signup and view all the answers

    Which measure of central tendency is most sensitive to extreme values?

    <p>Mean</p> Signup and view all the answers

    What is the formula for calculating the variance?

    <p>(sum of squared deviations) / (number of values)</p> Signup and view all the answers

    Which measure of spread is equal to the square root of the variance?

    <p>Standard deviation</p> Signup and view all the answers

    What is a significant impact of Data Science on businesses?

    <p>Improved decision-making and efficiency</p> Signup and view all the answers

    What are the three key components of Data Science?

    <p>Data, Statistics, and Visualization</p> Signup and view all the answers

    Which of the following is a supervised learning technique?

    <p>Linear Regression</p> Signup and view all the answers

    What is the difference between precision and recall?

    <p>Precision measures the number of true positives, while recall measures the number of false negatives</p> Signup and view all the answers

    Which of the following is a data visualization technique?

    <p>Box Plot</p> Signup and view all the answers

    What is the goal of feature engineering?

    <p>To transform the features into a more suitable representation for a machine learning algorithm</p> Signup and view all the answers

    What is the purpose of cross-validation?

    <p>To ensure that the model is not overfitting the data</p> Signup and view all the answers

    What is the purpose of hypothesis testing in data science?

    <p>To determine if a sample statistic is significantly different from a population parameter</p> Signup and view all the answers

    How can you define a function in Python that accepts an arbitrary number of positional arguments?

    <p>Using the *args parameter</p> Signup and view all the answers

    Which data structure is primarily used in NumPy for handling arrays?

    <p>ndarray</p> Signup and view all the answers

    Which method is used to create a NumPy array of integers ranging from 0 to 9?

    <p>np.arange(10)</p> Signup and view all the answers

    What is the default data type of elements in a NumPy array?

    <p>Integer</p> Signup and view all the answers

    What will be the result of the operation np.array([1, 2, 3]) + np.array([4, 5, 6])?

    <p>[5, 7, 9]</p> Signup and view all the answers

    How can you access the element at the second row and third column of a NumPy array arr?

    <p>arr[2, 3]</p> Signup and view all the answers

    What followed the equation of the regression line y = 2x + 3 when x is 5?

    <p>13</p> Signup and view all the answers

    Which of the following is NOT a common assumption of linear regression?

    <p>Multicollinearity</p> Signup and view all the answers

    In logistic regression, what type of outcome does the dependent variable typically represent?

    <p>A binary outcome or category</p> Signup and view all the answers

    What is the primary purpose of conducting residual analysis in regression models?

    <p>To identify outliers and assess the model's assumptions</p> Signup and view all the answers

    When performing polynomial regression, what effect does increasing the degree of the polynomial generally have?

    <p>Overfitting the data</p> Signup and view all the answers

    Which type of regression is typically preferred when dealing with multicollinearity among independent variables?

    <p>Lasso Regression</p> Signup and view all the answers

    In the context of regression analysis, which of the following is an example of a dependent variable?

    <p>Sales</p> Signup and view all the answers

    Given the function f(x) = x^3 + 3x^2 - 24*x + 7, what is true about x=2?

    <p><em>x</em>=2 will give the minimum for <em>f</em>(x)</p> Signup and view all the answers

    What distinguishes linear regression from logistic regression?

    <p>Linear regression produces a linear outcome, while logistic regression produces a binary outcome</p> Signup and view all the answers

    Which of the following accurately describes the purpose of logistic regression?

    <p>To predict categorical variables</p> Signup and view all the answers

    Which measure indicates how well the linear regression model fits the data?

    <p>R-squared</p> Signup and view all the answers

    What does the correlation coefficient measure in regression analysis?

    <p>The strength and direction of the relationship</p> Signup and view all the answers

    What is a primary objective of k-means clustering?

    <p>To minimize the distance within clusters</p> Signup and view all the answers

    How do k-means clustering and hierarchical clustering primarily differ?

    <p>K-means uses centroids, while hierarchical uses distance measures</p> Signup and view all the answers

    What is a limitation of using k-means clustering?

    <p>It requires a priori knowledge of the number of clusters</p> Signup and view all the answers

    What is the function of a hyperparameter in the gradient descent algorithm?

    <p>To set the learning rate</p> Signup and view all the answers

    What is a disadvantage of using a low learning rate in gradient descent?

    <p>The algorithm may converge slowly</p> Signup and view all the answers

    What is the condition on a and b for which the given system of linear equations has no solution?

    <p>a ≠ 4, 2a + b − 6 = 0</p> Signup and view all the answers

    Which statement is true about the determinant of a matrix?

    <p>The determinant of a diagonal matrix is the product of its diagonal entries.</p> Signup and view all the answers

    Using the provided confusion matrix for classification, how is accuracy calculated?

    <p>(True Positive + True Negative) / Total Predictions</p> Signup and view all the answers

    What distinguishes simple linear regression from multiple regression?

    <p>Simple linear regression involves only one independent variable, while multiple involves more than one.</p> Signup and view all the answers

    What is the goal of multivariate optimization?

    <p>To find the minimum or maximum of a function with multiple variables.</p> Signup and view all the answers

    Which method can be used to find the minimum of a function with multiple variables without derivatives?

    <p>Gradient descent</p> Signup and view all the answers

    What does pruning in decision trees achieve?

    <p>Reduces the complexity of the tree by removing unnecessary branches.</p> Signup and view all the answers

    Study Notes

    Data Science Section A

    • Data Science key components are Data, Model, and Visualization
    • Supervised Learning technique is Linear Regression
    • Unsupervised learning technique is K-Means Clustering
    • Feature engineering goal is to transform features into a suitable representation for machine learning algorithms
    • Data visualization technique is Hierarchical Clustering
    • Precision measures true positives, recall measures true negatives
    • Measures for classification algorithm quality include Precision, Recall, and F1 Score
    • Cross-validation ensures the model doesn't overfit the data
    • Linear Regression and Random Forest are classification algorithms
    • Hypothesis testing purpose is to determine if a sample statistic is significantly different from a population parameter
    • Null hypothesis states that there is no significant difference between a sample statistic and a population parameter
    • Alternative hypothesis states there is a significant difference between a sample statistic and a population parameter
    • P-value is the probability of observing a sample statistic as extreme or more extreme, assuming the null hypothesis is true
    • Significance level is the probability of making a type I error
    • Type II error is failing to reject a false null hypothesis

    Data Science Section B

    • A data storage domain

    • Study of data to extract meaningful insights

    • Field restricted to structured data only

    • Impact of Data Science: Improved decision-making and efficiency, decreased profitability, or increased manufacturing costs

    • Built-in data types in Python include lists

    • Result of "Hello, " + "World!" is "Hello, World!" in Python

    • Pseudo-random numbers generated using the random module in Python

    • Primary data structure in NumPy for arrays is ndarray

    • A NumPy array containing integers from 0 to 9 can be created using np.arange(10)

    • The default data type of elements in a NumPy array is integer

    • Element access in a NumPy array is done with arr[row, column]

    • Universal functions (ufuncs) are like sqrt in NumPy

    Data Science Section C

    • Gradient descent converges to local minimum (True or False)
    • Covariance is not a better metric than correlation for analyzing association
    • Linear regression minimizes the residual sum-of-squares (SSR)
    • Cross-validation techniques include Leave-One-Out Cross-Validation (LOOCV) and k-fold cross-validation
    • Classification problems include disease diagnosis and house price prediction
    • Function predicting the y value when x=5 in a Linear Regression equation of y = 2x + 3 is 13
    • Not a common assumption in Linear Regression is linearity

    Additional Topics

    • The purpose of residual analysis in regression is to identify outliers and evaluate model assumptions
    • A type of regression suitable for multicollinearity is Lasso Regression

    Tuple Slicing

    • To set val to 20 by slicing the tuple aTuple = ("Orange", (10, 20, 30), (5, 15, 25)) is val = aTuple[1][1]
    • Example of dependent variable is Age
    • Difference between simple and multiple regression: Simple regression has one independent variable, multiple regression has more than one independent variable
    • Multivariate optimization finds the minimum or maximum of a function with multiple variables
    • A method for finding a minimum without derivatives in a function with multiple variables is Gradient Descent
    • Pruning reduces the size of a decision tree by removing unnecessary branches
    • Goal of a support vector machine (SVM) is to find the ideal decision boundary that separates the data into classes

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    Description

    Test your knowledge of key components in data science, including supervised and unsupervised learning techniques, feature engineering, and data visualization. Explore essential metrics like precision, recall, and hypothesis testing to understand classification algorithms and their quality assessment.

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