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Last updated:

September 25, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

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Description

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)

Course Curriculum

  • 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

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