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Course10610617950 lectures

Data Science for Engineers

Prof. Ragunathan Rengasamy and Prof. Shankar Narasimhan · IIT Madras

Start with lecture 1 At NPTEL

About

Learning Objectives :

Introduce R as a programming language

Introduce the mathematical foundations required for data science

Introduce the first level data science algorithms

Introduce a data analytics problem solving framework

Introduce a practical capstone case study

Learning Outcomes:

Describe a flow process for data science problems (Remembering)

Classify data science problems into standard typology (Comprehension)

Develop R codes for data science solutions (Application)

Correlate results to the solution approach followed (Analysis)

Assess the solution approach (Evaluation)

Lectures

Week 1

  1. Data science for engineers Course philosophy and expectation YouTube
  2. Introduction to R YouTube
  3. Introduction to R (Continued) YouTube
  4. Variables and datatypes in R YouTube
  5. Data frames YouTube
  6. Recasting and joining of dataframes YouTube
  7. Arithmetic,Logical and Matrix operations in R YouTube
  8. Advanced programming in R : Functions YouTube
  9. Advanced Programming in R : Functions (Continued) YouTube
  10. Control structures YouTube
  11. Data visualization in R Basic graphics YouTube

Week 2

  1. Linear Algebra for Data science YouTube
  2. Solving Linear Equations YouTube
  3. Solving Linear Equations ( Continued ) YouTube
  4. Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors YouTube
  5. Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) YouTube
  6. Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) YouTube
  7. Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) YouTube

Week 3

  1. Statistical Modelling YouTube
  2. Random Variables and Probability Mass/Density Functions YouTube
  3. Sample Statistics YouTube
  4. Hypotheses Testing YouTube

Week 4

  1. Optimization for Data Science YouTube
  2. Unconstrained Multivariate Optimization YouTube
  3. Unconstrained Multivariate Optimization ( Continued ) YouTube
  4. Gradient ( Steepest ) Descent ( OR ) Learning Rule YouTube

Week 5

  1. Multivariate Optimization With Equality Constraints YouTube
  2. Multivariate Optimization With Inequality Constraints YouTube
  3. Introduction to Data Science YouTube
  4. Solving Data Analysis Problems - A Guided Thought Process YouTube

Week 6

  1. Module : Predictive Modelling YouTube
  2. Linear Regression YouTube
  3. Model Assessment YouTube
  4. Diagnostics to Improve Linear Model Fit YouTube
  5. Simple Linear Regression Model Building YouTube
  6. Simple Linear Regression Model Assessment YouTube
  7. Simple Linear Regression Model Assessment ( Continued ) YouTube
  8. Muliple Linear Regression YouTube

Week 7

  1. Cross Validation YouTube
  2. Multiple Linear Regression Modelling Building and Selection YouTube
  3. Classification YouTube
  4. Logisitic Regression YouTube
  5. Logisitic Regression ( Continued ) YouTube
  6. Performance Measures YouTube
  7. Logisitic Regression Implementation in R YouTube

Week 8

  1. K - Nearest Neighbors (kNN) YouTube
  2. K - Nearest Neighbors implementation in R YouTube
  3. K - means Clustering YouTube
  4. K - means implementation in R YouTube
  5. Data Science for engineers - Summary YouTube

Syllabus and materials

At NPTEL, as the course publishes them.

Credits and licence

Institution
IIT Madras
Department
Computer Science and Engineering
Taught by
Prof. Ragunathan Rengasamy, Prof. Shankar Narasimhan
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering
Source
NPTEL
Licence
CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
Attribution
NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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