Data Science Basic | Data Visualization by Diljeet Singh | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge
Data Visualization

Data visualization is crucial for transforming data into meaningful insights, utilizing various Python libraries such as Matplotlib, Seaborn, and Plotly. The chapter covers different types of visualizations, their appropriate contexts, and best practices for clarity and effectiveness. By applying these techniques, learners can effectively communicate complex information and trends to diverse audiences.

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.

Sections

  • 1

    Why Data Visualization Matters

    Data visualization is essential for simplifying complex data and making insights accessible to a broader audience.

    Learning Practice

  • 2

    Visualization With Matplotlib

    This section introduces the creation of basic visualizations using Matplotlib, focusing on line charts and bar charts as fundamental ways to represent data.

    Learning Practice

  • 2.1

    Line Chart

    The Line Chart section introduces the concept of creating basic line plots using the Matplotlib library in Python, focusing on visualizing trends over time.

    Learning Practice

  • 2.2

    Bar Chart

    In this section, you'll learn how to create and utilize bar charts using Matplotlib for comparing categorical data.

    Learning Practice

  • 3

    Visualization With Seaborn

    Seaborn enhances data visualization capabilities in Python, enabling users to create more aesthetically pleasing graphics and advanced statistical plots easily.

    Learning Practice

  • 3.1

    Histogram

    Histograms are used to visualize the distribution of numerical data by dividing it into bins and showing the frequency of data points in each bin.

    Learning Practice

  • 3.2

    Box Plot

    Box plots are visual representations that display the distribution of data based on a five-number summary, highlighting medians and outliers.

    Learning Practice

  • 3.3

    Count Plot (For Categorical Data)

    The count plot is a fundamental tool in Seaborn for visualizing the frequency of categorical data.

    Learning Practice

  • 3.4

    Heatmap (Correlation Matrix)

    The heatmap (correlation matrix) visualizes the correlation between multiple variables in a dataset, allowing for quick identification of relationships.

    Learning Practice

  • 4

    Interactive Charts With Plotly

    This section highlights how to create interactive charts using Plotly, emphasizing its features like zoom, hover information, and export options.

    Learning Practice

  • 4.1

    Line Chart

    The section provides an overview of creating line charts using Python's Matplotlib library, focusing on visualizing trends over time.

    Learning Practice

  • 4.2

    Scatter Plot

    This section covers scatter plots, a tool for analyzing the relationship between two numeric variables.

    Learning Practice

  • 5

    Best Practices For Data Visualization

    This section presents important principles for creating effective data visualizations.

    Learning Practice

  • 6

    Chapter Summary

    This chapter summary encapsulates the importance of data visualization and the key techniques utilized, primarily focusing on tools like Matplotlib, Seaborn, and Plotly.

    Learning Practice

References

Chapter 7_ Data Visualization.pdf

Class Notes

Memorization

What we have learnt

  • Data visualization simplifi...
  • Matplotlib is suitable for ...
  • Plotly enables interactive ...

Revision Tests