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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Welcome to this course "Certified Data Visualization in Python '' where you will learn about Python which you already know about and if you don't, you can go through our previous course on Python by which you are going to perform a multi-perform data visualization tool for designing any level of interactive data visualization. So for knowing more about data visualization check throughout the course. 

Here you will learn about the overview of the course, you will understand Data, Dimensionality, and Why we plot to set up the environment. You will begin with the most basic plots, you will also explore images and contours and the working on plots with many uncertainties are also a part of this course. You can look at other useful types of plots, you can make multiple panel plots using color bars and legends. 

These topics are explained in a very brief manner. You will learn more things about this topic so carry on with this course. 

You will require some prior Python programming experience whereas experience with data Analysis and machine Learning analysis will be also helpful. But these requirements are not necessary as you will learn this course from the very beginning to the advanced level. 

This course is for Developers, students pursuing computer science, programming enthusiasts, coding enthusiasts, data scientists and data analysts. 

You will get a lifetime access to this course and many job opportunities further So what are you waiting for to come and enroll your name in this course.

Course Content

Total: 43 lectures
  • Getting Started with Matplotlib
  • Setting Up the Plotting Environment
  • Editing and Running Code
  • Loading Data for Plotting
  • Plotting Our First Graph
  • Basic Structure of a Matplotlib Figure
  • Setting Colors in Matplotlib
  • Adjusting Text Formats
  • Customizing Lines and Markers
  • Customizing Grids and Ticks
  • Customizing Axes
  • Using Style Sheets
  • Title and Legend
  • Adjusting Layout
  • Adding Subplots
  • Adjusting Margins
  • Drawing Inset Plots
  • Adding Text Annotations
  • Adding Graphical Annotations
  • Typical API Data Formats
  • Introducing Pandas
  • Visualizing the Trend of Data
  • Visualizing Univariate Distribution
  • Visualizing a Bivariate Distribution
  • Visualizing Categorical Data
  • Controlling SeabornFigure Aesthetics
  • More About Colors
  • Getting End-of-Day (EOD) Stock Data from Quandl
  • Two-Dimensional Faceted Plots
  • Other Two-Dimensional Multivariate Plots
  • Three-Dimensional (3D) Plots
  • Scraping Information from Websites
  • Non-Interactive Backends
  • Interactive Backends
  • Creating Animated Plots
  • Effective Visualization – Planning Your Figure
  • Effective Visualization – Crafting Your Figure
  • Visualizing Statistical Data More Intuitively
  • Methods for Dimension Reduction
  • Visualizing Population Health Information
  • Map-Based Visualization for Geographical Data
  • Combining Geographical and Population Health Data
  • Survival Data Analysis on Cancer


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