Data Science for Engineers

Por: Swayam . en: ,

Week 1: Course philosophy and introduction to RWeek 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: OptimizationWeek 5: 1. Optimization 2. Typology of data science problems and a solution frameworkWeek 6: 1. Simple linear regression and verifying assumptions used in linear regression 2. Multivariate linear regression, model assessment, assessing importance of different variables, subset selectionWeek 7: Classification using logistic regressionWeek 8: Classification using kNN and k-means clustering