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Data Science for Engineers

Data Science for Engineers. Instructors: Prof. Raghunathan Rengaswamy and Prof. Shankar Narasimhan, Department of Computer Science and Engineering, IIT Madras. This course will provide an introduction to data analysis for beginners; a framework to understand different data analysis algorithms; a structured approach to convert high level data analysis problem statements into a well-defined workflow for solution; an introduction to R as a programming language with an emphasis on commands required for this course material; a brief description of concepts in linear algebra and statistics that the participants should focus on; conceptual description of selected machine learning algorithms; practical demonstration of the algorithm through a case study with R. (from nptel.ac.in)

Lecture 09 - Advanced Programming in R: Functions (cont.)

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Lecture 01 - Course Philosophy and Expectation

Lecture 02 - Introduction to R (Programming Language)

Lecture 03 - Introduction to R (cont.)

Lecture 04 - Variables and Datatypes in R

Lecture 05 - Data Frames

Lecture 06 - Recasting and Joining of Dataframes

Lecture 07 - Arithmetic, Logical and Matrix Operations in R

Lecture 08 - Advanced Programming in R: Functions

Lecture 09 - Advanced Programming in R: Functions (cont.)

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 (cont.)

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

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

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

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

Lecture 19 - Statistical Modeling

Lecture 20 - Random Variables and Probability Mass/Density Functions

Lecture 21 - Sample Statistics

Lecture 22 - Hypothesis Testing

Lecture 23 - Optimization for Data Science

Lecture 24 - Unconstrained Multivariate Optimization

Lecture 25 - Unconstrained Multivariate Optimization (cont.)

Lecture 26 - Numerical Example: 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 Modeling

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 (cont.)

Lecture 38 - Multiple Linear Regression

Lecture 39 - Cross Validation

Lecture 40 - Multiple Linear Regression Modeling Building and Section

Lecture 41 - Classification

Lecture 42 - Logistic Regression

Lecture 43 - Logistic Regression (cont.)

Lecture 44 - Performance Measures

Lecture 45 - Logistic Regression Implementation in R

Lecture 46 - K-Nearest Neighbors (K-NN)

Lecture 47 - K-Nearest Neighbors Implementation in R

Lecture 48 - K-Means Clustering

Lecture 49 - K-Means Implementation in R

Lecture 50 - Summary