Astronomy Research Data Analysis with Python

Astronomy Research Data Analysis with Python, available at $19.99, has an average rating of 4.68, with 42 lectures, 4 quizzes, based on 51 reviews, and has 1310 subscribers.

You will learn about Python Programming Basics upto Conditionals and Loops Data Visualization for Tabular Data Astronomy Image processing and visualization Hands-on learning approach with practical examples and real-world datasets. This course is ideal for individuals who are Astronomy students and hobbyists looking to apply Python in their studies or projects. or Researchers and professionals in astronomy or related fields seeking to enhance their data analysis skills. or Programmers interested in expanding their skills into the realm of astronomy and scientific data analysis. It is particularly useful for Astronomy students and hobbyists looking to apply Python in their studies or projects. or Researchers and professionals in astronomy or related fields seeking to enhance their data analysis skills. or Programmers interested in expanding their skills into the realm of astronomy and scientific data analysis.

Enroll now: Astronomy Research Data Analysis with Python

Summary

Title: Astronomy Research Data Analysis with Python

Price: $19.99

Average Rating: 4.68

Number of Lectures: 42

Number of Quizzes: 4

Number of Published Lectures: 42

Number of Published Quizzes: 4

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Python Programming Basics upto Conditionals and Loops
Data Visualization for Tabular Data
Astronomy Image processing and visualization
Hands-on learning approach with practical examples and real-world datasets.

Who Should Attend

Astronomy students and hobbyists looking to apply Python in their studies or projects.
Researchers and professionals in astronomy or related fields seeking to enhance their data analysis skills.
Programmers interested in expanding their skills into the realm of astronomy and scientific data analysis.

Target Audiences

Astronomy students and hobbyists looking to apply Python in their studies or projects.
Researchers and professionals in astronomy or related fields seeking to enhance their data analysis skills.
Programmers interested in expanding their skills into the realm of astronomy and scientific data analysis.

Course Description:

Embark on an enlightening journey through the cosmos with our comprehensive Udemy course, “Astronomy Research Data Analysis with Python.” This course is designed for astronomy enthusiasts, students, and researchers keen on mastering Python for analyzing astronomical data. With a focus on practical skills and real-world applications, this course simplifies complex concepts, making it accessible to learners with basic programming knowledge.

What You’ll Learn:

Module 1: Starting with Python Dive into Python programming, beginning with the basics. Understand Google Colab, variables, data types, and control flow. Learn about f-strings, user inputs, and functions. This foundation is crucial for handling astronomical data efficiently.
Module 2: Tabular Data Visualization Explore the world of tabular data with Pandas, Matplotlib, and Seaborn. Learn how to import libraries, analyze star color data, detect outliers, and create line plots and HR diagrams. You’ll gain the ability to visualize and understand complex astronomical datasets.
Module 3: Image Data Visualization Uncover the secrets of astronomical image data. Learn about FITS files, and use Python to visualize galaxies like M31. Understand image processing techniques like MinMax and ZScaleInterval scaling, enhancing your ability to interpret celestial images.
Module 4: Image Processing | Apply Filters and Extracting Features Delve deeper into image processing. Learn about convolution operations, Gaussian kernels, and feature enhancement. Discover techniques for identifying and extracting features from astronomical images, a skill vital for research and analysis.
Feedback, Conclusion, Further Steps Wrap up your learning experience with feedback sessions, a course conclusion, and guidance for future learning paths in astronomy and data analysis.

Who This Course is For:

Astronomy students and hobbyists looking to apply Python in their studies or projects.
Researchers and professionals in astronomy or related fields seeking to enhance their data analysis skills.
Programmers interested in expanding their skills into the realm of astronomy and scientific data analysis.

Course Features:

Hands-on learning approach with practical examples and real-world datasets.
Step-by-step guidance, ensuring a solid grasp of each concept.
Access to a community of like-minded learners and professionals.
Lifetime access to course materials, including updates.

Enroll Now:

Join us on this exciting journey to unravel the mysteries of the universe with Python. Enroll in “Astronomy Research Data Analysis with Python” today and take the first step towards mastering the art of astronomical data analysis!

DISCOUNT:

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Course Curriculum

Chapter 1: Starting with Python

Lecture 1: Introduction to the Program

Lecture 2: Google Colab Introduction

Lecture 3: Comments in Python

Lecture 4: Variables and Constants

Lecture 5: Basic Data Types

Lecture 6: f-Strings

Lecture 7: User Inputs

Lecture 8: Data Type Conversion

Lecture 9: Control Flow

Lecture 10: Functions in Python

Chapter 2: Tabular Data Visualization using Pandas, Matplotlib and Seaborn

Lecture 1: Introduction about the module 2

Lecture 2: Introduction to the Tabular Data

Lecture 3: Importing the Libraries

Lecture 4: Peeking into the Tabular Data

Lecture 5: Creation of Directory to save Visuals

Lecture 6: First Visualization from Tabular Data

Lecture 7: Customizing and Saving the Visualizations

Lecture 8: Visualizing Star Color Data

Lecture 9: Visualizing the Outliers in the Data

Lecture 10: Line Plots to Visualize the trend in Data

Lecture 11: Creating a Pairplot

Lecture 12: Create HR Diagram

Lecture 13: Downloading the Visualizations

Chapter 3: Image Data Visualization

Lecture 1: Introducing the Module

Lecture 2: What is an Image?

Lecture 3: Understanding FITS file

Lecture 4: Installing and Importing Libraries

Lecture 5: SkyView Form

Lecture 6: Fetch and Visualize M31 Galaxy in Python the image data

Lecture 7: Fetch and visualize another Image of M31

Lecture 8: Create your own FITS file

Lecture 9: Visualize Distribution of Pixels of M31

Lecture 10: Apply MinMax Pixel Scaling on Pixels of M31

Lecture 11: Different Types of Pixel Scaling

Lecture 12: ZScaleInterval from Astropy

Chapter 4: Image Processing | Apply Filters and Extracting Features

Lecture 1: Introducing the Module

Lecture 2: Understanding Convolution operation

Lecture 3: Gaussian Kernel for Denoising

Lecture 4: Enhancing the Features in the Image

Lecture 5: Corner Foerstner

Lecture 6: Multiscale Basic (Local) Features

Chapter 5: Course Conclusion

Lecture 1: Feedback, Conclusion, Further Steps

Instructors

Spartificial Innovations
AI and Space Science Institute

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 7 votes
4 stars: 12 votes
5 stars: 31 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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