CBSE Class 12th AI (Artificial Intelligence) | 9. Data Analysis using Python by Abraham | Learn Smarter
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9. Data Analysis using Python

Data analysis is fundamental to extracting meaningful insights from raw data, and this chapter introduces techniques using Python libraries such as Pandas, NumPy, and Matplotlib. Key skills include data loading, cleaning, manipulation, and visualization, all of which form the basis for more advanced applications in Machine Learning and Artificial Intelligence. The chapter also covers hands-on project work to reinforce the concepts learned.

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Sections

  • 9

    Data Analysis Using Python

    This section covers the importance of data analysis in AI, highlighting key Python libraries and methods for processing, cleaning, and visualizing data.

    Learning Practice

  • 9.1

    Introduction To Data Analysis

    This section introduces data analysis as a process of managing and interpreting data to extract useful insights.

    Learning Practice

  • 9.1.1

    Types Of Data Analysis

    This section introduces the four main types of data analysis: descriptive, diagnostic, predictive, and prescriptive.

    Learning Practice

  • 9.2

    Python Libraries For Data Analysis

    This section introduces key Python libraries—NumPy, Pandas, and Matplotlib—that are essential for data analysis.

    Learning Practice

  • 9.2.1

    Numpy (Numerical Python)

    NumPy is a core library in Python for scientific computing, offering powerful multidimensional array objects and numerical operations.

    Learning Practice

  • 9.2.2

    Pandas (Panel Data)

    Pandas is a powerful Python library used for data manipulation and analysis, focusing on its two main data structures: Series and DataFrame.

    Learning Practice

  • 9.2.3

    Matplotlib

    Matplotlib is a widely used library in Python for creating static, animated, and interactive visualizations.

    Learning Practice

  • 9.3

    Loading And Exploring Datasets

    This section covers how to load datasets into Python using Pandas and explore fundamental properties of the data.

    Learning Practice

  • 9.3.1

    Reading Data From Csv

    This section covers how to read data from CSV files using the Pandas library in Python, demonstrating the command and its functions.

    Learning Practice

  • 9.3.2

    Understanding Dataset Properties

    This section highlights the essential properties of datasets, outlining key functions in Pandas that help in exploring and understanding data.

    Learning Practice

  • 9.4

    Data Cleaning

    Data cleaning is essential for ensuring accurate analysis by addressing missing values, duplicates, and data type inconsistencies.

    Learning Practice

  • 9.4.1

    Handling Missing Values

    This section discusses strategies for managing missing values in datasets, emphasizing techniques such as identifying and filling null values.

    Learning Practice

  • 9.4.2

    Removing Duplicates

    This section discusses the process of removing duplicate entries from datasets using Pandas.

    Learning Practice

  • 9.4.3

    Changing Data Types

    This section explains the importance of changing data types in data analysis using Python, specifically using the Pandas library.

    Learning Practice

  • 9.5

    Data Manipulation

    Data manipulation involves selecting, filtering, and sorting data using Python libraries like Pandas.

    Learning Practice

  • 9.5.1

    Selecting Columns And Rows

    This section describes how to select specific columns and rows in a Pandas DataFrame, essential for effective data analysis.

    Learning Practice

  • 9.5.2

    Filtering Data

    This section focuses on how to filter data in a DataFrame using specific conditions.

    Learning Practice

  • 9.5.3

    Sorting Data

    This section discusses how to sort data using Python's Pandas library, focusing on various techniques for organizing data effectively.

    Learning Practice

  • 9.6

    Data Aggregation

    Data aggregation is a vital process in data analysis that involves summarizing and transforming data for easier insights.

    Learning Practice

  • 9.6.1

    Grouping Data

    This section covers the concept of grouping data using Pandas, explaining how to aggregate data based on specific categories.

    Learning Practice

  • 9.6.2

    Pivot Tables

    Pivot tables are powerful tools that allow users to summarize and analyze data effectively, particularly in the context of organizing multifaceted information in a manageable format.

    Learning Practice

  • 9.7

    Data Visualization With Matplotlib

    This section covers key data visualization techniques using the Matplotlib library in Python, including line charts, bar charts, histograms, and pie charts.

    Learning Practice

  • 9.7.1

    Line Chart

    This section explains how to create and interpret line charts using Matplotlib in Python.

    Learning Practice

  • 9.7.2

    Bar Chart

    This section covers how to create bar charts using Matplotlib in Python to visualize data effectively.

    Learning Practice

  • 9.7.3

    Histogram

    This section explains histograms, a vital tool in data visualization that shows the distribution of numerical data.

    Learning Practice

  • 9.7.4

    Pie Chart

    This section covers how to create a pie chart using the gender distribution data.

    Learning Practice

  • 9.8

    Mini Project: Analyzing Student Data

    This section guides students through a mini project to analyze student data using Python, emphasizing data loading, cleaning, aggregation, and visualization.

    Learning Practice

  • 9.9

    Summary

    This section provides a concise overview of the key concepts and tools covered in the chapter on data analysis using Python.

    Learning Practice

References

Chapter_9_Data.pdf

Class Notes

Memorization

What we have learnt

  • Python libraries like Panda...
  • You learned how to load, cl...
  • Practical knowledge of data...

Final Test

Revision Tests