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About this course

Welcome to the course of Data Representation and Visualization using Matplotlib Library used in Python. This course helps you to understand the basics and fundamentals of data Representation in computers and visualization of those dataset using Python Libraries. It requires your basic understanding of Python language as prior. The aim of this course is to help you get the insights of analysis and visualization of Data which will help you to shift your career in Data analytics, and Data Science field. We constantly provide help and support throughout the learning of this course so that you will understand each and every aspect of this course. This course is divided into 10 different modules and each module has their own sections and subsections. 

What You’ll Learn: 

  • Understand the data representation in computers 
  • Able to understand Number system and its conversion 
  • Know the Python representation of data using Various Data structures 
  • Able to understand data visualization using Python libraries 
  • Able to understand the pyplot submodule in matplotlib 
  • Know about figures and plotting in matplotlib 
  • Able to understand different kind of plotting using matplotlib 
  • Help you to understand the life cycle of plotting 
  • Know the image plotting using matplotlib 
  • Customize the matplotlib using style sheets and rcParams 
  • Understand the application of Data Visualization in industries 

  • Python
  • Data Structure
  • Data Manipulation
  • Data Visualization Matplotlib
  • Data Analytics
avatar  Learner Career Outcomes
  • 52%

    started a new career after completing these courses
  • 46%

    started a new career after completing these courses
  • 14%

    got a pay increase or promotion
  •   Flexible deadlines
    Reset deadlines in accordance to your schedule.
  •   Shareable Certificate
    Earn a Certificate upon completion
  •   100% online
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  •   English

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Syllabus - What you will learn from this course



2 Hours to complete

Module 1: Data Representation in Computer

  • What is Data and how it is represented in Computers ?
    • Basics of Data
    • Various ways of Data Representation
      • Binary Data Storage
      • Other forms of Data Storage
  • Fundamentals of Number System for Data Representation
    • Number System for Data Storage and Conversion
      • How does the Number System work ?
      • Conversion of Numbers from one base to other



2 Hours to complete

Module 2: Representation of Data using Python

  • Using Python for Data Representation
    • How does Python make better data representation ?
      • Python Data Structures
      • Python Tools for Data Representation
  • Using Python Data Structures 
    • Number Data Structure
    • List and Tuple Data Structure
    • Set and Dictionary Data Structure
  • Applications of Python Data Structures 
    • Data manipulation using Python 
    • Data visualization using Python 



1 Hours to complete

Module 3: Visualization of Data using Python

  • What is Data Visualization ?
  • How data visualization help us understand better
    • Plotting of Data
    • Preparing Data Charts
    • Preparing Data graphs
    • Preparing other forms of Data visualization
  • Using Matplotlib for Data Visualization
    • What is Matplotlib ?
    • How is Matplotlib used for Data visualization ?
    • Where is the Matplotlib codebase ?
  • Learn Matplotlib for Data Visualization



2 Hours to complete

Module 4: Using Matplotlib for Data Visualization

  • Installation of Matplotlib
    • Install Matplot using pip
    • Check running version of Matplotlib 
    • Importing Matplotlib in your code
  • pyplot submodule in Matplotlib
    • What is pyplot submodule ?
    • Examples of pyplot
  • Plotting of Data using Matplotlib
    • Parts of Figure in plotting 
      • Figure
      • Axes
      • Axis
      • Artist
    • plot() function in Matplotlib
    • Examples of Plotting
    • Applications of Plotting in Data Visualization



2 Hours to complete

Module 5: Using pyplot in Matplotlib

  • Introduction to pyplot 
    • Formatting the style of your plot
  • Plotting with Keyword strings
  • Plotting with categorical variables 
  • Controlling line properties 
  • Working with multiple figures and axes
  • Working with text
    • Using mathematical expressions in text
    • Annotating text
  • Logarithmic and non-linear axes



2 Hours to complete

Module 6: Different Plotting using Matplotlib

  • Line Plot
    • plot() function in line plot
  • Multiple subplots in one figure
  • Images 
  • Contouring and pseudocolor
  • Histograms
  • Paths
  • Three Dimensional Plotting
  • Streamplot
  • Eclipses
  • Bar Charts
  • Pie Charts
  • Table 
  • Scatter Plots
  • GUI Widgets
  • Data Handling
  • Filled Curves
  • Log Plots
  • Polar Plots



1 Hours to complete

Module 7: Life Cycle of Plotting

  • Interfaces of Matplotlib
    • Object-Oriented API Vs Pyplot
  • Take Data for Plotting
    • Started figuring out Data
    • Controlling the style
    • Customizing the plot
    • Combining multiple visualizations
    • Saving our plot



1 Hours to complete

Module 8: Image Plotting using Matplotlib

  • Importing Image data into Numpy arrays
  • Plotting Numpy arrays as images
    • Apply pseudo color schemes to image plots
    • Color scale reference
    • Examining a specific data range
    • Array interpolation schemes



2 Hours to complete

Module 9: Customizing Matplotlib

  • Customizing Matplotlit with style sheets
    • Using style sheets
    • Defining our own style 
    • Composing styles
    • Temporary styling
  • Customizing Matplotlib with rcParams
    • Default configuration
    • Changing the defaults
      • rcParams
        • Dynamic rc settings 
        • The Matplotlib rc files
          • A sample Matplotlib rc file



1 Hours to complete

Module 10: Data visualization Applications

  • Where data visualization applies ?
  • How to create data visualization for Industries ?
    • For comparing data
    • Composition analysis
    • Analysis over a period
    • Distribution analysis of data
    • Visualization of geographical dataset
  • Data visualization for Data Analytics
  • Data visualization for Data Science

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