Data Science for Engineers

Week 1: Course philosophy and introduction to R

Week 2: Linear algebra for data science

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

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: Optimization 2. Typology of data science problems and a solution framework

Week 6: 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