Lecture 1 - Data science for engineers Course philosophy and expectation

Lecture 2 - Introduction to R

Lecture 3 - Introduction to R (Continued...)

Lecture 4 - Variables and datatypes in R

Lecture 5 - Data frames

Lecture 6 - Recasting and joining of dataframes

Lecture 7 - Arithmetic,Logical and Matrix operations in R

Lecture 8 - Advanced programming in R : Functions

Lecture 9 - Advanced Programming in R : Functions (Continued...)

Lecture 10 - Control structures

Lecture 11 - Data visualization in R Basic graphics

Lecture 12 - Linear Algebra for Data science

Lecture 13 - Solving Linear Equations

Lecture 14 - Solving Linear Equations (Continued...)

Lecture 15 - Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors

Lecture 16 - Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors (Continued... 1)

Lecture 17 - Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors (Continued... 2)

Lecture 18 - Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors (Continued... 3)

Lecture 19 - Statistical Modelling

Lecture 20 - Random Variables and Probability Mass/Density Functions

Lecture 21 - Sample Statistics

Lecture 22 - Hypotheses Testing

Lecture 23 - Optimization for Data Science

Lecture 24 - Unconstrained Multivariate Optimization

Lecture 25 - Unconstrained Multivariate Optimization (Continued...)

Lecture 26 - Gradient (Steepest) Descent (OR) Learning Rule

Lecture 27 - Multivariate Optimization With Equality Constraints

Lecture 28 - Multivariate Optimization With Inequality Constraints

Lecture 29 - Introduction to Data Science

Lecture 30 - Solving Data Analysis Problems - A Guided Thought Process

Lecture 31 - Module : Predictive Modelling

Lecture 32 - Linear Regression

Lecture 33 - Model Assessment

Lecture 34 - Diagnostics to Improve Linear Model Fit

Lecture 35 - Simple Linear Regression Model Building

Lecture 36 - Simple Linear Regression Model Assessment

Lecture 37 - Simple Linear Regression Model Assessment (Continued...)

Lecture 38 - Muliple Linear Regression

Lecture 39 - Cross Validation

Lecture 40 - Multiple Linear Regression Modelling Building and Selection

Lecture 41 - Classification

Lecture 42 - Logisitic Regression

Lecture 43 - Logisitic Regression (Continued...)

Lecture 44 - Performance Measures

Lecture 45 - Logisitic Regression Implementation in R

Lecture 46 - K-Nearest Neighbors (kNN)

Lecture 47 - K-Nearest Neighbors implementation in R

Lecture 48 - K-means Clustering

Lecture 49 - K-means implementation in R

Lecture 50 - Data Science for engineers - Summary