# R Programming for Data Science Training

# R Programming for Data Science Training

(4.9) | 350 Ratings

### Introduction

R Programming for Data Science Training Details | |||
---|---|---|---|

Track | Regular Track | Weekend Track | Fast Track |

Course Duration | 30 Hrs | 8 Weekends | 5 Days |

Hours | 1hr/day | 2 Hours a day | 6 Hours a day |

Training Mode | Online Classroom | Online Classroom | Online Classroom |

Delivery | Instructor Led-Live | Instructor Led-Live | Instructor Led-Live |

### Course Curriculum

**Introduction to R**

- R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.

**R-Packages**

- R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

**Sorting Dataframe**

- R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

**Matrices and Vectors**

- Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

**Reading data from external files**

- Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

**Generating plots**

- Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

**Analysis of Variance (ANOVA)**

- Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

**K-means Clustering**

- K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

**Association Rule Mining**

- Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

**Regression in R**

- Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

**Analyzing Relationship with Regression**

- Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

**Advance Regression**

- Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

**Logistic Regression**

- Logistic Regression Mean, Logistic Regression in R.

**Advance Logistic Regression**

- Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

**Receiver Operating Characteristic (ROC)**

- Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

**Kolmogorov Smirnov Chart**

- Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

**Database connectivity with R**

- Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

**Integrating R with Hadoop**

- Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

**R Case Studies**

- Logistic Regression Case Study
- In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
- Multiple Regression Case Study
- You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.
- Receiver Operating Characteristic (ROC) case study
- You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

**R Programming Projects**

- Project 1
- Domain – Restaurant Revenue Prediction
- Data set – Sales
- Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.
- Project 2
- Domain – Data Analytics
- Objective – To predict about the class of a flower using its petal’s dimensions
- Project 3
- Domain – Finance
- Objective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
- Project 4
- Domain – Stock Market
- Objective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices

### Exam & Certification

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### Course Curriculum

**Introduction to R**

- R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.

**R-Packages**

- R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

**Sorting Dataframe**

- R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

**Matrices and Vectors**

- Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

**Reading data from external files**

- Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

**Generating plots**

- Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

**Analysis of Variance (ANOVA)**

- Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

**K-means Clustering**

- K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

**Association Rule Mining**

- Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

**Regression in R**

- Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

**Analyzing Relationship with Regression**

- Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

**Advance Regression**

- Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

**Logistic Regression**

- Logistic Regression Mean, Logistic Regression in R.

**Advance Logistic Regression**

- Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

**Receiver Operating Characteristic (ROC)**

- Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

**Kolmogorov Smirnov Chart**

- Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

**Database connectivity with R**

- Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

**Integrating R with Hadoop**

- Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

**R Case Studies**

- Logistic Regression Case Study
- In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.
- Multiple Regression Case Study
- You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.
- Receiver Operating Characteristic (ROC) case study
- You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

**R Programming Projects**

- Project 1
- Domain – Restaurant Revenue Prediction
- Data set – Sales
- Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.
- Project 2
- Domain – Data Analytics
- Objective – To predict about the class of a flower using its petal’s dimensions
- Project 3
- Domain – Finance
- Objective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
- Project 4
- Domain – Stock Market
- Objective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices