Astronomy Data Science With Python Programming

Name: Astronomy Data Science With Python Programming
Rating: 3.7 (9 reviews)

Learn Astronomy with Python, Image Processing, and Machine Learning with practical projects and step-by-step guidance.

Created bySpartificial Innovations

Last updated 2/2025

English

English [Auto],

What you'll learn

Master Python programming, including data structures, loops, and libraries like NumPy and Matplotlib.
Learn digital image processing and apply convolution, edge detection, and filters on real-world datasets.
Understand and implement machine learning models like Linear and Logistic Regression using Python.
Build and train neural networks and convolutional neural networks (CNNs) from scratch using TensorFlow and Keras.

Course content

5 sections • 109 lectures • 19h 16m total length

Introduction8:24
Python Module1:01
Python Comments8:15
Data Type – Strings13:06
Variables and Constants6:38
Data Type – Numerical4:58
Data Types Conversion10:31
Data Type – Boolean and Python Operators16:27
String Methods10:29
Data Structure – List25:37
Data Structure – Tuple9:34
Data Structure – Set4:52
Data Structure – Dictionary11:44
Data Structure Conversions3:35
Conditional Statements10:09
For Loop16:09
While Loop10:05
Functions19:15
Object Oriented Programming26:55
Numpy Library – 125:34
Numpy Library – 222:31
Matplotlib Library18:55
Module 1 Conclusion0:38
Module 1 Quiz 1
Module 1 Quiz 2

Image Processing Module1:05
Digital Images17:40
Bits and Bits per Pixel8:59
Digital Image Processing and Computer Vision7:29
Images in Python – 125:30
Images in Python – 215:59
Convolutions13:46
Gaussian Kernel19:38
Unsharp Mask11:44
Canny Edge Detector11:02
Basic Multiscale Features17:21
FITS Files3:39
Galaxies Morphology Classification10:18
Header – NGC318413:09
Image Data – NGC31848:16
Wide Scale and Nucleus Scale – NGC318410:21
Implement Filters on Image Data – NGC318411:56
Concluding – NGC3184 Project9:23
Introducing M87 and M87*14:26
Exploring M87 using Python – 114:09
Exploring M87 using Python – 218:46
Exploring M87 using Python – 312:36
Module 2 Conclusion0:36
Module 2 Quiz 1

Introduction to Machine Learning0:58
Introduction to Artificial Intelligence and Machine Learning2:02
Applications of Artificial Intelligence6:21
Supervised vs Unsupervised vs Reinforcement4:32
Linear Regression: Intuition15:07
Linear Regression: Cost Function8:57
Linear Regression: Gradient Descent16:54
Get ready with the Code Along file!2:57
Generate the Dummy Training Dataset15:08
Customise the Plot and Get Ready with Model Parameters5:08
Build functions for prediction and cost10:43
Build function for Updating Parameters29:47
Build function for Training and Train the Model24:17
Check the Model Performance12:17
Generate the Testing Dataset and Evaluate the Model14:44
Introduction to Logistic Regression3:32
Dataset and Aim17:34
Explore the Dataset6:19
Prepare the Dataset and Pipeline16:11
Use Pipeline for Training and Testing17:30
Download the Pipeline and Test it8:08
Module 3 Conclusion0:51
Module 3 Quiz 1

Introduction to Deep Learning1:00
What is Deep Learning?6:48
Artificial Neuron and Biological Neuron13:09
Introduction to Multi-Layer Perceptron8:09
Most Commonly used Activation Functions9:32
Problem Statement to Build a Neural Network from scratch2:06
Understanding the Network to Build8:42
Equations - Cost Function, Forward and Backward Propagation7:46
Derivation of Backward Propagation Equations18:51
Code the Neural Network from Scratch24:05
Problem Statement and Adding Data to Notebook3:35
Read the csv file and explore the data10:33
Create Visualisations12:49
Split the Data into Training and Testing15:53
Preprocessing the Data14:05
Build the Network using Tensorflow and Keras and Compile it11:32
Train the Network and Visualise the Training8:23
Test the Model on the Unseen Data3:16
Concluding the Problem Statement and Saving the Model7:03
Module 4 Conclusion1:09
Module 4 Quiz 1

Convolutional Neural Networks (CNNs)0:59
Example of a CNN Architecture4:27
Calculate the Output Shape of Conv2D layer8:04
Calculate total trainable Parameters in Conv2D layer3:09
Example of a complete Convolution Calculation5:23
Summary of convolution operation3:12
Pooling Operation5:27
Fully Connected Layers5:24
Kaggle Setup6:16
Intro to Dataset and Problem Statement11:45
Get the Dataset in the notebook10:50
Importing libraries3:03
Read the csv file and perform the train test split7:48
Visualise random images in the data10:22
Create a function to preprocess one image14:05
Create a function to preprocess all the images in the data16:47
Build, Compile the CNN Model28:12
Train the CNN Model10:23
Make the predictions on the test dataset7:32
Module 5 Conclusion0:38
Course Conclusion1:16
Module 5 Quiz 1
Module 5 Quiz 2

Requirements

No Programming or Astronomy Experience Required

Description

This course is designed to take you from a beginner to a confident practitioner in Python programming, image processing, and machine learning. Through step-by-step lessons and hands-on projects, you will build a solid foundation in these essential skills and apply them to real-world problems.

What You’ll Learn:

Python Programming: Master Python basics, including data types, variables, loops, conditional statements, and libraries like NumPy and Matplotlib.
Image Processing: Learn how to process digital images using Python, including convolution operations, edge detection, and filters.
Machine Learning: Gain a strong understanding of core ML concepts, including Linear and Logistic Regression, with practical coding examples.
Deep Learning and CNNs: Build neural networks from scratch, train them using TensorFlow and Keras, and explore convolutional neural networks (CNNs).

Hands-on Projects:

You’ll work on engaging projects such as:

Analyzing real astronomical image datasets like NGC3184 and M87.
Building and training machine learning models for classification and regression tasks.
Implementing neural networks and CNNs to solve real-world problems using Kaggle datasets.

Who This Course Is For:

Beginners with no prior experience in Python or machine learning.
Students and professionals looking to strengthen their knowledge of AI and data science.
Anyone interested in exploring how programming and AI are applied to real-world scenarios, such as image processing and astronomy.

By the end of this course, you’ll have the skills to confidently build Python programs, process digital images, and implement machine learning models. Whether you’re a student, researcher, or tech enthusiast, this course will empower you to take the next step in your learning journey.

Let me know if you’d like to adjust this further!

Who this course is for:

Aspiring Data Scientists and ML Engineers
Students and Professionals
Tech Enthusiasts
Researchers and Hobbyists

Astronomy Data Science With Python Programming

What you'll learn

Explore related topics

Course content

Python Module23 lectures • 4hr 45min

Image Processing23 lectures • 4hr 38min

Introduction to Machine Learning22 lectures • 4hr

Introduction to Deep Learning20 lectures • 3hr 8min

Convolutional Neural Networks (CNNs)21 lectures • 2hr 45min

Requirements

Description

Who this course is for: