EP 219 Data Analysis and Interpretation (Autumn 2017-18)

Instructor Name: Vikram Rentala 

Course Type: Theory

Pre-requisites:Matrix algebra, Integration and Differentiation

Course Content: Probability Theory - Axioms and Bayes' Theorem

Probability Density/Distribution Functions and their characteristics (mean, variance etc.) - Exponential, Gaussian, Poisson
Transformation of random variables
Multivariate probability density/distribution function
Contour Plots
Correlation, Covariance and Independence
Reduction of number of variates
Probability Distributions - Bernoulli, Binomial, Multinomial, Uniform
Central Limit Theorem
Multidimensional Gaussian random variables
Chi squared distribution and fitting experimental data
Measurement Errors
Averaging measurements (including correlation)
Statistical Inference - drawing conclusions from data
Likelihood and maximum likelihood estimate

Other topics covered:

Books: Introduction to Statistics and Data Analysis for Physicists - G. Bohm and G. Zech

Lectures: Attendance was never taken. All lectures were taught on the blackboard.

Assignments:The class was divided into groups of four and graded coding assignments (4-5) were given in which we had to code in Python and write the report in Latex, both of which we had to learn ourselves. 

Exams and Grading: Midsem - 30 marks, Assignments - 30 marks, Endsem - 40 marks 

Online material:

Follow-up Courses:

Pro-tips: Lectures are taught directly from Bohm and Zech

ShareLatex can be used to make Latex documents 

Personal Comments:Lectures were delivered and doubts were cleared with utmost clarity and although initially lectures might seem slow and simple, once you attend lectures, there is little effort needed from your side for preparation.

Respondent: Samarth Sabu