Data Processing — Chapter Summary
ICSE · Class 11 · Artificial Intelligence
Summary of Data Processing for ICSE Class 11 Artificial Intelligence. Part of the ICSE Class 11 Artificial Intelligence syllabus.
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Overview
Data processing is the set of methods used to clean, organize, transform, and standardize data so that it becomes suitable for analysis. Clean data supports better results, while dirty data can create wrong conclusions. The main ideas in this chapter are data cleaning, handling missing values, dupli
Key Concepts
Data cleaning is the process
Data cleaning is the process of identifying and correcting or removing errors and inconsistencies within a dataset so that the data is in a format app
Dirty data is data that contains
Dirty data is data that contains many errors or has not gone through the cleaning process. It may include missing values, outliers, duplicates, errone
Missing values occur when some required
Missing values occur when some required data is not present. Large portions of missing crucial data can cause bias in results.
Outliers are values that are far
Outliers are values that are far outside the norm and not representative of the data. They may result from typos or exceptional circumstances.
Duplicates are repeated data entries
Duplicates are repeated data entries that can overrepresent one item in analysis. Duplicate rows can be identified and removed with Pandas.
Learning Objectives
- Understand data cleaning and why it is needed
- Identify types of dirty data such as missing values, outliers, duplicates, errors, and inconsistencies
- Use Pandas to check, remove, and fill missing values and duplicates
- Understand the idea of data quality, completeness, and consistency
- Learn data transformation, normalization, and standardization
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Sources & Official References
Content is aligned to the official syllabus. Refer to the board website for the latest curriculum.
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