Course curriculum

  • 1

    Course Introdution

  • 2

    1.Course philosophy and introduction to R

    • 1.Data science for engineers - Course philosophy and expectation

    • 2.Introduction to R

    • 3.Introduction to R (continued)

    • 4.Variables and Datatypes in R

    • 5.Data frames

    • 6.Recasting and joining of dataframes

    • 7.Arithmetic,Logical and Matrix operations in R

    • 8.Advanced programming in R Functions

    • 9.Advanced Programming in R Functions (Continued)

    • 10.Control structures

    • 11.Data visualization in R Basic graphics
  • 3

    2.Linear algebra for data science

    • 12.Linear Algebra for Data science

    • 13.Solving Linear Equations

    • 14.Solving Linear Equations ( Continued )

    • 15.Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors

    • 16.Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1 )

    • 17.Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )

    • 18.Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )
  • 4

    3.Statistics

    • 19.Statistical Modelling

    • 20.Random Variables and Probability Mass Density Functions

    • 21.Sample Statistics

    • 22.Hypothesis Testing
  • 5

    4.Optimization

    • 23.Optimization for Data Science

    • 24.Unconstrained Multivariate Optimization

    • 25.Unconstrained Multivariate Optimization ( Continued )

    • 26.Gradient ( Steepest ) Descent ( OR ) Learning Rule
  • 6

    5.Typology of data science problems and a solution framework

    • 27.Multivariate Optimization With Equality Constraints

    • 28.Multivariate Optimization With Inequality Constraints

    • 29.Introduction to Data Science

    • 30.Solving Data Analysis Problems - A Guided Thought Process
  • 7

    6.Simple linear regression and Multivariate linear regression, model assessment

    • 31.Module Predictive Modelling

    • 32.Linear Regression

    • 33.Model Assessment

    • 34.Diagnostics to Improve Linear Model Fit

    • 35.Simple Linear Regression Model Building

    • 36..Simple Linear Regression Model Assessment

    • 37.Simple Linear Regression Model Assessment ( Continued )

    • 38.Muliple Linear Regression
  • 8

    7.Classification using logistic regression

    • 39.Cross Validation

    • 40.Multiple Linear Regression Modelling Building and Selection

    • 41.Classification

    • 42.Logisitic Regression

    • 43.Logisitic Regression ( Continued )

    • 44.Performance Measures

    • 45.Logisitic Regression Implementation in R
  • 9

    8.Classification using kNN and k-means clustering

    • 46.K - Nearest Neighbors (kNN)

    • 47.K - Nearest Neighbors implementation in R

    • 48.K - means Clustering

    • 49.K - means implementation in R

    • 50.Data Science for engineers - Summary