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Basics of Matplotlib for Data Analysis & Data Science:Python
Rating: 4.5 out of 5(64 ratings)
3,425 students

Basics of Matplotlib for Data Analysis & Data Science:Python

Learn basics of Matplotlib ,frequent used Matplotlib plots , styling in Matplotlib with the help of real-world use-cases
Last updated 12/2023
English

What you'll learn

  • Learn about Essentials of matplotlib to create plots like Bar Charts, Line Charts, Scatter Plots, Histogram ,distribution plots , and more!
  • How to customize matplotlib plots
  • Learn basic functionality when & where to use matplotlib
  • Learnbasic styling of matplotlib

Course content

5 sections14 lectures1h 30m total length
  • Introduction & course benefits !5:25
  • Quick Summary of Jupyter Notebook5:47
  • Datasets & resources0:01

Requirements

  • Have a Keen Desire to learn !

Description

In this course, you will learn how to create interactive Visuals in python using the Matplotlibly data visualizations library


This course will teach your everything you need to know to use Python to create interactive visuals  with Matplotlib. Have you ever wanted to take your Python skills to the next level in data visualization? With this course you will be able to create fully customization plots , interactive visuals with the open source libraries like Matplotlib



Data visualisation is very critical for generating and communicating easy to understand finding and insights. Either you are a Data Analyst who wants to create a dashboard/present your analysis or you are a Data Scientist who wants to create a UI for your machine learning models, Matplotlib can be a boon for both.


You will learn in this course many chart types..

  • Bar chart

  • Line cart

  • Pie chart

  • Scatter plot

  • Histogram

  • Box plot

  • Violin plot

  • Distribution (KDE) Plot


We'll start off by teaching you enough Python and Pandas that you feel comfortable working and generating data  Then we'll continue by teaching you about basic data visualization with Matplotlib, including scatter plots, line charts, bar charts , box plots, histograms, distribution plots and many more ! We'll also give you an intuition of when to use each plot type.


By taking this course you will be learning the bleeding edge of data visualization technology with Python and gain a valuable new skill to show your colleagues or potential employers.                                                                                                                                                                                                                                                         

Who this course is for:

  • Any Python programmers who want to present their analyses using interactive visualisation