This is my 14 hours lectures on Data Analysis and Visualization teaching at Department of Space and Applications (DSA), University of Science and Technology of Hanoi (USTH). Lectures are associated with Python Jupyter Notebooks:

Lecture 0: Introduction to Jupyter notebook and Python [ pdf ] ; [ Notebook part1], and [ Notebook part 2 ]

Lecture 1: Fundamental statistics quantities: Mean, median, standard deviation (variance), correlation [pdf] ; [ Notebook ]

Lecture 2: Probability distributions: Binomial, Uniform, Normal (or Gaussian), Poisson, Gamma, T-Student’s, Chi-square, the central limit theorem [pdf ] ; [ Notebook ]

Lecture 3: Hypothesis testing, model fitting and parameter estimation [ pdf ] ; [ Notebook ]

Lecture 4: Principal components analysis (PCA) and Bayesian methods [ pdf ] ; [ Notebook ]

Lecture 5: Basic Machine Learning using Scikit-learn tool [ pdf ] ; [ Notebook ]

References

[1] Practical Statistics for Astronomers - Wall & Jenkins

[2] Book: Statistical Methods in Experimental Physics - Frederick James

[3] http://astronomy.swin.edu.au/~cblake/stats.html (Statistics course-Swinburne University)