CBSE Class 10th AI (Artificial Intelleigence) | 6. Data Exploration by Abraham | Learn Smarter
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6. Data Exploration

Data Exploration is a crucial process in AI and Data Science that helps uncover insights from raw, often unstructured data. It involves identifying patterns, handling missing values, visualizing data, and understanding relationships between variables while ensuring ethical standards are maintained. Key techniques include statistical summaries and various visualization tools to aid comprehension.

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Sections

  • 6

    Data Exploration

    Data Exploration is a critical phase in data analysis that focuses on understanding, cleaning, and visualizing raw data.

    Learning Practice

  • 6.1

    What Is Data Exploration?

    Data Exploration is the initial process of investigating data to identify patterns, anomalies, and relationships, facilitating further analysis.

    Learning Practice

  • 6.2

    Types Of Data

    This section introduces the three main types of data—structured, unstructured, and semi-structured—essential for data exploration in AI and Data Science.

    Learning Practice

  • 6.2.1

    Structured Data

    Structured data refers to organized information formatted in rows and columns, typically found in spreadsheets and databases. This organization enables easier analysis and visualization of the data.

    Learning Practice

  • 6.2.2

    Unstructured Data

    Unstructured data lacks a predefined format and is complex, making it challenging to analyze.

    Learning Practice

  • 6.2.3

    Semi-Structured Data

    Semi-structured data is a blend of structured and unstructured data formats, such as JSON or XML.

    Learning Practice

  • 6.3

    Basic Data Exploration Techniques

    This section discusses fundamental techniques for exploring datasets, including understanding dataset structure and calculating summary statistics.

    Learning Practice

  • 6.3.1

    Understanding Dataset Structure

    This section focuses on understanding the structure of datasets as a critical part of data exploration, emphasizing the importance of rows, columns, data types, and unique values.

    Learning Practice

  • 6.3.2

    Summary Statistics

    Summary statistics provide essential insights into the data distribution, including measures like mean, median, mode, and standard deviation.

    Learning Practice

  • 6.4

    Handling Missing And Incorrect Data

    This section discusses how to manage missing values and outliers in datasets to ensure accurate data analysis.

    Learning Practice

  • 6.4.1

    Missing Values

    This section discusses the issue of missing data in datasets, its common causes, and various techniques for handling it.

    Learning Practice

  • 6.4.2

    Outliers

    Outliers are data points that significantly differ from other observations, often requiring specific handling.

    Learning Practice

  • 6.5

    Data Visualization For Exploration

    This section introduces data visualization as a tool to represent data graphically, enabling the identification of patterns, trends, and outliers.

    Learning Practice

  • 6.5.1

    What Is Data Visualization?

    Data visualization is the graphical representation of information and data, enabling easy recognition of patterns and trends.

    Learning Practice

  • 6.5.2

    Common Visualization Tools

    This section introduces common visualization tools used for data exploration.

    Learning Practice

  • 6.6

    Relationships Between Data

    This section discusses the relationships between data, focusing on correlation and the important distinction between correlation and causation.

    Learning Practice

  • 6.6.1

    Correlation

    Correlation measures how two variables are related to each other, showing positive, negative, or no correlation.

    Learning Practice

  • 6.6.2

    Causation Vs Correlation

    Causation versus correlation highlights the distinction between stating that one event causes another and noting that two events are related.

    Learning Practice

  • 6.7

    Tools And Technologies Used In Data Exploration

    This section discusses various tools and technologies that are essential for data exploration.

    Learning Practice

  • 6.8

    Ethics In Data Exploration

    This section highlights the importance of ethical considerations during data exploration, focusing on privacy, avoiding bias, and legal compliance.

    Learning Practice

References

Chapter_6_Data.pdf

Class Notes

Memorization

What we have learnt

  • Data Exploration is essenti...
  • Key techniques include hand...
  • Correlation does not imply ...

Final Test

Revision Tests