Data Analytics Lifecycle Stages
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Questions and Answers

What is one of the essential steps before data transformation and cleansing?

  • Implementing machine learning models
  • Ignoring data outliers
  • Familiarizing yourself with the data (correct)
  • Deploying database systems

    • What is the first stage in the Data Analytics Lifecycle?

  • Data Analysis
  • Data Visualization
  • Data Collection (correct)
  • Data Processing

    • Which of the following is commonly used for data transformation and cleansing?

  • Alpine Miner (correct)
  • Slack
  • Adobe Photoshop
  • Microsoft Excel

    • Which phase of the Data Analytics Lifecycle is primarily focused on cleaning and transforming data?

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

    Which technique is NOT associated with data transformation and cleansing?

    <p>Social media analytics</p> Signup and view all the answers

    What activity is part of data conditioning?

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

    What is a critical activity that occurs during the Data Analysis phase?

    <p>Creating data models</p> Signup and view all the answers

    In the context of the Data Analytics Lifecycle, what does Phase 2: Data Preparation aim to achieve?

    <p>Ensure data quality and relevance</p> Signup and view all the answers

    Which tool is primarily utilized for handling big data in transformation and cleansing processes?

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

    During which stage of the Data Analytics Lifecycle is data typically transformed into visual formats?

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

    What is the primary purpose of Phase 5 in the model building process?

    <p>To interpret and compare results</p> Signup and view all the answers

    Which tool is NOT typically used for model building?

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

    In Phase 5, which key question should be addressed regarding the model's performance?

    <p>Did we succeed or fail?</p> Signup and view all the answers

    Which of the following actions is included in Phase 5?

    <p>Interpreting the results</p> Signup and view all the answers

    What is one method used to handle missed values in data?

    <p>Using a default value</p> Signup and view all the answers

    Which of the following best describes the main objective of Phase 3: Data Planning?

    <p>To determine techniques, workflow, and methods</p> Signup and view all the answers

    What should be compared to initial hypotheses during Phase 5?

    <p>Model performance results</p> Signup and view all the answers

    In the context of data integrity, why is consistency important?

    <p>It ensures the accuracy and reliability of data</p> Signup and view all the answers

    What might be an appropriate technique for filling in missed values aside from using averages?

    <p>Using a statistical model or assumption</p> Signup and view all the answers

    Which option is NOT a method for handling missed values in data?

    <p>Storing data on physical media</p> Signup and view all the answers

    What is a recommended strategy to validate approaches effectively?

    <p>Use smaller test sets to validate approaches.</p> Signup and view all the answers

    Which of the following is suggested to optimize the environment for model building?

    <p>Employ fast hardware and parallel processing.</p> Signup and view all the answers

    Why is it beneficial to use smaller test sets during the validation process?

    <p>They allow for easier identification of flaws in the model.</p> Signup and view all the answers

    What is the purpose of employing fast hardware in model building?

    <p>To speed up processing times and streamline workflows.</p> Signup and view all the answers

    How does parallel processing benefit model building and workflows?

    <p>It enables simultaneous processing to improve overall speed.</p> Signup and view all the answers

    What is the primary focus when implementing a model in a production environment?

    <p>Defining the process for updating and retraining the model</p> Signup and view all the answers

    What should happen when it is necessary to retire a model?

    <p>A process to update and retrain the model must also be defined</p> Signup and view all the answers

    Which aspect is NOT a part of maintaining a model in a production environment?

    <p>Randomly changing model parameters without evaluation</p> Signup and view all the answers

    Why is it important to define a process for retraining the model?

    <p>To ensure consistency and reliability in the model's outcomes</p> Signup and view all the answers

    Which factor should be considered when deciding to update a model?

    <p>Feedback from end-users regarding model predictions</p> Signup and view all the answers

    Study Notes

    Data Analytics Lifecycle Stages

    • The lifecycle involves several key stages: discovery, data prep, model planning, model building, operationalize, and communicate results which are all key to creating and running a useful data analytic project.

    Data Preparation (Phase 2)

    • The primary goal of this phase is to construct a powerful and robust analytics environment for the team.
    • A dedicated analytics sandbox with at least 10 times the capacity of the existing Enterprise Data Warehouse (EDW) is necessary.
    • Extract-Load-Transform (ELT) processes are essential for identifying and executing data transformations.
    • Big ELT and ETL processes (Extract-Load-Transform and Extract-Transform-Load) aid this data manipulation.
    • Data transformations and cleansing are performed using tools like SQL, Hadoop, MapReduce, and Alpine Miner.
    • Data should be thoroughly examined to ensure its quality. Data conditioning, visualization, and surveys help in this analysis.
    • Visualization tools include R packages (base, ggplot2, lattice), Gnuplot, and tools like Ggobi/Rggobi, Spotfire, and Tableau.

    Data Cleaning (Phase 3)

    • Data cleaning (or cleansing) is the process of preparing raw data for analysis effectively.
    • Identifying and managing incomplete data, removing noise, and fixing duplicates are crucial parts of this phase..
    • Ensuring data integrity and consistency through validation helps ensure accurate results.
    • Missing values are addressed using default values, averages, or random methods as appropriate based on the data issue and its nature overall.

    Data Planning (Phase 3)

    • Determine the appropriate techniques, work processes, and methods to achieve desired outcomes using information gleaned from data structure, volume, and potential hypotheses.
    • Tools like R/Postgres SQL, SQL Analytics, Alpine Miner, SAS/ACCESS, and SPSS/OBDC assist this analytical effort.
    • Data exploration is a crucial component, which includes various steps like variable selection and model fitting.
    • Best performance is gained when converting to SQL or a database language when appropriate, choosing the right technique based on the goals set for the model.

    Model Building (Phase 4)

    • Data sets are prepared for various purposes such as training, testing, and production and should address all needs and expectations.
    • Smaller test sets are employed to validate approaches.
    • The optimal environment uses fast hardware for parallel processing.
    • R, PL/R, SQL, Alpine Miner, and SAS Enterprise Miner, are among the tools that are useful here.

    Communication of Results (Phase 5)

    • Assess project success and determine any areas needing improvement.
    • Comparing results to previously held hypotheses (initial hypotheses) is a vital step.
    • Identify significant points and measure the overall contribution of the project to the business.

    Operationalization (Phase 6)

    • Running a pilot project to gain initial experience is an important first step.
    • Assessment of project benefits is essential to gauging its overall value.
    • Model deployment in a production environment is essential for long-term functionality and utility.
    • The implementation of a defined process helps in keeping the model current. This process accounts for any retraining or retirement that may be needed at a later time.

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    Related Documents

    Session 4: Big Data - Computer Science - 3rd Year - PDF

    Description

    Explore the essential stages of the data analytics lifecycle, focusing particularly on data preparation. Understand the importance of creating a robust analytics environment and the tools and processes needed for effective data transformation and cleansing. This quiz covers crucial concepts for anyone involved in data analytics.

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