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Business Analytics Overview IDM 2020
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Business Analytics Overview IDM 2020

Created by
@LeadingRhythm9663

Questions and Answers

What is the primary goal of business analytics?

  • To automate all business processes
  • To increase data storage capacity
  • To use data for informed business decisions (correct)
  • To create visually appealing reports

    • Which type of analytics aims to identify the root causes of outcomes?

  • Diagnostic Analytics (correct)
  • Predictive Analytics
  • Prescriptive Analytics
  • Descriptive Analytics

    • Which analytics type would be most appropriate for forecasting future sales trends?

  • Predictive Analytics (correct)
  • Diagnostic Analytics
  • Prescriptive Analytics
  • Descriptive Analytics

    • What major advantage does data-driven decision making provide to businesses?

    <p>Consistent competitive advantage</p> Signup and view all the answers

    What process does CRISP-DM stand for?

    <p>CRoss-Industry Standard Process for Data Mining</p> Signup and view all the answers

    Which type of analytics provides recommendations for specific actions based on data?

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

    What is one major challenge associated with business analytics?

    <p>Difficulty in interpreting large datasets</p> Signup and view all the answers

    Which of the following is NOT a type of business analytics?

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

    What is a primary objective during the deployment phase of the CRISP-DM process?

    <p>To determine if there are significant business issues to consider</p> Signup and view all the answers

    What is an essential step in planning for deployment in the CRISP-DM process?

    <p>Concluding a strategy for how to utilize data mining results</p> Signup and view all the answers

    Which of the following aspects is crucial to monitor after deploying data mining results?

    <p>The maintenance of data integrity and usage accuracy</p> Signup and view all the answers

    What does the final report in the deployment phase typically include?

    <p>A summary of the project and the lessons learned</p> Signup and view all the answers

    What challenge is NOT typically encountered during the deployment of data mining results?

    <p>Stakeholder engagement and communication</p> Signup and view all the answers

    What is the primary goal of the data cleaning process?

    <p>To raise the data quality to the level required by the selected analysis techniques</p> Signup and view all the answers

    Which phase typically consumes over 90% of the project time in the CRISP-DM process?

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

    What does data transformation involve in the context of data preparation?

    <p>Creating new variables and adjusting existing data attributes</p> Signup and view all the answers

    Which of the following is NOT a task typically involved in data selection?

    <p>Cleaning missing data within records</p> Signup and view all the answers

    What is the purpose of integrating data during the data preparation phase?

    <p>To combine information from multiple sources to create new records</p> Signup and view all the answers

    What type of transformations are primarily referred to as formatting transformations?

    <p>Syntactic modifications that do not change the data’s meaning</p> Signup and view all the answers

    What are derived attributes in the context of constructing data?

    <p>Attributes created based on existing data properties</p> Signup and view all the answers

    What is the first step in selecting the modeling technique based on the data mining objective?

    <p>Selecting the appropriate modeling technique</p> Signup and view all the answers

    What is the primary purpose of generating a test design before building a model?

    <p>To define a procedure for assessing model quality and validity</p> Signup and view all the answers

    In which phase of CRISP-DM is the actual selection of a modeling technique performed?

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

    What does the assess model step primarily focus on?

    <p>Judging the success of the modeling application</p> Signup and view all the answers

    Which method is typically used to evaluate model quality in classification tasks?

    <p>Error rates</p> Signup and view all the answers

    Artificial Neural Networks (ANN) are particularly useful for which type of problems?

    <p>Poorly structured problems with fuzzy data and uncertainty</p> Signup and view all the answers

    What is an essential part of the evaluation phase of CRISP-DM?

    <p>Reviewing and assessing the model's performance on test data</p> Signup and view all the answers

    In the context of building models, what is the role of domain knowledge during the assess model phase?

    <p>To interpret the models' results effectively</p> Signup and view all the answers

    What happens during the build model step in the Modeling phase?

    <p>Applying the selected modeling tool to the dataset</p> Signup and view all the answers

    What is the primary goal of the Business Understanding phase in the CRISP-DM methodology?

    <p>To understand client objectives and uncover project influencing factors</p> Signup and view all the answers

    Which of these is NOT a step in the Data Preparation phase of the CRISP-DM process?

    <p>Modeling technique evaluation</p> Signup and view all the answers

    What does the Data Understanding phase primarily involve?

    <p>Examining the quality and familiarity of data</p> Signup and view all the answers

    In the context of CRISP-DM, what distinguishes a business goal from a data mining goal?

    <p>Business goals focus on client outcomes, while data mining goals specify technical objectives</p> Signup and view all the answers

    Which of the following activities is part of the Evaluation phase in CRISP-DM?

    <p>Determining if results meet business objectives</p> Signup and view all the answers

    What is meant by the 'comfort factor' for new adopters in relation to the CRISP-DM framework?

    <p>It demonstrates the maturity of data mining practices.</p> Signup and view all the answers

    Which task is emphasized during the Data Understanding phase when examining data quality?

    <p>Identifying missing values and outliers</p> Signup and view all the answers

    What is the significance of the project plan produced during the Business Understanding phase?

    <p>It provides a roadmap for achieving data mining and business goals.</p> Signup and view all the answers

    In the CRISP-DM framework, which phase involves putting the results into practice?

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

    Which statement accurately describes the purpose of the CRISP-DM framework?

    <p>It facilitates project planning and enhances data mining maturity.</p> Signup and view all the answers

    Study Notes

    Business Analytics Overview

    • Business analytics is the practice of leveraging data to inform decisions and enhance performance.
    • Key benefits include data-driven decision making, competitive advantage, improved customer experience, cost reduction, and enhanced employee productivity.

    Types of Business Analytics

    • Descriptive Analytics: Analyzes past data to identify patterns and trends, summarizing historical performance.
    • Diagnostic Analytics: Investigates reasons behind past events, identifying root causes of outcomes.
    • Predictive Analytics: Utilizes historical data and statistical methods to forecast future events and trends.
    • Prescriptive Analytics: Recommends actions to optimize outcomes through advanced algorithms and models.

    CRISP-DM Framework

    • CRISP-DM (CRoss-Industry Standard Process for Data Mining) ensures a reliable, repeatable data mining process.
    • It consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.

    CRISP-DM Phases

    • Business Understanding: Define project objectives and data mining goals, assess the situation, and produce a project plan.
    • Data Understanding: Gather initial data, assess quality, discover insights, and identify quality issues.
    • Data Preparation: Takes up to 90% of the project time, involving data collection, cleansing, integration, and transformation.
    • Modeling: Selection of modeling techniques, building the model, and assessment of model performance for optimization.
    • Evaluation: Review model performance against business objectives, interpret its significance, and determine if additional business considerations are required.
    • Deployment: Implement results in practice, plan for ongoing monitoring and maintenance, and produce final reports on outcomes.

    Modeling Considerations

    • Various modeling techniques can be utilized, such as decision trees or neural networks, depending on business goals.
    • Model assessment includes validation against test data and collaboration with domain experts for practical interpretations.

    Machine Learning Insight

    • Machine Learning enables computers to learn from data autonomously, capable of handling complex tasks like pattern recognition and predictive analysis.
    • Artificial Neural Networks are a subset of machine learning used for handling poorly structured problems and creating predictive models in various fields.

    Challenges and Limitations

    • Key challenges include data quality and availability, privacy issues, security risks, model limitations, and ethical concerns surrounding bias and fairness.

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    Description

    This quiz provides an overview of Business Analytics, including its definition, types, and the CRISP-DM process. It also explores data analytics techniques and discusses the challenges faced in the field. Ideal for students and professionals looking to deepen their understanding of business analytics.

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