Data Science for Engineers | IIT Madras

Data science for engineers Course philosophy and expectation

Introduction to R

Introduction to R (Continued)

Variables and datatypes in R

Data frames

Recasting and joining of dataframes

Arithmetic,Logical and Matrix operations in R

Advanced programming in R : Functions

Advanced Programming in R : Functions (Continued)

Control structures

Data visualization in R Basic graphics

Linear Algebra for Data science

Solving Linear Equations

Solving Linear Equations ( Continued )

Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors

Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)

Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )

Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )

Statistical Modelling

Random Variables and Probability Mass/Density Functions

Sample Statistics

Hypotheses Testing

Optimization for Data Science

Unconstrained Multivariate Optimization

Unconstrained Multivariate Optimization ( Continued )

Gradient ( Steepest ) Descent ( OR ) Learning Rule

Multivariate Optimization With Equality Constraints

Multivariate Optimization With Inequality Constraints

Introduction to Data Science

Solving Data Analysis Problems - A Guided Thought Process

Module : Predictive Modelling

Linear Regression

Model Assessment

Diagnostics to Improve Linear Model Fit

Simple Linear Regression Model Building

Simple Linear Regression Model Assessment

Simple Linear Regression Model Assessment ( Continued )

Muliple Linear Regression

Cross Validation

Multiple Linear Regression Modelling Building and Selection

Classification

Logisitic Regression

Logisitic Regression ( Continued )

Performance Measures

Logisitic Regression Implementation in R

K - Nearest Neighbors (kNN)

K - Nearest Neighbors implementation in R

K - means Clustering

K - means implementation in R

Data Science for engineers - Summary