What is the difference between structured and unstructured data?

Structured data has a fixed tabular schema with defined rows and columns (e.g. CSV, SQL tables). Unstructured data has no predefined schema (e.g. images, audio, raw video).

Home ASSEB AI HubUnit 5: Data Literacy – Data Collection to Data Analysis

Class XIASSEB-527Unit 5: Data Literacy – Data Collection to Data Analysis

Unit 5: Data Literacy – Data Collection to Data Analysis

Master official ASSEB Class XI AI Unit 5: Types of Data, Data Collection, Data Wrangling, Cleaning Missing Values, and Exploratory Data Analysis (EDA).

Theory: 7 Marks

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Practical: 7 Marks

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11 min read

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Learning Outcomes

Understand the critical importance of clean data in machine learning workflows.
Identify data formats: structured (tabular CSV), semi-structured (JSON), and unstructured (images, audio).
Master data collection, data wrangling, missing value imputation, and outlier detection.
Perform Exploratory Data Analysis (EDA) and visualize distributions using Matplotlib.
Explore the Scikit-learn library in Python and execute standard preprocessing workflows.

Board Exam Scoring Tips

In 3-mark questions, list the 4 main steps of data preprocessing: 1) Data Cleaning, 2) Data Integration, 3) Data Transformation, 4) Data Reduction.
Know when to impute with Median vs Mean: Median is used when data has extreme outliers.

Key Takeaways

Garbage in, garbage out: no machine learning algorithm can overcome corrupted, biased, or poorly cleaned data.
Data wrangling transforms raw, unstructured data into clean structured schemas suitable for modelling.
Missing data can be dropped (`df.dropna()`) or imputed with central tendencies like mean, median, or mode (`df.fillna()`).
Exploratory Data Analysis (EDA) uses visual plots to discover patterns, anomalies, and feature correlations before modelling.

Practice Unit MCQ Quiz

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