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Questions and Answers
What is the first phase of the CRISP-DM methodology?
Which activity is part of the Data Preparation phase?
What is the ultimate purpose of the Deployment phase in CRISP-DM?
Which of the following best describes data collection?
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What is often required after selecting modeling techniques in the CRISP-DM methodology?
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During which phase is it essential to familiarize oneself with data quality problems?
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Why is the Evaluation phase critical in the CRISP-DM methodology?
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What does the 'Modeling' phase primarily involve?
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What is the primary reason for conducting data collection in businesses?
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Which of the following is an example of primary data collection?
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What distinguishes qualitative data collection methods from quantitative methods?
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Which type of data collection is usually quicker and easier to obtain?
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What is a key factor to consider before beginning data collection?
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Which of the following is NOT a method of primary data collection?
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What is a significant advantage of secondary data collection methods?
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When conducting quantitative data collection, what is primarily expressed?
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Study Notes
Data Life Cycle
- CRISP-DM Methodology stands for Cross Industry Standard Process for Data Mining
- Business Understanding: Defines the project objectives and requirements from a business perspective, converting them into a data mining problem definition.
- Data Understanding: Involves initial data collection, getting familiar with the data, identifying quality issues, and discovering initial insights.
- Data Preparation: Constructs the final dataset (data fed into modeling tools) from the initial raw data, involving tasks like selection, transformation, cleaning, and preparation.
- Modeling: Selects and applies various modeling techniques, calibrating parameters to optimal values.
- Evaluation: Thoroughly evaluates the model and reviews the construction steps to ensure it meets business objectives.
- Deployment: Creates the model, organizing and presenting knowledge gained in a useful way for the customer.
Data Collection
- Data Collection is a systematic process of gathering observations or measurements.
- Data Collection Methods are used in businesses and sales organizations to analyze problems, draw conclusions, and make decisions.
- Data Collection can be qualitative or quantitative.
- Qualitative Data Collection Methods analyze the quality or reasons behind something, without mathematical calculations.
- Quantitative Data Collection Methods express data in figures or numbers, using both traditional and online methods.
Primary Data Collection
- Primary data is original data collected directly from the source.
- Examples include surveys, opinion polls, experiments, and observations.
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Quantitative Data Collection Methods involve numerical data and include methods like:
- Surveys: collecting data through questionnaires.
- Experiments: measuring the effects of variables.
- Observations: systematically recording behaviors or phenomena.
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Qualitative Data Collection Methods involve non-numerical data and include methods like:
- Interviews: gathering in-depth information from individuals.
- Focus Groups: facilitating discussions with a small group of people.
- Ethnographic Studies: observing and interacting with people in their natural settings.
Secondary Data Collection
- Secondary data is already collected by someone else and has been statistically analyzed.
- This type of data is readily available and doesn't require special collection methods.
- Examples include data from sensors, magazines, and documents.
What Before Data Collection?
- Define the aim of the problem: Clearly identify the goals and objectives of the data collection.
- Determine the type of data: Decide what kind of information is needed to address the problem (qualitative, quantitative, or both).
- Establish methods and procedures: Plan how the data will be collected, stored, and processed.
Step 1: Define the Aim of the Problem
- Identify exactly what you want to achieve with the data collection.
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Description
Explore the stages of the data life cycle through the lens of the CRISP-DM methodology. This quiz covers key phases such as business understanding, data preparation, modeling, evaluation, and deployment. Test your knowledge on essential concepts used in data mining and analytics.