Astronomy Research Data Analysis with Python

Name: Astronomy Research Data Analysis with Python
Rating: 4.3 (109 reviews)

Starting from Python, learn data analysis, data visualizations and image process (beginner to advanced) techniques.

Created bySpartificial Innovations

Last updated 12/2023

English

What you'll 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.

Course content

5 sections • 42 lectures • 7h 4m total length

Introduction to the Program3:26
Google Colab Introduction6:00
Explore google colab notebooks to code in python online, using code cells and text cells to document experiments for astronomy visualizations and data analysis.
Comments in Python6:12
Learn how Python comments describe code, using # for single-line comments, triple quotes for multi-line comments, and keyboard shortcuts to document logic and aid revision.
Variables and Constants7:26
Basic Data Types12:06
f-Strings9:58
User Inputs6:21
Take user inputs in Python with the input command, store them, and check their type. Inputs are strings by default, and you can convert to integer or float later.
Data Type Conversion12:45
Learn how to convert data types in Python, including strings to integers and floats, using int() and float(), handling conversion errors, and performing numeric operations with user input.
Control Flow20:36
Functions in Python26:01
Intro to Python Quiz

Introduction about the module 21:16
Introduction to the Tabular Data2:48
Importing the Libraries4:21
Peeking into the Tabular Data12:33
Creation of Directory to save Visuals6:12
Create a dedicated directory in python to save visualizations, using os.makedirs with exist_ok, define a base path in Google Colab, and build dynamic paths for saving plots.
First Visualization from Tabular Data4:23
Learn to create your first bar chart from a tabular star-type column using pandas and matplotlib, counting categories with value_counts and displaying the basic plot for saving later.
Customizing and Saving the Visualizations19:38
Customize a matplotlib bar chart by adjusting figure size, adding bar labels, applying a dark background, coloring bars, styling the title and axes, rotating x-ticks, and saving the plot.
Visualizing Star Color Data9:25
Visualizing the Outliers in the Data14:54
Line Plots to Visualize the trend in Data11:50
Creating a Pairplot12:09
Create HR Diagram33:34
Downloading the Visualizations5:51
Tabular Data Visualization Quiz

Introducing the Module1:06
Explore the concept of astronomical images and fits files, fetch Andromeda data via SkyView form, apply pixel scaling methods, and understand z scale intervals in Astropy.
What is an Image?13:51
Understanding FITS file4:32
The lecture explains fits files, their header units and metadata, and how they store astronomical image data from surveys like Andromeda Galaxy for image processing workflows.
Installing and Importing Libraries2:54
SkyView Form6:05
Explore the Sky View form to fetch Andromeda Galaxy images from DSS surveys and prepare the data for Python analysis with the Astro Query library.
Fetch and Visualize M31 Galaxy in Python the image data16:01
Fetch and visualize another Image of M317:38
Create your own FITS file11:50
Visualize Distribution of Pixels of M319:47
Apply MinMax Pixel Scaling on Pixels of M3111:21
Different Types of Pixel Scaling10:46
Compare standard scaling, log normalization, and square root normalization on M31 pixel data, visualize results, and find that log normalization best reveals core details of the Andromeda galaxy.
ZScaleInterval from Astropy21:57
Image Data Processing and Visualisation

Introducing the Module1:00
Explore convolution operations, apply denoising with a gaussian kernel, and enhance features using majoring and pseudo filters, then extract corner förstner and multi basic features with the src image library.
Understanding Convolution operation7:05
Gaussian Kernel for Denoising12:44
Explore gaussian denoising by building gaussian kernels and applying 2d convolution with SciPy, demonstrating noise reduction while preserving details on M31 grayscale images.
Enhancing the Features in the Image9:51
Corner Foerstner9:13
Multiscale Basic (Local) Features10:44
Advanced Image Processing

Requirements

No Programming Knowledge or Experience Required
No Astronomy Experience or Knowledge Required

Description

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:

Visit spartificial[dot]com official website for discounted price.

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.

Astronomy Research Data Analysis with Python

What you'll learn

Explore related topics

Course content

Starting with Python10 lectures • 1hr 51min

Tabular Data Visualization using Pandas, Matplotlib and Seaborn13 lectures • 2hr 19min

Image Data Visualization12 lectures • 1hr 58min

Image Processing | Apply Filters and Extracting Features6 lectures • 51min

Course Conclusion1 lecture • 7min

Requirements

Description

Who this course is for: