Data Science for Engineers

Week 1:  Course philosophy and introduction to R  

Week 2:  Linear algebra for data science 

1. Algebraic view - vectors, matrices, product of matrix & vector, rank, null space, solution of over-determined
set of equations and pseudo-inverse) 

2. Geometric view - vectors, distance, projections, eigenvalue decomposition

Week 3:  Statistics (descriptive statistics, notion of probability, distributions, mean, variance, covariance, covariance
matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, confidence
interval for estimates)  

Week 4:  Optimization

Week 5:  1. Optimization

2. Typology of data science problems and a solution framework

Week 6:  1. Simple linear regression and verifying assumptions used in linear regression 

2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selection

Week 7:  Classification using logistic regression

Week 8:  Classification using kNN and k-means clustering