Graph plotting in Python for scientific Journals & papers

Graph plotting in Python for scientific Journals & papers, available at Free, has an average rating of 4.45, with 21 lectures, 18 quizzes, based on 54 reviews, and has 3056 subscribers.

You will learn about You will learn how to use Python to create stunning charts and data visualizations Create complex data visualizations using Matplotlib Create custom Matplotlib settings for journals, and conference plots Student, researchers, data scientist and teachers who wants to elevate their figures to the next level Explore dimensionality of the data, data interpretation Import multiple datasets and plot This course is ideal for individuals who are Students (undergrad and graduate) keen in data visualization or Researchers, data scientists or Anyone who wants to learn data visualization or Explore dimensionality of the data or PhD's and Postdoc's It is particularly useful for Students (undergrad and graduate) keen in data visualization or Researchers, data scientists or Anyone who wants to learn data visualization or Explore dimensionality of the data or PhD's and Postdoc's.

Enroll now: Graph plotting in Python for scientific Journals & papers

Summary

Title: Graph plotting in Python for scientific Journals & papers

Price: Free

Average Rating: 4.45

Number of Lectures: 21

Number of Quizzes: 18

Number of Published Lectures: 21

Number of Published Quizzes: 18

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

You will learn how to use Python to create stunning charts and data visualizations
Create complex data visualizations using Matplotlib
Create custom Matplotlib settings for journals, and conference plots
Student, researchers, data scientist and teachers who wants to elevate their figures to the next level
Explore dimensionality of the data, data interpretation
Import multiple datasets and plot

Who Should Attend

Students (undergrad and graduate) keen in data visualization
Researchers, data scientists
Anyone who wants to learn data visualization
Explore dimensionality of the data
PhD's and Postdoc's

Target Audiences

Students (undergrad and graduate) keen in data visualization
Researchers, data scientists
Anyone who wants to learn data visualization
Explore dimensionality of the data
PhD's and Postdoc's

Welcome to the finest data visualization or graph plotting course using Matplotlib on the web, in my viewpoint. The technical skills you learn in this course will help you advance in your career as a data scientist, researcher, or science student. This course is designed for students of science & engineering interested in producing top-notch scientific graphics as well as researchers and data scientists. First, I’ll give you a brief overview of Python. Along with that, I’ll cover the essential packages, such as Numpy, Pandas, and Matplotlib, that we’ll use often in this course. Before getting into more complex preparation for posters and scientific publications, I’ll start with the fundamentals. At the completion of this course, You will be able to plot any form of data from different varieties of data files.

In this course, you will learn:

Working with JupyterLab
Create complex data visualizations using Matplotlib
Import and extract data from CSV, TXT, MAT, and H5 files
Import multiple datasets and plot
Create custom Matplotlib settings for journals, and conference plots
2D colormap plots and customization
3D plots and customization

What distinguish this course from the hundreds of others available online?

While most online courses follow simply descriptive material and take endless hours, this short course highlights the necessity of visually appealing plots as a need for any kind of scientific or professional presentation, as well as the integration of visualizations from various datasets. Instead of spending endless hours on hypothetical data, this combines the ideas, tactics, and crucial settings.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing Python

Chapter 2: Plotting using Matplotlib

Lecture 1: Matplotlib Introduction | Basic line plots

Lecture 2: Customization of line plot part I

Lecture 3: Customization of line plot part II

Lecture 4: Export settlings vector (PDF, SVG) and raster graphics (PNG, JPG)

Chapter 3: Advanced Plotting

Lecture 1: Subplots: Introduction

Lecture 2: Semilog, loglog plots

Lecture 3: Double y-axis plots

Lecture 4: Inserting Image to a data plot

Chapter 4: Importing experimental data and plotting

Lecture 1: Importing (*.txt) data file and plotting

Lecture 2: Importing CSV files and plotting

Lecture 3: Importing Matlab's MAT file and plotting

Lecture 4: Importing (*.H5) files and plotting

Lecture 5: Importing multiple data files from a folder

Lecture 6: Using Pandas to import data files and plot

Chapter 5: 2D Colormap plots

Lecture 1: Introduction to 2D Colormap plots

Lecture 2: Customization of 2D colormap plots, eg colorbar, colormap

Chapter 6: Vector fields , contour, and 3D plots

Lecture 1: Visualizing Vector Fields

Lecture 2: Contour and Contourf plots

Lecture 3: 3D plots

Instructors

Dr. Manabendra Kuiri
Teacher | Scientist | Researcher in Physics

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 17 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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