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Questions and Answers
Which of the following types of data is represented by subjects such as marital status and hair color?
Which of the following types of data is represented by subjects such as marital status and hair color?
What aspect is least important in ordinal data compared to the order of variables?
What aspect is least important in ordinal data compared to the order of variables?
Which data preprocessing step involves removing inaccuracies from the dataset?
Which data preprocessing step involves removing inaccuracies from the dataset?
In the context of numerical data, which of the following is an example of ratio data?
In the context of numerical data, which of the following is an example of ratio data?
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Which aspect of real-world data is highlighted as a major challenge before it can be used for analytics?
Which aspect of real-world data is highlighted as a major challenge before it can be used for analytics?
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What is the primary characteristic of structured data?
What is the primary characteristic of structured data?
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Which of the following best describes data integrity?
Which of the following best describes data integrity?
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What does data granularity refer to?
What does data granularity refer to?
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Which of the following factors does NOT contribute to the quality of analytics-ready data?
Which of the following factors does NOT contribute to the quality of analytics-ready data?
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In the context of data metrics, what does data currency refer to?
In the context of data metrics, what does data currency refer to?
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Study Notes
Business Intelligence, Analytics, and Data Science: A Managerial Perspective - Chapter 2
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Descriptive Analytics I: This chapter covers the nature of data, statistical modeling, and visualization.
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Data: A collection of facts; usually obtained from experiences, observations, or experiments. Data can include numbers, words, images, etc.
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Data Quality and Integrity: Critical to analytics. Data integrity includes accuracy, completeness, consistency, and validity of an organization's data.
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Data Types:
- Structured Data: Standardized format, well-defined structure, complies with a data model, and follows a persistent order, easily accessed. Examples: names, dates, addresses, credit card numbers, stock info, geolocation.
- Unstructured Data: Any combination of textual, imagery, voice, and web content.
- Semi-structured Data: Extensible markup language (XML), hypertext markup language (HTML), JavaScript Object Notation (JSON), log files, etc.
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Metrics for Analytics Ready Data:
- Data source reliability
- Data content accuracy
- Data accessibility
- Data security and data privacy
- Data richness
- Data consistency
- Data currency/data timeliness
- Data validity and relevance
- Data granularity
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Categorical Variables:
- Nominal Data: Used to label variables without quantitative value. Examples: gender, hair color, nationality, marital status.
- Ordinal Data: Variables ordered based on their relative position. The differences between the values aren't necessarily consistent or meaningful (e.g., Likert scales: very likely, likely, neutral, unlikely, very unlikely).
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Numerical Variables:
- Interval Data: Variables with order and difference. Examples: Classification of people (teenager, youth, middle-age, etc.)
- Ratio Data: Order and difference between variables, with a true zero point. Examples: income, height, weight, sales, etc.
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Data Preprocessing: The process of preparing data for analytics. This includes data consolidation, data cleaning, data transformation, and data reduction.
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Data Reduction Techniques:
- Variables: Dimensional reduction, variable selection
- Cases/samples: Sampling, balancing/stratification, discretization
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Dimensionality Reduction: A process to reduce the number of dimensions (features) in a dataset while preserving the most important properties. This is useful for large, high-resolution images.
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Discretization: Converting continuous data into discrete intervals or categories. Typically applied to large datasets to simplify analysis or model building (e.g., age groups, income brackets).
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Data Normalization Reorganizing data to remove unstructured or redundant information, enabling a standardized data format. This creates an organized data system.
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Data Visualization Using visual representations for exploring, understanding and communicating data. Often incorporates charts, graphs, illustrations.
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Statistical Modeling for Business Analytics: Uses statistics to analyze data. Includes:
- Descriptive Statistics: Describing data (e.g., mean, median, mode, range, variance).
- Inferential Statistics: Drawing conclusions about a population based on a sample (e.g., hypothesis testing, regression, various graphs/charts).
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Regression Modeling: A statistical technique to characterize the relationship between explanatory and response variables. Used for hypothesis testing and forecasting.
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Business Reporting:
- Report: Information to drive decisions, acting as a communication artifact.
- Report Composition: Includes sources, format (text, table, graphs), distribution channels (in-print, email, portal).
- Dashboard Design Considerations: The design should present data in a clear and concise manner so information is easily accessible, enabling rapid assimilation.
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Performance Dashboards: Used in Business Process Management (BPM) and Business Intelligence (BI) platforms. Provide visual displays of information, combined on a single screen for quick analysis. These dashboards facilitate drilling into data and exploration.
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Description
Explore the fundamentals of descriptive analytics in this quiz based on Chapter 2 of 'Business Intelligence, Analytics, and Data Science: A Managerial Perspective'. Understand the types of data, importance of data quality, and statistical modeling essentials for effective data visualization.
