syllabus.md

Prerequisite

Introductory knowledge in any programming language and a basic knowledge of astronomy is considered a pre-requisite.

Python (12 weeks)

1. Preliminary stuff

• How computers read code
• Algorithms and flowcharts
• Features of python
• Scripts, REPL and IDEs
• Jupyter notebook and Google Colab
• Scripts vs Functions
• Importing code: Modules and packages
• Standard Library and External package installation
• File I/O

2. Basics of Python (3 weeks)

• Bools
• Numbers
• Conditionals
• Comparisons
• Strings
• Lists
• Loops
• Tuples
• Dicts

3. Numerical analysis using Numpy (1 week)

• Numpy arrays
• Creating arrays
• Basic data types
• Basic visualization
• Indexing and slicing

4. Plotting using Matplotlib (1 week)

• Scatter plots
• Error bars
• Histograms
• Subplots
• Imshow

5. Tabular data analaysis with Pandas (1 week)

• Dataframes and Series
• Reading files
• Indexing
• Missing data management
• Merge and join

6. (Advanced) Machine Learning using Scikit-learn

• What is machine learning
• Types of ML
• Classical ML methods
• Decision trees and RFs
• PCA and SVN
• Perceptron
• Backpropagation

Astronomy

1. Observational Astronomy:
   • Astronomical coordinates
   • MJD
   • Magnitudes
   • Different wavelengths
   • Telescopes
2. Data analysis in astronomy (3 weeks)
   • Software tools:
   • Vizier Simbad, etc
   • CASJOBS
   • Astronomical packages in python:
     • Astropy
     • Astroquery
     • AstroML
   • Modern sky surveys:
   • SDSS
   • Panstarrs
   • Gaia
   • CRTS
3. (Optional) Variable Stars (3 weeks)
   • What are stars?
   • What are variable stars?
   • Types of variability
   • Raw and phase folded light curves (Data Analysis)
   • Methods for finding periods: Lomb scargle (Data Analysis)
4. (Optional) Galaxy (3 weeks)
   • What are galaxies?
   • Galaxy morphology
   • Hubble tuning fork
   • Analyzing image data from telescope

References

• https://exercism.org/ (Basics)
• http://scipy-lectures.org
  • Sections 1.3 - 1.9:
    • File I/O
  • Numpy
• AAVSO guide
• https://datacarpentry.org/astronomy-python/
• Machine Learning - An algorithmic perspective - Stephen Marsland
• Coursera.org:
  • The Evolving Universe - Caltech Weeks 1, 2, ,6 and 7
  • Data Driven Astronomy - University of Sydney
• Youtube:
  • Crash Course Astronomy
  • How do computers read code ?