NOC:Data Science for Engineers


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