Python Data Analysis and Visualization Course – Astronomical Data

Overview

Embark on a comprehensive 6.5-hour course exploring data analysis, visualization, and image processing techniques using real-world astronomical data. Master Python fundamentals, delve into tabular datasets, create various visualizations using matplotlib and seaborn, and learn advanced image processing methods. Gain hands-on experience with FITS files, pixel scaling, convolution operations, and feature enhancement filters. Perfect for beginners, this course simplifies complex concepts through practical, step-by-step tutorials, culminating in the ability to analyze and visualize astronomical data effectively.

Syllabus

⌨️ Introduction to Course
⌨️ Introduction to Google Colab
⌨️ Python Comments
⌨️ Variables and Constants
⌨️ Basic Data Types
⌨️ f-string
⌨️ User inputs
⌨️ Data Type Conversions
⌨️ Control Flow
⌨️ Functions
⌨️ Intro to Tabular Dataset
⌨️ Importing the libraries for tabular dataset
⌨️ Exploring the tabular dataset
⌨️ Creating First Visualization with barplot matplotlib
⌨️ Adding customization to barplot
⌨️ Creating bargraph using Seaborn
⌨️ Visusalizing outliers using boxplot
⌨️ Introducing subplots using matplotlib
⌨️ Line plots
⌨️ Pair plot and scatter plot
⌨️ Creating HR Diagram
⌨️ Downloading visualizations
⌨️ Understanding Images, pixels, grayscale and colors
⌨️ What are FITS file?
⌨️ Installing Astroquery
⌨️ SkyView Form - Virtual Telescope
⌨️ Visualize Andromeda galaxy
⌨️ Creating FITS file and testing
⌨️ Pixel Distribution
⌨️ MinMax Pixel Scaling
⌨️ Standard Scaling, Log Normalization, Square root normalization
⌨️ ZScaleInterval
⌨️ Convolution Operation - Why? Why? and Demonstration
⌨️ Gaussian Denoising
⌨️ Enhancing features with Meijering and Sato filter
⌨️ Corner Foerstner - What and How to use it in Python
⌨️ Multiscale Basic Features using skimage
⌨️ Conclusion