This week will cover the basics of working with astronomical image data, including techniques for adjusting contrast, background estimation, and source extraction. By the end of the week, you will be able to:

• Understand how astronomical images are produced and what types of information they contain.
• Adjust the contrast of an image to reveal faint details.
• Estimate and remove background noise from an image.
• Identify and measure sources in an image, including deblending overlapping sources.

This week will cover the basics of working with Integral Field Unit (IFU) data, which provides 2D maps of the spectrum of light from astronomical objects. By the end of the week, you will be able to:

• Understand how IFU data is produced and what types of information it contains.
• Extract spectra from IFU data and analyze them.
• Create visualizations of IFU data, including maps of emission line fluxes.

This week will cover the basics of working with tabular data in astronomy, including the use of correlation functions and citizen science projects. By the end of the week, you will be able to:

• Understand how tabular data is used in astronomy and what types of information it contains.
• Calculate correlation functions to study the clustering of galaxies.
• Participate in citizen science projects to contribute to astronomical research.

This week will cover the basics of using neural networks for astronomical data analysis, including the use of deep learning libraries like PyTorch. By the end of the week, you will be able to:

• Understand the basics of neural networks and how they can be applied to astronomy data.
• Train and evaluate a neural network for a specific task, such as galaxy classification.
• Use PyTorch to implement and train a neural network.