### Course Curriculum

Introduction to R | |||

Handout – Introduction to R | 00:00:00 | ||

1.1 Getting Started in R | FREE | 00:00:00 | |

1.2 R Environment | FREE | 00:00:00 | |

1.3 R Packages | 00:00:00 | ||

1.4 R Data Types Vectors | 00:00:00 | ||

1.5 R Dataframes | 00:00:00 | ||

1.6 List in R | 00:00:00 | ||

1.7 Factor and Matrices | 00:00:00 | ||

1.8 R History and Scripts | 00:00:00 | ||

1.9 R Functions | 00:00:00 | ||

1.10 Errors in R | 00:00:00 | ||

R quiz unit 1 | Unlimited | ||

Data Handling in R | |||

Handout – Data Handling in R | 00:00:00 | ||

2.1 Data handling introduction | FREE | 00:00:00 | |

2.2 Importing the Datasets | FREE | 00:00:00 | |

2.3 Checklist | 00:00:00 | ||

2.4 Subsetting the Data | 00:00:00 | ||

2.5 Subsetting Variable Condition | 00:00:00 | ||

2.6 Calculated Fields _ ifelse | 00:00:00 | ||

2.7 Sorting and Duplicates | 00:00:00 | ||

2.8 Joining and Merging | 00:00:00 | ||

2.9 Exporting the Data | 00:00:00 | ||

R Quiz Unit 2 | Unlimited | ||

Basic Descriptive Statistics & Reporting | |||

Handout – Basic Statistics, Plots and Reporting in R | 00:00:00 | ||

3.1 Introduction and Sampling | FREE | 00:00:00 | |

3.2 Descriptive Statistics | FREE | 00:00:00 | |

3.3 Percentiles and Quartiles | 00:00:00 | ||

3.4 Box Plots | 00:00:00 | ||

3.5 Creating Graphs and Conclusion | 00:00:00 | ||

R Quiz Unit 3 | Unlimited | ||

Regression Analysis | |||

Handout – Regression Analysis | 00:00:00 | ||

1.1 Introduction and Corelation | FREE | 00:00:00 | |

1.2 LAB Corelation Calculation in R | FREE | 00:00:00 | |

1.3 Beyond Pearson Corelation | 00:00:00 | ||

1.4 From Corelation to Regression | 00:00:00 | ||

1.5 Regression Line Fitting in R | 00:00:00 | ||

1.6 R Squared | 00:00:00 | ||

1.7 Multiple Regression | 00:00:00 | ||

1.8 Adjusted R Squared | 00:00:00 | ||

1.9 Issue with Multiple Regression | 00:00:00 | ||

1.10 Multicollinearity | 00:00:00 | ||

1.11 Regression Conclusion | 00:00:00 | ||

Regression Quiz | Unlimited | ||

Logistic Regression | |||

Handout – Logistic Regression in R | 00:00:00 | ||

2.1 Need of Non-Linear Regression | FREE | 00:00:00 | |

2.2 Logistic Function and Line | FREE | 00:00:00 | |

2.3 Multiple Logistic Regression | 00:00:00 | ||

2.4 Goodness of Fit for a Logistic Regression | 00:00:00 | ||

2.5 Multicollinearity in Logistic Regression in R | 00:00:00 | ||

2.6 Individual Impact of Variables in R | 00:00:00 | ||

2.7 Model Selection in R | 00:00:00 | ||

2.8 Logistic Regression Conclusion | 00:00:00 | ||

Logistic Regression Quiz | Unlimited | ||

Decision Trees | |||

Handout – Decision Tree in R | 00:00:00 | ||

3.1 Introduction to Decision Tree & Segmentation | FREE | 00:00:00 | |

3.2 The Decision Tree Philosophy & The Decision Tree Approach | FREE | 00:00:00 | |

3.3 The Splitting criterion &Entropy Calculation | 00:00:00 | ||

3.4 Information Gain & Calculation | 00:00:00 | ||

3.5 The Decision Tree Algorithm | 00:00:00 | ||

Decision Trees Quiz | Unlimited | ||

Model Selection and Cross Validation | |||

Handout – Model Selection and Cross Validation in R | 00:00:00 | ||

4.1 Introduction to Model Selection | FREE | 00:00:00 | |

4.2 Sensitivity Specificity | FREE | 00:00:00 | |

4.3 Sensitivity Specificity Continued | 00:00:00 | ||

4.4 ROC AUC | 00:00:00 | ||

4.5 The Best Model | 00:00:00 | ||

4.6 Errors | 00:00:00 | ||

4.7 Overfitting Underfitting | 00:00:00 | ||

4.8 Bias_Variance Treadoff | 00:00:00 | ||

4.9 Holdout Data Validation | 00:00:00 | ||

4.10 Ten Fold CV | 00:00:00 | ||

Projects | |||

Direct Mail Marketing | 00:00:00 | ||

Automobile Pricing Strategy | 00:00:00 |

**260 STUDENTS ENROLLED**