# Data Science for Engineers

IIT Madras, , Prof. Prof. Shankar NarasimhanProf. Ragunathan Rengasamy

Updated On 02 Feb, 19

IIT Madras, , Prof. Prof. Shankar NarasimhanProf. Ragunathan Rengasamy

Updated On 02 Feb, 19

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4.1 ( 11 )

Course philosophy and introduction to R

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

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)

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

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.