COURSE LAYOUT
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
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