Complete Python & Matplotlib Data Visualization

Complete Python & Matplotlib Data Visualization, available at $19.99, has an average rating of 4, with 86 lectures, based on 48 reviews, and has 615 subscribers.

You will learn about Prepare excellent Visualizations Publications and Printing quality Visualizations 3D Visualizations Image Processing with NumPy and Matplotlib Add Matplotlib and NumPy in resume Appear for interviews confidently This course is ideal for individuals who are Data Scientists, Data Analysts, and Data Engineers or Mathematicians, Statisticians, Scientists, and Engineers or Image Processing and Computer Vision Professionals or Machine Learning and AI Professionals or Bioinformatics and Biostatistics professionals or Programmers and Developers or Students, Job seekers, anyone wanting to learn new stuff It is particularly useful for Data Scientists, Data Analysts, and Data Engineers or Mathematicians, Statisticians, Scientists, and Engineers or Image Processing and Computer Vision Professionals or Machine Learning and AI Professionals or Bioinformatics and Biostatistics professionals or Programmers and Developers or Students, Job seekers, anyone wanting to learn new stuff.

Enroll now: Complete Python & Matplotlib Data Visualization

Summary

Title: Complete Python & Matplotlib Data Visualization

Price: $19.99

Average Rating: 4

Number of Lectures: 86

Number of Published Lectures: 86

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Prepare excellent Visualizations
Publications and Printing quality Visualizations
3D Visualizations
Image Processing with NumPy and Matplotlib
Add Matplotlib and NumPy in resume
Appear for interviews confidently

Who Should Attend

Data Scientists, Data Analysts, and Data Engineers
Mathematicians, Statisticians, Scientists, and Engineers
Image Processing and Computer Vision Professionals
Machine Learning and AI Professionals
Bioinformatics and Biostatistics professionals
Programmers and Developers
Students, Job seekers, anyone wanting to learn new stuff

Target Audiences

Data Scientists, Data Analysts, and Data Engineers
Mathematicians, Statisticians, Scientists, and Engineers
Image Processing and Computer Vision Professionals
Machine Learning and AI Professionals
Bioinformatics and Biostatistics professionals
Programmers and Developers
Students, Job seekers, anyone wanting to learn new stuff

Become a Master in Data Visualization with Python 3 and acquire employers’ one of the most requested skills of 21st Century! A great data visualization engineer earns more than $150000 per year!

This is the most comprehensive, yet straight-forward course for the Data Visualization with Python 3 on Udemy! Whether you have never worked with Data Visualization before, already know basics of Python, or want to learn the advanced features of matplotlib and NumPy with Python 3, this course is for you! In this course we will teach you Data Visualization with Python 3, Jupyter, NumPy, and Matplotlib.

(Note, we also provide you PDFs and Jupyter Notebooks in case you need them)

With over 85 lectures and more than 10 hours of video this comprehensive course leaves no stone unturned in teaching you Data Visualization with Python 3!

This course will teach you Data Visualization in a very practical manner, with every lecture comes a full programming video and a corresponding Jupyter notebook that has Python 3 code! Learn in whatever manner is the best for you!

We will start by helping you get Python3, NumPy, matplotlib, and Jupyter installed on your Windows computer and Raspberry Pi.

We cover a wide variety of topics, including:

Basics of Scientific Python Ecosystem
Basics of Digital Image Processing
Basics of NumPy and Matplotlib
Installation of Python 3 on Windows
Setting up Raspberry Pi
Tour of Python 3 environment on Raspberry Pi
Jupyter installation and basics
NumPy Ndarrays
Array Creation Routines
Basic Visualization with Matplotlib
Ndarray Manipulation
Random Array Generation
Bitwise Operations
Statistical Functions
Plotting with Matplotlib
Other types of visualizations (bar, histograms, scatter, and bubble)
Contours
3D Visualizations (plot, mesh, and surfaces)
Advanced Concepts in Matplotlib
Basics Image Processing with NumPy and Matplotlib
and much more…..

You will get lifetime access to over 75 lectures plus corresponding PDFs, Image Datasets, and the Jupyter notebooks for the lectures!

So what are you waiting for? Learn Data Visualization with Python 3 in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Objectives, Expected Audience, and Prerequisites of the course

Lecture 2: Course Contents and Topics Overview

Lecture 3: Please do leave your feedback

Lecture 4: Scientific Python 3 Ecosystem

Lecture 5: What is Matplotlib?

Lecture 6: PDF of URLs of projects in the Scientific Python 3 Ecosystem

Chapter 2: Python 3 On Windows

Lecture 1: Install Python 3 on Windows

Lecture 2: Verify Python 3 Environment On Windows

Chapter 3: Raspberry Pi and Python 3

Lecture 1: What is Raspberry Pi?

Lecture 2: Raspberry Pi OS Setup

Lecture 3: Remote Desktop with VNC

Lecture 4: Install IDLE3 on Raspberry Pi Raspbian

Lecture 5: Python 3 on Raspberry Pi

Lecture 6: Additional Software for Remote Connection

Lecture 7: Turn your Raspberry Pi 4 into a portable tablet with Raspad 3

Chapter 4: Python 3 Basics

Lecture 1: Hello World! on Windows PC

Lecture 2: Hello World! on Raspberry Pi

Lecture 3: Interpreter Vs Script Mode of Python 3

Lecture 4: A brief Tour of IDLE

Lecture 5: Raspberry Pi Vs PC: What platform to choose

Chapter 5: Python Package Index and pip

Lecture 1: Python Package Index and pip

Lecture 2: pip on Windows

Lecture 3: pip on Raspberry Pi

Chapter 6: Installation of NumPy and Matplotlib

Lecture 1: Install NumPy and Matplotlib on Windows

Lecture 2: Install NumPy and Matplotlib on Raspberry Pi

Chapter 7: Jupyter Notebook for Scientific Computing

Lecture 1: Jupyter and IPython

Lecture 2: Install Jupyter on Windows PC

Lecture 3: Install Jupyter on Raspberry Pi

Lecture 4: Install PuTTY on Windows PC

Lecture 5: Remote Connection to Remote Notebook Server

Lecture 6: A brief tour of Jupyter

Lecture 7: List of commands used in this section

Chapter 8: Getting Started with NumPy

Lecture 1: What is NumPy

Lecture 2: Ndarrays, Indexing, and Slicing

Lecture 3: Ndarray Properties

Lecture 4: NumPy Constants

Lecture 5: NumPy Datatypes

Chapter 9: Creation of Arrays and Matplotlib Visualizations

Lecture 1: Ones and Zeros

Lecture 2: Matrices

Lecture 3: Numerical Ranges and Visualizations

Chapter 10: Random Sampling

Lecture 1: Random Sampling

Chapter 11: Array Manipulation

Lecture 1: Array Manipulation

Chapter 12: Bitwise Operations

Lecture 1: Bitwise Operations

Chapter 13: Statistical Functions

Lecture 1: Statistical Functions

Chapter 14: Plotting in detail

Lecture 1: Single Line Plots

Lecture 2: Multi-line Plots

Lecture 3: Grid Axes and Labels

Lecture 4: Colors, Styles, and Marker

Chapter 15: More Types of Visualizations

Lecture 1: Histograms and XKCD

Lecture 2: Bar Chart and Visualizing Errors

Lecture 3: Pie and Scatter

Lecture 4: Stream Plots and Visualizing Vectors

Lecture 5: Fill Between and Stacked Column

Lecture 6: Contours

Lecture 7: Step Plots

Lecture 8: Horizontal Bar Plots

Lecture 9: Hexbin Visualizations

Lecture 10: Polar Visualizations

Chapter 16: Object Oriented Programming Style and Subplots

Lecture 1: Object Oriented Programming Style and Subplots

Chapter 17: NumPy, Matplotlib, and Image Processing

Lecture 1: What is Digital Image Processing?

Lecture 2: Image Datasets

Lecture 3: Installing Pillow on Windows and Raspberry Pi

Lecture 4: Reading, displaying, and saving images with Matplotlib

Lecture 5: NumPy for Images

Lecture 6: Image Statistics

Lecture 7: Image Masks

Lecture 8: Image Channels

Lecture 9: Arithmetic Operations

Lecture 10: Logical Operations

Lecture 11: Histogram with NumPy and Matplotlib

Chapter 18: 3D Visualizations

Lecture 1: 3D Plotting

Lecture 2: 3D Meshgrid

Lecture 3: 3D Surfaces

Chapter 19: Advanced Concepts in Matplotlib

Lecture 1: Stacked Area plots

Lecture 2: Colormaps

Lecture 3: Colorbar

Lecture 4: Interpolation Methods

Chapter 20: Shapes Shapes

Lecture 1: Shapes Shapes

Chapter 21: Matplotlib Animation

Lecture 1: Matplotlib Animation

Instructors

Ashwin Pajankar • 85,000+ Students Worldwide
Instructor | Programmer | Maker | Author | Youtuber

Rating Distribution

1 stars: 3 votes
2 stars: 3 votes
3 stars: 6 votes
4 stars: 8 votes
5 stars: 28 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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