Intelligent Data Science And Analytics Assessment Test

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  • 1/10 Questions

    Given the machine data and equation while it looks for the coefficient values in the equation is termed...

    • Artificial Intelligence
    • Machine Learning
    • Machine Language
    • Programming
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About This Quiz

Although the two are different concepts, data science (or data driven science) and data analytics (or data analysis) are both focused on data. Take this quiz to find out more.

Intelligent Data Science And Analytics Assessment Test - Quiz

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  • 2. 
    Why is Python preferable for text analytics over R? - ProProfs

    Why is Python preferable for text analytics over R?

    • It is easier to learn

    • It has libraries

    • It uses data

    • It is marked up

    Correct Answer
    A. It has libraries
    Explanation
    Python is preferable for text analytics over R because it has a wide range of libraries specifically designed for text analysis tasks. These libraries, such as NLTK, spaCy, and TextBlob, provide ready-to-use functions and tools for tasks like tokenization, stemming, sentiment analysis, and named entity recognition. These libraries save time and effort for developers and researchers, allowing them to focus on the analysis itself rather than building the necessary tools from scratch. Additionally, Python's libraries are constantly updated and improved by a large community, ensuring that users have access to the latest advancements in text analytics.

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  • 3. 
    The goal of _____ is to identify any changes to a web page that might increase outcome. - ProProfs

    The goal of _____ is to identify any changes to a web page that might increase outcome.

    • Ads testing

    • A/B testing

    • A/T testing

    • S/T testing

    Correct Answer
    A. A/B testing
    Explanation
    A/B testing is a method used to compare two versions of a web page or advertisement to determine which one performs better in terms of achieving a desired outcome. By presenting different variations to a sample audience and analyzing their responses, any changes that lead to an improved outcome can be identified. This allows businesses to make data-driven decisions and optimize their web pages or ads for better results.

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  • 4. 
    The point in a data distribution where there is no bias to the left nor to the right is called... - ProProfs

    The point in a data distribution where there is no bias to the left nor to the right is called...

    • Central distribution

    • Normal distribution

    • Central tendency

    • Normal deviation

    Correct Answer
    A. Normal distribution
    Explanation
    The term "normal distribution" refers to a specific type of data distribution where the values are symmetrically distributed around the mean, with no bias towards the left or right. This means that the data is evenly spread out and follows a bell-shaped curve. Therefore, the correct answer is "Normal distribution."

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  • 5. 
    Which of these is widely used for mining classifying datasets? - ProProfs

    Which of these is widely used for mining classifying datasets?

    • Logistical regression

    • Classification technique

    • Recommender systems

    • Data merging

    Correct Answer
    A. Classification technique
    Explanation
    Classification technique is widely used for mining classifying datasets. It is a method of categorizing data into different classes or groups based on their characteristics. This technique helps in organizing and analyzing large amounts of data by assigning labels or categories to each data point. It is commonly used in various fields such as machine learning, data mining, and pattern recognition to make predictions and decisions based on the given dataset.

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  • 6. 
    Which of the following is a statistical technique in which the value of a variable A is predicted from the value of a second variable B? - ProProfs

    Which of the following is a statistical technique in which the value of a variable A is predicted from the value of a second variable B?

    • Interpolation

    • Extrapolation

    • Linear regression

    • Rectilinear regression

    Correct Answer
    A. Linear regression
    Explanation
    Linear regression is a statistical technique used to predict the value of one variable (A) based on the value of another variable (B). It assumes a linear relationship between the two variables and calculates the best-fit line that minimizes the distance between the observed data points and the predicted values. This technique is commonly used in various fields, such as economics, social sciences, and finance, to analyze and predict the relationship between variables.

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  • 7. 
    Which of these estimates gives prior knowledge about data? - ProProfs

    Which of these estimates gives prior knowledge about data?

    • Maximum likelihood estimates

    • Bayesian estimates

    • Prior estimates

    • Knowledge estimates

    Correct Answer
    A. Bayesian estimates
    Explanation
    Bayesian estimates provide prior knowledge about the data. In Bayesian estimation, prior information or beliefs about the parameters of the data are incorporated into the analysis. This prior knowledge is combined with the observed data to obtain the posterior distribution, which represents the updated beliefs about the parameters. Therefore, Bayesian estimates take into account prior information, making them a suitable choice for incorporating existing knowledge into the analysis.

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  • 8. 
    When you are estimating a value from two values, you're... - ProProfs

    When you are estimating a value from two values, you're...

    • Extrapolating

    • Interpolating

    • Extracting

    • Collecting

    Correct Answer
    A. Interpolating
    Explanation
    When you are estimating a value from two known values, you are interpolating. Interpolation involves using the known data points to estimate or predict an intermediate value within the range of those points. It is a method of estimating values within a range based on the relationship between the known values.

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  • 9. 
    Which of the following is used to understand linear transformation? - ProProfs

    Which of the following is used to understand linear transformation?

    • Eigenvector

    • Linear regression

    • Rectilinear

    • Eigenvalue

    Correct Answer
    A. Eigenvector
    Explanation
    Eigenvectors are used to understand linear transformations. When a linear transformation is applied to an eigenvector, the resulting vector is parallel to the original eigenvector. Eigenvectors provide insight into the direction of the transformation and how it stretches or contracts space. By analyzing the eigenvalues associated with each eigenvector, we can determine the amount of stretching or contracting that occurs in each direction. Therefore, eigenvectors are a fundamental concept in understanding linear transformations.

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  • 10. 
    At what points do gradient descend methods converge? - ProProfs

    At what points do gradient descend methods converge?

    • Local minima

    • Optima minima

    • They don't converge

    • At point one

    Correct Answer
    A. They don't converge
    Explanation
    Gradient descent methods do not always converge to a specific point. Instead, they continuously update the parameters in the direction of steepest descent until a stopping criterion is met. This means that the algorithm may stop at a local minimum or even a saddle point, rather than converging to the global minimum. Therefore, the correct answer is "They don't converge."

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  • Current Version
  • Mar 22, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Mar 08, 2018
    Quiz Created by
    Cripstwick
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