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What you'll get

  • Govt. Verified Certification
  • Job Credibility
  • Certification Valid for Life
  • Lifetime Access To E-learning
  • On-demand video
  • Flexible 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

Course Content

Total: 75 lectures
  • Introduction
  • Installing matplotlib, NumPy, and SciPy
  • Installing virtualenv and virtualenvwrapper
  • Installing matplotlib on Mac OS X
  • Installing matplotlib on Windows
  • Installing Python Imaging Library (PIL) for image processing
  • Installing a requests module
  • Customizing matplotlib's parameters in code
  • Customizing matplotlib's parameters per project
  • Introduction
  • Importing data from CSV
  • Importing data from Microsoft Excel files
  • Importing data from fixed-width datafiles
  • Importing data from tab-delimited files
  • Importing data from a JSON resource
  • Exporting data to JSON, CSV, and Excel
  • Importing data from a database
  • Cleaning up data from outliers
  • Reading files in chunks
  • Reading streaming data sources
  • Importing image data into NumPy arrays
  • Generating controlled random datasets
  • Smoothing the noise in real-world data
  • Introduction
  • Defining plot types – bar, line, and stacked charts
  • Drawing a simple sine and cosine plot
  • Defining axis lengths and limits
  • Defining plot line styles, properties, and format strings
  • Setting ticks, labels, and grids
  • Adding a legend and annotations
  • Moving spines to the center
  • Making histograms
  • Making bar charts with error bars
  • Making pie charts count
  • Plotting with filled areas
  • Drawing scatter plots with colored markers
  • Introduction
  • Setting the transparency and size of axis labels
  • Adding a shadow to the chart line
  • Adding a data table to the figure
  • Using subplots
  • Customizing grids
  • Creating contour plots
  • Filling an under-plot area
  • Drawing polar plots
  • Visualizing the filesystem tree using a polar bar
  • Introduction
  • Creating 3D bars
  • Creating 3D histograms
  • Animating in matplotlib
  • Animating with OpenGL
  • Introduction
  • Processing images with PIL
  • Plotting with images
  • Displaying an image with other plots in the figur
  • Plotting data on a map using Basemap
  • Plotting data on a map using Google Map API
  • Generating CAPTCHA images
  • Introduction
  • Understanding logarithmic plots
  • Understanding spectrograms
  • Creating a stem plot
  • Drawing streamlines of vector flow
  • Using colormaps
  • Using scatter plots and histograms
  • Plotting the cross-correlation between two variables
  • Importance of autocorrelation
  • Introduction
  • Drawing barbs
  • Making a box and a whisker plot
  • Making Gantt charts
  • Making errorbars
  • Making use of text and font properties
  • Rendering text with LaTeX
  • Understanding the difference between pyplot and OO API


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.


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