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Knowledge
Understanding data types and structures is crucial in data science. Various forms of data can be classified as structured, semi-structured, or unstructured, each with its own characteristics. Python provides a range of data types, such as integers, floats, strings, and booleans, and offers essential data structures like lists, dictionaries, and data frames for effective data management.
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Sections
Class Notes
Memorization
What we have learnt
- Data can be structured, sem...
- Python supports several dat...
- Lists, tuples, dictionaries...
Final Test
Revision Tests
What we have learnt
- Data can be structured, semi-structured, or unstructured.
- Python supports several data types including int, float, str, and bool.
- Lists, tuples, dictionaries, and sets are essential data structures in Python.
- Pandas DataFrames are crucial for handling structured data in real-world projects.
Key Concepts
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Term: Structured Data
Definition: Data organized in tabular format, readily stored in databases.
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Term: SemiStructured Data
Definition: Data that lacks a strict structure but possesses organizational properties.
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Term: Unstructured Data
Definition: Data with no predefined format, making it complex to analyze.
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Term: List
Definition: An ordered and mutable collection of items.
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Term: Tuple
Definition: An ordered and immutable collection of items.
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Term: Dictionary
Definition: An unordered collection that stores data in key-value pairs.
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Term: Set
Definition: An unordered collection of unique elements.
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Term: DataFrame
Definition: A two-dimensional table structure used in Pandas for handling data.