NOC:Data Science for Engineers (USB)

Special Price ₹760.00 Regular Price ₹950.00
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SKU
106106179



Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 50

NPTEL Course Size : 15 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

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

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