Learn data analysis, data visualizations, and image processing techniques using real-world astronomical data. The course offers a practical, hands-on approach to simplifying complex concepts in data analysis, making it great for beginners. Code: https://drive.google.com/drive/folders/1KufkfHYGUyQmqexkKjgGJZgWgeh69ENf?usp=sharing ✏️ Course developed by @Spartificial Spartificial Website: https://spartificial.com/ ⭐️ Contents ⭐️
⌨️ (0:00:34) Introduction to Course
⌨️ (0:03:54) Introduction to Google Colab
⌨️ (0:09:13) Python Comments
⌨️ (0:14:39) Variables and Constants
⌨️ (0:21:26) Basic Data Types
⌨️ (0:33:06) f-string
⌨️ (0:42:14) User inputs
⌨️ (0:47:43) Data Type Conversions
⌨️ (0:59:34) Control Flow
⌨️ (1:19:34) Functions
⌨️ (1:46:05) Intro to Tabular Dataset ⌨️ (1:48:14) Importing the libraries for tabular dataset
⌨️ (1:51:44) Exploring the tabular dataset
⌨️ (2:08:55) Creating First Visualization with barplot matplotlib
⌨️ (2:12:48) Adding customization to barplot ⌨️ (2:31:26) Creating bargraph using Seaborn
⌨️ (2:39:54) Visusalizing outliers using boxplot ⌨️ (2:46:13) Introducing subplots using matplotlib
⌨️ (2:53:57) Line plots ⌨️ (3:05:04) Pair plot and scatter plot ⌨️ (3:16:25) Creating HR Diagram ⌨️ (3:49:18) Downloading visualizations ⌨️ (3:54:44) Understanding Images, pixels, grayscale and colors
⌨️ (4:07:44) What are FITS file? ⌨️ (4:11:24) Installing Astroquery
⌨️ (4:13:44) SkyView Form – Virtual Telescope ⌨️ (4:19:12) Visualize Andromeda galaxy
⌨️ (4:41:14) Creating FITS file and testing ⌨️ (4:52:19) Pixel Distribution
⌨️ (5:01:39) MinMax Pixel Scaling
⌨️ (5:12:30) Standard Scaling, Log Normalization, Square root normalization
⌨️ (5:22:34) ZScaleInterval
⌨️ (5:43:39) Convolution Operation – Why? Why? and Demonstration
⌨️ (5:50:13) Gaussian Denoising ⌨️ (6:02:21) Enhancing features with Meijering and Sato filter ⌨️ (6:11:12) Corner Foerstner – What and How to use it in Python ⌨️ (6:19:21) Multiscale Basic Features using skimage
⌨️ (6:30:02) Conclusion 🎉 Thanks to our Champion and Sponsor supporters:
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