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Data Science for Engineers. Instructors: Prof. Raghunathan Rengaswamy and Prof. Shankar Narasimhan, Department of Computer Science and Engineering, IIT Madras
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Description
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)
Course content
- Lecture 01 – Course Philosophy and Expectation Unlimited
- Lecture 02 – Introduction to R (Programming Language) Unlimited
- Lecture 03 – Introduction to R (cont.) Unlimited
- Lecture 04 – Variables and Datatypes in R Unlimited
- Lecture 05 – Data Frames Unlimited
- Lecture 06 – Recasting and Joining of Dataframes Unlimited
- Lecture 07 – Arithmetic, Logical and Matrix Operations in R Unlimited
- Lecture 08 – Advanced Programming in R: Functions Unlimited
- Lecture 09 – Advanced Programming in R: Functions (cont.) Unlimited
- Lecture 10 – Control Structures Unlimited
- Lecture 11 – Data Visualization in R Basic Graphics Unlimited
- Lecture 12 – Linear Algebra for Data Science Unlimited
- Lecture 13 – Solving Linear Equations Unlimited
- Lecture 14 – Solving Linear Equations (cont.) Unlimited
- Lecture 15 – Linear Algebra – Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Unlimited
- Lecture 16 – Linear Algebra – Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Unlimited
- Lecture 17 – Linear Algebra – Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Unlimited
- Lecture 18 – Linear Algebra – Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors Unlimited
- Lecture 19 – Statistical Modeling Unlimited
- Lecture 20 – Random Variables and Probability Mass/Density Functions Unlimited
- Lecture 21 – Sample Statistics Unlimited
- Lecture 22 – Hypothesis Testing Unlimited
- Lecture 23 – Optimization for Data Science Unlimited
- Lecture 24 – Unconstrained Multivariate Optimization Unlimited
- Lecture 25 – Unconstrained Multivariate Optimization (cont.) Unlimited
- Lecture 26 – Numerical Example: Gradient (Steepest) Descent (OR) Learning Rule Unlimited
- Lecture 27 – Multivariate Optimization with Equality Constraints Unlimited
- Lecture 28 – Multivariate Optimization with Inequality Constraints Unlimited
- Lecture 29 – Introduction to Data Science Unlimited
- Lecture 30 – Solving Data Analysis Problems – A Guided Thought Process Unlimited
- Lecture 31 – Module: Predictive Modeling Unlimited
- Lecture 32 – Linear Regression Unlimited
- Lecture 33 – Model Assessment Unlimited
- Lecture 34 – Diagnostics to Improve Linear Model Fit Unlimited
- Lecture 35 – Simple Linear Regression Model Building Unlimited
- Lecture 36 – Simple Linear Regression Model Assessment Unlimited
- Lecture 37 – Simple Linear Regression Model Assessment (cont.) Unlimited
- Lecture 38 – Multiple Linear Regression Unlimited
- Lecture 39 – Cross Validation Unlimited
- Lecture 40 – Multiple Linear Regression Modeling Building and Section Unlimited
- Lecture 41 – Classification Unlimited
- Lecture 42 – Logistic Regression Unlimited
- Lecture 43 – Logistic Regression (cont.) Unlimited
- Lecture 44 – Performance Measures Unlimited
- Lecture 45 – Logistic Regression Implementation in R Unlimited
- Lecture 46 – K-Nearest Neighbors (K-NN) Unlimited
- Lecture 47 – K-Nearest Neighbors Implementation in R Unlimited
- Lecture 48 – K-Means Clustering Unlimited
- Lecture 49 – K-Means Implementation in R Unlimited
- Lecture 50 – Summary Unlimited
Instructor
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