Supervised Learning and Big Data Concepts
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Questions and Answers

What distinguishes unstructured data from structured data?

  • Unstructured data is always stored in structured databases.
  • Unstructured data follows a specific model.
  • Unstructured data is easier to analyze than structured data.
  • Unstructured data cannot be contained in a row-column database. (correct)

    • Which of the following statements is true about data lakes?

  • Data lakes are more costly to update than data warehouses.
  • Data lakes are a type of database focusing on specific data.
  • Data lakes mainly store structured data.
  • Data lakes are used to store raw and all data structures. (correct)

    • What is a common challenge associated with unstructured data?

  • It is less valuable than structured data.
  • It can easily be stored in data warehouses.
  • It is challenging to search, manage, and analyze. (correct)
  • It can only be found in structured formats.

    • In what way is a data warehouse different from a data lake?

    <p>A data warehouse primarily stores specific and structured data.</p> Signup and view all the answers

    Which of the following is an example of unstructured data?

    <p>A Facebook feed with status updates and pictures</p> Signup and view all the answers

    What is the first step in the data workflow within an organization?

    <p>Data Collection &amp; Storage</p> Signup and view all the answers

    What type of data is typically involved in the data collection step?

    <p>Images, videos, and text files</p> Signup and view all the answers

    During which step of the data workflow is data cleaned?

    <p>Data Preparation</p> Signup and view all the answers

    What is the primary purpose of building dashboards in the Exploration & Visualization phase?

    <p>To visualize data and track changes</p> Signup and view all the answers

    What is done with data in the Data Preparation step?

    <p>It is organized and cleaned</p> Signup and view all the answers

    What can be achieved by exploring and visualizing data?

    <p>Visualizing trends and relationships</p> Signup and view all the answers

    What is the result of the data being stored in raw format during the first step?

    <p>Data requires cleaning and preparation</p> Signup and view all the answers

    What does the term 'Big Data' refer to?

    <p>Data volumes exceeding 1 ZB</p> Signup and view all the answers

    What is the primary goal of supervised learning?

    <p>To learn a function that predicts output values from input variables</p> Signup and view all the answers

    In the training set D, what does the variable $y_i$ represent?

    <p>Output or target value</p> Signup and view all the answers

    What type of problem does regression address in supervised learning?

    <p>Estimating continuous valued outputs</p> Signup and view all the answers

    In the context of supervised learning, what is a training example?

    <p>A pair consisting of an input variable and its corresponding output</p> Signup and view all the answers

    How does the function $h$ relate to supervised learning?

    <p>It represents the relationship between input and output variables</p> Signup and view all the answers

    What does the variable $x_i$ represent in the training set?

    <p>The input feature variable</p> Signup and view all the answers

    Which of the following best describes a training set in supervised learning?

    <p>A collection of input-output value pairs</p> Signup and view all the answers

    When predicting house prices using supervised learning, what constitutes the input variable?

    <p>The living areas of the houses</p> Signup and view all the answers

    What is the primary role of a Data Engineer?

    <p>To ensure data is delivered correctly, efficiently, and in the right form</p> Signup and view all the answers

    Which of the following best describes Big Data?

    <p>Data with massive volumes that require advanced methods for handling</p> Signup and view all the answers

    What is a necessary first step in managing Big Data?

    <p>Ingest data from diverse sources</p> Signup and view all the answers

    What are Data Pipelines used for?

    <p>To enable the flow of data from sources to data warehouses</p> Signup and view all the answers

    Which of the following is NOT a task of a Data Engineer?

    <p>Create machine learning models for prediction</p> Signup and view all the answers

    What are the five V's commonly used to characterize Big Data?

    <p>Volume, Variety, Velocity, Veracity, Value</p> Signup and view all the answers

    Why is data corrupted?

    <p>It can happen during data transfer or due to software failures</p> Signup and view all the answers

    What is the outcome of building prediction models using Machine Learning?

    <p>Generating insights to help in decision-making</p> Signup and view all the answers

    What does a Q-Q plot represent when evaluating normality?

    <p>The variable quantiles on the y-axis and the expected quantiles of the normal distribution on the x-axis</p> Signup and view all the answers

    What issue arises from assuming non-collinearity in a regression model?

    <p>Difficulty in detecting the effect of each variable on the label</p> Signup and view all the answers

    Which method can be used to assess multicollinearity in a regression analysis?

    <p>Correlation Matrix</p> Signup and view all the answers

    Which factor can indicate multicollinearity and lead to issues in regression analysis?

    <p>High correlation between independent variables</p> Signup and view all the answers

    What is one method to resolve multicollinearity in a dataset?

    <p>Removing redundant variables</p> Signup and view all the answers

    What is the ideal outcome regarding the plot of normally distributed variables on a Q-Q plot?

    <p>Points should align closely along a 45-degree line</p> Signup and view all the answers

    What does the Variance Inflation Factor (VIF) assess in a regression model?

    <p>The extent of multicollinearity among independent variables</p> Signup and view all the answers

    Which graphical representation is useful for evaluating multicollinearity?

    <p>Heat maps of correlation matrix</p> Signup and view all the answers

    Study Notes

    Supervised Learning

    • Supervised learning uses labeled data to train a model to predict outputs for new, unlabeled data.
    • Examples include predicting house prices using data about living areas and prices.
    • The aim is to learn a function that maps input features to output values.

    Big Data

    • Big data is characterized by large volume, making traditional data handling methods inefficient.
    • One zettabyte is equal to 1021 bytes.

    Data Workflow

    • Data workflow consists of four general steps:
      • Data Collection & Storage: Collection of data from various sources, like traffic, surveys, or media traces. Data is stored in raw format in data lakes or databases.
      • Data Preparation: cleaning data, removing missing or duplicate values, and converting it into a more organized format.
      • Exploration & Visualization: Visualizing data using dashboards to track changes, compare different datasets, and analyze trends and relationships.
      • Experimentation & Prediction: Gaining insights from data to draw conclusions and make decisions. This step can involve building predictive models using machine learning techniques.

    Data Engineers

    • Data engineers manage the data workflow.
    • Their tasks include delivering correct data in the right form, to the right people, and efficiently.
    • They ingest data from different resources, optimize databases for analysis, remove corrupted data, and develop, test, and maintain data architectures.

    Big Data Management

    • Managing big data involves ingesting data from multiple sources, processing it, and storing it.
    • Data pipelines enable the efficient flow of data from sources to data warehouses.

    Data Types - Unstructured Data

    • Unstructured data doesn't follow a predefined format. Examples include Facebook feeds, presentations, and PDF documents.
    • It's challenging to search, manage, and analyze.
    • It's often stored in data lakes, but can appear in data warehouses or databases.
    • Unstructured data is extremely valuable and can be analyzed using AI and ML techniques.

    Data Lakes & Data Warehouses

    • Data lakes store all raw data, while data warehouses store specific data for specific use.
    • Data lakes store all data structures, while data warehouses mainly store structured data.
    • A data warehouse is a type of database.
    • Data lakes are cost-effective, while data warehouses are more costly to update.

    Linear Models - Normality

    • Normality is evaluated using Q-Q plots.
    • Q-Q plots compare quantiles of the variable to expected quantiles of the normal distribution.
    • For normally distributed variables, the relationship should fall on a 45-degree line.

    Linear Models - Collinearity

    • Multicollinearity occurs when independent variables are highly correlated with each other.
    • This can undermine the statistical significance of independent variables and make it challenging to assess their individual effects.
    • Multicollinearity can be assessed using correlation matrices and variance inflation factors (VIF).
    • It can be resolved by removing redundant variables, using Principal Component Analysis, or other methods.

    Evaluating Multicollinearity: Heat Maps of Correlation Matrix

    • Heat maps of correlation matrices can be used to visualize multicollinearity.

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    Description

    This quiz explores key concepts in supervised learning and big data workflows. You'll learn about the process of training models with labeled data, and the challenges associated with large volumes of data. Test your understanding of data collection, preparation, exploration, and prediction techniques.

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