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    M 2 Data Analytics Lifecycle

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    bhuvangates
    This document describes the six phases of the data analytics lifecycle: 1) Discovery, 2) Data Preparation, 3) Model Planning, 4) Model Building, 5) Communicate Results, 6) Operationalize. It notes that data preparation is generally the most iterative and time-intensive phase, where initial hypotheses are formed in the Discovery phase and test/training data sets are created in the Model Building phase. The document poses multiple-choice questions about the different phases and activities within the lifecycle.

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    M 2 Data Analytics Lifecycle

    Uploaded by

    bhuvangates
    416 views22 pages
    This document describes the six phases of the data analytics lifecycle: 1) Discovery, 2) Data Preparation, 3) Model Planning, 4) Model Building, 5) Communicate Results, 6) Operationalize. It notes that data preparation is generally the most iterative and time-intensive phase, where initial hypotheses are formed in the Discovery phase and test/training data sets are created in the Model Building phase. The document poses multiple-choice questions about the different phases and activities within the lifecycle.

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    M 2 Data Analytics Lifecycle

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    This document describes the six phases of the data analytics lifecycle: 1) Discovery, 2) Data Preparation, 3) Model Planning, 4) Model Building, 5) Communicate Results, 6) Operationalize. It notes that data preparation is generally the most iterative and time-intensive phase, where initial hypotheses are formed in the Discovery phase and test/training data sets are created in the Model Building phase. The document poses multiple-choice questions about the different phases and activities within the lifecycle.

    Copyright:

    © All Rights Reserved
    Download as PPS, PDF, TXT or read online from Scribd
    Download as pps, pdf, or txt
    416 views22 pages

    M 2 Data Analytics Lifecycle

    Uploaded by

    bhuvangates
    This document describes the six phases of the data analytics lifecycle: 1) Discovery, 2) Data Preparation, 3) Model Planning, 4) Model Building, 5) Communicate Results, 6) Operationalize. It notes that data preparation is generally the most iterative and time-intensive phase, where initial hypotheses are formed in the Discovery phase and test/training data sets are created in the Model Building phase. The document poses multiple-choice questions about the different phases and activities within the lifecycle.

    Copyright:

    © All Rights Reserved
    Download as PPS, PDF, TXT or read online from Scribd
    Download as pps, pdf, or txt
    of 22
    At a glance
    Powered by AI
    The document discusses the different phases of the data analytics lifecycle including Discovery, Data Preparation, Model Planning, Model Building, Communicate Results, and Operationalize.

    The six phases of the data analytics lifecycle discussed are: Discovery, Data Preparation, Model Planning, Model Building, Communicate Results, and Operationalize.

    Data scientists typically spend most of their time in the Data Preparation phase, as it is generally the most iterative and time-intensive part of an analytics project.

    Data Analytics

    Lifecycle
    B.Bhuvaneswaran
    Assistant Professor (SS)
    Department of Computer Science & Engineering
    Rajalakshmi Engineering College
    Thandalam
    Chennai 602 105
    bhuvaneswaran@rajalakshmi.edu.in

    Phase
    Phase
    Phase
    Phase
    Phase
    Phase

    1:
    2:
    3:
    4:
    5:
    6:

    Discovery
    Data Preparation
    Model Planning
    Model Building
    Communicate Results
    Operationalize

    Phase 1: Discovery

    Learn the business domain, including relevant history,


    such as whether the organization or business unit has
    attempted similar projects in the past, from which you
    can learn.
    Assess the resources you will have to support the
    project, in terms of people, technology, time, and data.
    Frame the business problem as an analytic challenge
    that can be addressed in subsequent phases.
    Formulate initial hypotheses (IH) to test and begin
    learning the data.

    Question? (Ref. Module-2,


    Page-16)

    In which lifecycle stage are initial


    hypotheses formed?

    A. Discovery
    B. Model planning
    C. Model building
    D. Data preparation

    Of all of the phases, the step of Data


    Preparation is generally the most iterative
    and time intensive.

    Question? (Ref. Module-2,


    Page-20)

    In which phase of the data analytics


    lifecycle do Data Scientists spend the
    most time in a project?

    A. Discovery
    B. Data Preparation
    C. Model Building
    D. Communicate Results

    Phase 4: Model Building

    Develop data sets for testing, training, and


    production purposes.
    Get the best environment you can for
    executing models and workflows, including fast
    hardware and parallel processing.

    Question? (Ref. Module-2,


    Page-29)

    In which lifecycle stage are test and


    training data sets created?

    A. Model building
    B. Model planning
    C. Discovery
    D. Data preparation

    Question? (Ref. Module-2,


    Page-29)

    In which lifecycle stage are appropriate


    analytical techniques determined?

    A. Model planning
    B. Model building
    C. Data preparation
    D. Discovery

    Question? (Ref. Module-2,


    Page-31)

    In which phase of the analytic lifecycle


    would you expect to spend most of the
    project time?

    A. Discovery
    B. Data preparation
    C. Communicate Results
    D. Operationalize

    Question? (Ref. Module-2,


    Page-33)

    Which activity is performed in the


    Operationalize phase of the Data
    Analytics Lifecycle?

    A. Define the process to maintain the model


    B. Try different analytical techniques
    C. Try different variables
    D. Transform existing variables

    Question? (Ref. Module-2,


    Page-37)

    What is an appropriate data visualization


    to use in a presentation for an analyst
    audience?

    A. Pie chart
    B. Area chart
    C. Stacked bar chart
    D. ROC curve

    References

    Data Science and Big Data Analytics


    (DSBDA), EMC.

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