9626 IT - Data Mining 1

What is the first step in the data mining process?

• Understand the Data

• Prepare the Data

• Understand the Business

• Build the Model

Why is it important to understand the business before starting data mining?

• To gather data faster

• To define what success looks like and identify goals

• To clean the data effectively

• To choose the right software tools

In which step do analysts assess available data sources and their limitations?

• Prepare the Data

• Understand the Data

• Evaluate the Results

• Implement Change and Monitor

What activities are included in the "Prepare the Data" step?

• Analyzing trends and patterns

• Cleaning, standardizing, and checking for outliers in the data

• Presenting findings to stakeholders

• Defining business objectives

What happens during the "Build the Model" stage of data mining?

• Data is collected from various sources

• Relationships, trends, associations, or patterns are identified using clean data

• Results are evaluated and presented to management

• Business goals are redefined

What is the primary goal of evaluating results in the data mining process?

• To gather more data for analysis

• To assess findings and present them to decision-makers

• To implement changes based on preliminary findings

• To clean up any remaining data issues

In which step do management teams decide how to respond to analysis findings?

• Understand the Business

• Implement Change and Monitor

• Prepare the Data

• Build the Model

What does it mean to "monitor" after implementing changes based on data mining results?

• To continuously analyze new data for further insights

• To assess how effective changes were in achieving business goals

• To collect more data for future analysis

• To present findings again to stakeholders

Which of the following best describes a SWOT analysis in relation to understanding business needs?

• A method for cleaning data

• A framework for assessing strengths, weaknesses, opportunities, and threats

• A technique for building predictive models

• A way to evaluate results of data mining

Why might an oversized dataset be problematic during data preparation?

• It can lead to inaccurate results.

• It can slow down computations and analysis.

• It may not contain enough relevant information.

• It complicates model building.