| Module Name | Download |
|---|---|
| noc20-cs28_Week_01_Assignment_01 | noc20-cs28_Week_01_Assignment_01 |
| noc20-cs28_Week_02_Assignment_01 | noc20-cs28_Week_02_Assignment_01 |
| noc20-cs28_Week_02_Assignment_02 | noc20-cs28_Week_02_Assignment_02 |
| noc20-cs28_Week_03_Assignment_01 | noc20-cs28_Week_03_Assignment_01 |
| noc20-cs28_Week_03_Assignment_02 | noc20-cs28_Week_03_Assignment_02 |
| noc20-cs28_Week_04_Assignment_01 | noc20-cs28_Week_04_Assignment_01 |
| noc20-cs28_Week_04_Assignment_02 | noc20-cs28_Week_04_Assignment_02 |
| noc20-cs28_Week_05_Assignment_01 | noc20-cs28_Week_05_Assignment_01 |
| noc20-cs28_Week_05_Assignment_02 | noc20-cs28_Week_05_Assignment_02 |
| noc20-cs28_Week_06_Assignment_01 | noc20-cs28_Week_06_Assignment_01 |
| noc20-cs28_Week_06_Assignment_02 | noc20-cs28_Week_06_Assignment_02 |
| noc20-cs28_Week_07_Assignment_01 | noc20-cs28_Week_07_Assignment_01 |
| noc20-cs28_Week_07_Assignment_02 | noc20-cs28_Week_07_Assignment_02 |
| noc20-cs28_Week_08_Assignment_01 | noc20-cs28_Week_08_Assignment_01 |
| noc20-cs28_Week_08_Assignment_02 | noc20-cs28_Week_08_Assignment_02 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download Verified |
| 2 | Introduction to R | Download Verified |
| 3 | Introduction to R (Continued) | Download Verified |
| 4 | Variables and datatypes in R | Download Verified |
| 5 | Data frames | Download Verified |
| 6 | Recasting and joining of dataframes | Download Verified |
| 7 | Arithmetic,Logical and Matrix operations in R | Download Verified |
| 8 | Advanced programming in R : Functions | Download Verified |
| 9 | Advanced Programming in R : Functions (Continued) | Download Verified |
| 10 | Control structures | Download Verified |
| 11 | Data visualization in R Basic graphics | Download Verified |
| 12 | Linear Algebra for Data science | Download Verified |
| 13 | Solving Linear Equations | Download Verified |
| 14 | Solving Linear Equations ( Continued ) | Download Verified |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download Verified |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download Verified |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download Verified |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download Verified |
| 19 | Statistical Modelling | Download Verified |
| 20 | Random Variables and Probability Mass/Density Functions | Download Verified |
| 21 | Sample Statistics | Download Verified |
| 22 | Hypotheses Testing | Download Verified |
| 23 | Optimization for Data Science | Download Verified |
| 24 | Unconstrained Multivariate Optimization | Download Verified |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download Verified |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download Verified |
| 27 | Multivariate Optimization With Equality Constraints | Download Verified |
| 28 | Multivariate Optimization With Inequality Constraints | Download Verified |
| 29 | Introduction to Data Science | Download Verified |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download Verified |
| 31 | Module : Predictive Modelling | Download Verified |
| 32 | Linear Regression | Download Verified |
| 33 | Model Assessment | Download Verified |
| 34 | Diagnostics to Improve Linear Model Fit | Download Verified |
| 35 | Simple Linear Regression Model Building | Download Verified |
| 36 | Simple Linear Regression Model Assessment | Download Verified |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download Verified |
| 38 | Muliple Linear Regression | Download Verified |
| 39 | Cross Validation | Download Verified |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download Verified |
| 41 | Classification | Download Verified |
| 42 | Logisitic Regression | Download Verified |
| 43 | Logisitic Regression ( Continued ) | Download Verified |
| 44 | Performance Measures | Download Verified |
| 45 | Logisitic Regression Implementation in R | Download Verified |
| 46 | K - Nearest Neighbors (kNN) | Download Verified |
| 47 | K - Nearest Neighbors implementation in R | Download Verified |
| 48 | K - means Clustering | Download Verified |
| 49 | K - means implementation in R | Download Verified |
| 50 | Data Science for engineers - Summary | Download Verified |
| Sl.No | Chapter Name | Bengali |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | Gujarati |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | Hindi |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | Marathi |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | Tamil |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |
| Sl.No | Chapter Name | Telugu |
|---|---|---|
| 1 | Data science for engineers Course philosophy and expectation | Download |
| 2 | Introduction to R | Download |
| 3 | Introduction to R (Continued) | Download |
| 4 | Variables and datatypes in R | Download |
| 5 | Data frames | Download |
| 6 | Recasting and joining of dataframes | Download |
| 7 | Arithmetic,Logical and Matrix operations in R | Download |
| 8 | Advanced programming in R : Functions | Download |
| 9 | Advanced Programming in R : Functions (Continued) | Download |
| 10 | Control structures | Download |
| 11 | Data visualization in R Basic graphics | Download |
| 12 | Linear Algebra for Data science | Download |
| 13 | Solving Linear Equations | Download |
| 14 | Solving Linear Equations ( Continued ) | Download |
| 15 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors | Download |
| 16 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1) | Download |
| 17 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 ) | Download |
| 18 | Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 ) | Download |
| 19 | Statistical Modelling | Download |
| 20 | Random Variables and Probability Mass/Density Functions | Download |
| 21 | Sample Statistics | Download |
| 22 | Hypotheses Testing | Download |
| 23 | Optimization for Data Science | Download |
| 24 | Unconstrained Multivariate Optimization | Download |
| 25 | Unconstrained Multivariate Optimization ( Continued ) | Download |
| 26 | Gradient ( Steepest ) Descent ( OR ) Learning Rule | Download |
| 27 | Multivariate Optimization With Equality Constraints | Download |
| 28 | Multivariate Optimization With Inequality Constraints | Download |
| 29 | Introduction to Data Science | Download |
| 30 | Solving Data Analysis Problems - A Guided Thought Process | Download |
| 31 | Module : Predictive Modelling | Download |
| 32 | Linear Regression | Download |
| 33 | Model Assessment | Download |
| 34 | Diagnostics to Improve Linear Model Fit | Download |
| 35 | Simple Linear Regression Model Building | Download |
| 36 | Simple Linear Regression Model Assessment | Download |
| 37 | Simple Linear Regression Model Assessment ( Continued ) | Download |
| 38 | Muliple Linear Regression | Download |
| 39 | Cross Validation | Download |
| 40 | Multiple Linear Regression Modelling Building and Selection | Download |
| 41 | Classification | Download |
| 42 | Logisitic Regression | Download |
| 43 | Logisitic Regression ( Continued ) | Download |
| 44 | Performance Measures | Download |
| 45 | Logisitic Regression Implementation in R | Download |
| 46 | K - Nearest Neighbors (kNN) | Download |
| 47 | K - Nearest Neighbors implementation in R | Download |
| 48 | K - means Clustering | Download |
| 49 | K - means implementation in R | Download |
| 50 | Data Science for engineers - Summary | Download |