What is the difference between structured and unstructured data?

Structured data follows a rigid tabular schema of rows and columns (e.g. CSV, relational databases), whereas unstructured data lacks a predefined format (e.g. raw audio, satellite photos, free-form text).

Home ASSEB AI HubUnit 6: Data Collection and Exploration

Class XIASSEB-527Unit 6: Data Collection and Exploration

Unit 6: Data Collection and Exploration

Master official ASSEB Class XI AI Unit 6: Types of data and formats, Data collection, data wrangling, missing value handling, Exploratory Data Analysis (EDA), and Matplotlib.

Theory: 6 Marks

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Practical: 7 Marks

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11 min read

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Class XI Units Navigator(14 Official Units)

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U1Employability Skills - Unit 1: ICT SkillsFree U2Employability Skills - Unit 2: Entrepreneurial Skills4m U3Employability Skills - Unit 3: Green Skills2m U1Introduction to Artificial IntelligenceFree U2AI Paradigms5m U3Mathematics for AI5m U4Programming for AI5m U5Machine Learning Paradigms4m U6Data Collection and Exploration6m U7Supervised Algorithms5m U8Unsupervised Algorithms4m U9Storytelling with Data5m U10AI Ethics and Values4m U11Case Studies & Project4m

Learning Outcomes

Understand the importance of clean, representative data in AI systems.
Categorize data modalities and formats: Structured (CSV), Semi-Structured (JSON), and Unstructured (images, text).
Perform data wrangling: handling missing values (imputation vs deletion), duplicate detection, and schema standardization.
Conduct Exploratory Data Analysis (EDA) and visualize statistical distributions with Matplotlib.

Board Exam Scoring Tips

In 3-mark questions, outline the 4 steps of data preprocessing: 1) Cleaning, 2) Integration, 3) Transformation, 4) Reduction.
Explain when to impute missing values with Median instead of Mean (when extreme outliers skew the distribution).

Key Takeaways

Data quality determines model capability: 'Garbage in, garbage out' is the foundational rule of machine learning.
Primary data is collected firsthand for a dedicated project, while secondary data is repurposed from existing repositories.
Exploratory Data Analysis (EDA) reveals anomalies, outliers, and feature correlations before training starts.

Practice Unit MCQ Quiz

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