Questions and Answers
What is the purpose of Min-Max Normalization?
Which feature creation methodology involves creating new attributes that capture important information?
What is the purpose of Z-Score Standardization?
Which data transformation technique involves transforming numerical data into categorical data?
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What is the purpose of Binary Encoding?
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What is the purpose of Data Reduction?
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What is the purpose of data pre-processing in statistics?
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Which of the following is a type of probability sampling?
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What is the purpose of data cleaning in statistics?
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Which of the following is a type of data transformation technique?
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What is the purpose of data transformation in statistics?
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Which of the following is a type of non-probability sampling?
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What is the purpose of data integration in statistics?
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Which of the following is a type of data quality issue?
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What is the purpose of data reduction in statistics?
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Which of the following is a data transformation technique used to handle outliers?
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What is the primary goal of data normalization?
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Which data transformation technique is used to convert categorical data into numerical data?
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What is the purpose of data reduction?
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Which of the following is a feature extraction technique?
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What is the purpose of data aggregation?
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What is the purpose of data transformation?
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What is the purpose of data discretization?
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What is the purpose of imputation?
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What is the purpose of data filtering?
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What is the purpose of data standardization?
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Study Notes
Data Integration Process
- Data integration is the process of combining data from various sources
- It involves data sour identification, data extraction, data mapping, data validation, and data quality assurance
- Techniques used in data integration include Extract, Transform, Load (ETL) and Extract, Load, Transform
Data Transformation Process
- Data transformation is the process of transforming data into another format suitable for analysis
- It involves data transformation, data loading, and data synchronization
- Techniques used in data transformation include data discovery, data mapping, code generation, and execution
Importance of Data Integration
- Improved decision-making
- Compliance with regulations
- Enhanced employee insights
- Streamlined processes
Data Integration Techniques
- Normalization – adjusting data value
- Standardization – scaling data
- Encoding – converting from categorical to numerical
- Aggregation – combining multiple data points
- Filtering – removing redundant or irrelevant data
- Imputation – filling in missing values
Types of Data Integration
- Inner Join – matching values
- Left Join – left values and matching values from the right
- Right Join – right values and matching values from the left
- Outer Join – the union of all values
Data Transformation Techniques
- Normalization – scaling specific variable falls to normal
- Inferential statistics – drawing conclusions
- Sampling – law of large numbers and central limit theorem
- Data profiling – understanding the characteristics and quality of data
- Clear documentation – ensuring repeatability and quality
- Automation – streamlining and standardizing processes
Data Pre-processing
- Data pre-processing is improving the quality of data for secondary analysis
- Data cleaning – addressing noise in data to ensure accuracy and correctness
- Importance of data pre-processing:
- Ensures data accuracy and reliability
- Improves data quality
- Reduces errors and bias in analysis
- Supports effective decision-making
Data Quality Issues
- Missing values – incomplete
- Noise and outliers – data that deviate
- Inconsistencies – error, formatting
- Duplicate data – repeated values
Data Cleaning Process
- Handling missing values – imputing data
- Smoothing noisy data – eliminating outliers
- Detecting and deleting outliers – use box plot
- Fixing structural errors – all error in words
- Removing duplicates – avoiding redundancy
- Data validation – authenticating data
Data Transformation Techniques
- Data smoothing – helps predicting trends or seasonality
- Min-Max normalization – transforming into a standardized format
- Z-Score standardization – transforming into a standard normal distribution
- Binning – transforming numerical to categorical
- Feature creation – creating new attributes that capture important information
- Feature extraction – creating new features
- Mapping data or new space – lower-dimensional space to higher-dimensional space
- Feature construction – built intermediate features
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Description
This quiz covers the data integration process, including data source identification, data extraction, data mapping, and data aggregation. It also touches on data generalization, attribution construction, and data discretization.
