Prescriptive Analytics and Big Data Quiz
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Questions and Answers

What does 'Velocity' refer to in the context of big data?

  • The processes and algorithms to extract insights from data
  • The form of the data
  • The tools and frameworks required to process big data
  • The speed at which the data is generated (correct)
  • What is the main focus of analytics in the context of big data?

  • Contextualizing and processing raw data into useful information (correct)
  • Filtering and categorizing raw data
  • Storing and processing big data
  • Choosing the technologies and algorithms for analytics
  • What is a characteristic of big data related to its 'Volume'?

  • It comes in different forms such as structured, unstructured, or semi-structured
  • It refers to how fast the data is generated
  • It is so large that it would not fit on a single machine (correct)
  • It encompasses the processes, technologies, frameworks, and algorithms to extract meaningful insights from data
  • What does 'Variety' refer to in the context of big data?

    <p>The forms of the data, such as structured, unstructured, or semi-structured</p> Signup and view all the answers

    What is the primary focus of descriptive analytics?

    <p>Analyzing past data to present it in a summarized form</p> Signup and view all the answers

    Which type of analytics is used for diagnosing the reasons as to why certain events happened?

    <p>Diagnostic Analytics</p> Signup and view all the answers

    What is the main purpose of predictive analytics?

    <p>Predicting the occurrence of an event or forecasting the future values</p> Signup and view all the answers

    Which type of analytics involves analyzing past data to present it in a summarized form?

    <p>Descriptive Analytics</p> Signup and view all the answers

    What is the main focus of prescriptive analytics?

    <p>Predicting various outcomes</p> Signup and view all the answers

    Which type of data is involved in big data analytics?

    <p>Structured, semi-structured, and unstructured data</p> Signup and view all the answers

    What techniques are used in big data analytics?

    <p>Clustering and regression</p> Signup and view all the answers

    When are specialized tools and frameworks required for big data analysis?

    <p>When dealing with massive scale data</p> Signup and view all the answers

    What are the three main characteristics of big data mentioned in the text?

    <p>Volume, velocity, variety</p> Signup and view all the answers

    Which type of analytics involves analyzing historical data to search for patterns indicating fraud?

    <p>Descriptive analytics</p> Signup and view all the answers

    In which phase of the big data analytics life-cycle is the data stored in specialized storage solutions designed to scale?

    <p>Data storage and scaling</p> Signup and view all the answers

    What can wearable electronic devices allow in the healthcare example mentioned in the text?

    <p>Non-invasive and continuous monitoring of physiological parameters</p> Signup and view all the answers

    Study Notes

    Velocity in Big Data

    • Refers to the speed at which data is generated, processed, and analyzed.
    • Real-time data streaming is crucial for timely decision-making.

    Analytics Focus in Big Data

    • Main focus is on extracting meaningful insights to guide decisions.
    • Aims to derive value from data through various analytical techniques.

    Volume in Big Data

    • Characteristic denotes the immense quantity of data generated daily.
    • Data sources include social media, sensors, transactions, and more, leading to terabytes to petabytes of data.

    Variety in Big Data

    • Refers to the diverse types of data, including structured, semi-structured, and unstructured formats.
    • Examples include text, images, videos, and sensor data.

    Descriptive Analytics

    • Primary focus is on summarizing past data to understand trends and patterns.
    • Helps businesses understand what happened in historical data.

    Diagnostic Analytics

    • Utilized for identifying reasons behind specific past events or outcomes.
    • Involves analyzing data to uncover cause-and-effect relationships.

    Predictive Analytics

    • Aims to forecast future events based on historical data patterns.
    • Utilizes statistical models and machine learning techniques for predictions.

    Summarization in Analytics

    • Descriptive analytics presents past data in a summarized format.
    • Helps in grasping key trends without delving into raw data.

    Prescriptive Analytics

    • Focuses on recommending actions based on data insights.
    • Involves decision-making processes to optimize outcomes.

    Types of Data in Big Data Analytics

    • Involves various forms such as structured databases, unstructured documents, and streaming data from IoT devices.
    • Data types enhance insights through comprehensive analysis.

    Techniques in Big Data Analytics

    • Includes machine learning, data mining, statistical analysis, and natural language processing.
    • Techniques enable extraction of actionable insights from massive datasets.

    Specialized Tools for Big Data

    • Required when traditional data processing software is insufficient for handling large volumes and velocities of data.
    • Tools and frameworks facilitate scalability, storage, and processing capabilities.

    Characteristics of Big Data

    • Three main characteristics: Volume, Velocity, and Variety.
    • These characteristics define the complexity and scale of data processing needs.

    Fraud Detection Analytics

    • Involves analyzing historical data to spot patterns indicating fraudulent activity.
    • Uses advanced algorithms to identify anomalies and suspicious behavior.

    Data Storage in Big Data Analytics Life-Cycle

    • During the storage phase, data is housed in specialized solutions that can scale to handle growing volumes.
    • Ensures efficiency and effectiveness in data management.

    Wearable Electronic Devices in Healthcare

    • Allow for continuous monitoring of patients' health metrics.
    • Enable real-time data collection, aiding timely interventions and personalized care.

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    Test your knowledge about prescriptive analytics and big data, including prediction models, outcomes, and advanced analytic techniques used against large and diverse data sets.

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