Data Science Certification Training - R Programming

Overview

The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.

Price (*ask for discount) 400 USD
Access period 180 days

Prerequisite list

  • There are no prerequisites for this course. If you are new in the field of data science, this is the best course to start with.

Audience list

  • IT professionals looking for a career switch into data science and analytics
  • Software developers looking for a career switch into data science and analytics
  • Professionals working in data and business analytics
  • Graduates looking to build a career in analytics and data science
  • Anyone with a genuine interest in the data science field
  • Experienced professionals who would like to harness data science in their fields

What is included

  • 24 hours of self-paced video
  • 8 real-life industry projects in retail, insurance, finance, airlines domains.
  • Hands-on practice with R CloudLabs .
  • Includes statistical concepts like regression and cluster analysis.
  • Includes “Business Analytics with Excel” course.

Certification Info

  • How To Earn?  Complete 85% of the course. Complete 1 project and 1 simulation test with a minimum score of 60%.
  • How To Maintain?  N/A

Certification Exam Format

  • No Exam

Retake policy

  • N/A.

Enrollment Policy

  • You should pay the online course fee then the online course access will be granted to you within 1 week after receiving payment.
  • Course fee payment is not refundable.

Frequently Asked Questions

Course Outline

Introduction to Business Analytics
  • Introduction
  • Objectives
  • Need of Business Analytics
  • Business Decisions
  • Introduction to Business Analytics
  • Features of Business Analytics
  • Types of Business Analytics
  • Descriptive Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Prescriptive Analytics (contd.)
  • Supply Chain Analytics
  • Health Care Analytics
  • Marketing Analytics
  • Human Resource Analytics
  • Web Analytics
  • Application of Business Analytics - Wal-Mart Case Study
  • Application of Business Analytics - Signet Bank Case Study
  • Business Decisions
  • Business Intelligence (BI)
  • Data Science
  • Importance of Data Science
  • Data Science as a Strategic Asset
  • Big Data
  • Analytical Tools
  • Quiz
  • Summary
  • Conclusion
Introduction to R
  • Introduction
  • Objectives
  • An Introduction to R
  • Comprehensive R Archive Network (CRAN)
  • Cons of R
  • Companies Using R
  • Understanding R
  • Installing R on Various Operating Systems
  • Installing R on Windows from CRAN Website
  • Demo - Install R
  • Install R
  • IDEs for R
  • Installing RStudio on Various Operating Systems
  • Demo - Install RStudio
  • Install RStudio
  • Steps in R Initiation
  • Benefits of R Workspace
  • Setting the Workplace
  • Functions and Help in R
  • Demo - Access the Help Document
  • Access the Help Document
  • R Packages
  • Installing an R Package
  • Demo - Install and Load a Package
  • Install and Load a Package
  • Quiz
  • Summary
  • Conclusion
R Programming
  • Introduction
  • Objectives
  • Operators in R
  • Arithmetic Operators
  • Demo - Perform Arithmetic Operations
  • Use Arithmetic Operations
  • Relational Operators
  • Demo - Use Relational Operators
  • Use Relational Operators
  • Logical Operators
  • Demo - Perform Logical Operations
  • Use Logical Operators
  • Assignment Operators
  • Demo - Use Assignment Operator
  • Use Assignment Operator
  • Conditional Statements in R
  • Ifelse() Function
  • Demo - Use Conditional Statements
  • Use Conditional Statements
  • Switch Function
  • Demo - Use the Switch Function
  • Use Switch Function
  • Loops in R
  • Break Statement
  • Next Statement
  • Demo - Use Loops
  • Use Loops
  • Scan() Function
  • Running an R Script
  • Running a Batch Script
  • R Functions
  • Demo - Use R Functions
  • Use Commonly Used Functions
  • Demo - Use String Functions
  • Use Commonly-USed String Functions
  • Quiz
  • Summary
  • Conclusion
R Data Structure
  • Introduction
  • Objectives
  • Types of Data Structures in R
  • Vectors
  • Demo - Create a Vector
  • Create a Vector
  • Scalars
  • Colon Operator
  • Accessing Vector Elements
  • Matrices
  • Accessing Matrix Elements
  • Demo - Create a Matrix
  • Create a Matrix
  • Arrays
  • Accessing Array Elements
  • Demo - Create an Array
  • Create an Array
  • Data Frames
  • Elements of Data Frames
  • Demo - Create a Data Frame
  • Create a Data Frame
  • Factors
  • Demo - Create a Factor
  • Create a Factor
  • Lists
  • Demo - Create a List
  • Create a List
  • Importing Files in R
  • Importing an Excel File
  • Importing a Minitab File
  • Importing a Table File
  • Importing a CSV File
  • Demo - Read Data from a File
  • Read Data from a File
  • Exporting Files from R
  • Quiz
  • Summary
  • Conclusion
Apply Functions
  • Introduction
  • Objectives
  • Types of Apply Functions
  • Apply() Function
  • Demo - Use Apply() Function
  • Use Apply Function
  • Lapply() Function
  • Demo - Use Lapply() Function
  • Use Lapply Function
  • Sapply() Function
  • Demo - Use Sapply() Function
  • Use Sapply Function
  • Tapply() Function
  • Demo - Use Tapply() Function
  • Use Tapply Function
  • Vapply() Function
  • Demo - Use Vapply() Function00:05
  • Use Vapply Function
  • Mapply() Function
  • Dplyr Package - An Overview
  • Dplyr Package - The Five Verbs
  • Installing the Dplyr Package
  • Functions of the Dplyr Package
  • Functions of the Dplyr Package - Select()
  • Demo - Use the Select() Function
  • Use the Select Function
  • Functions of Dplyr-Package - Filter()
  • Demo - Use the Filter() Function
  • Use Select Function
  • Functions of Dplyr Package - Arrange()
  • Demo - Use the Arrange() Function
  • Use Arrange Function
  • Functions of Dplyr Package - Mutate()
  • Functions of Dply Package - Summarise()
  • Demo - Use the Summarise() Function
  • Use Summarise Function
  • Quiz
  • Summary
  • Conclusion
Data Visualization
  • Introduction
  • Objectives
  • Graphics in R
  • Types of Graphics
  • Bar Charts
  • Creating Simple Bar Charts
  • Editing a Simple Bar Chart
  • Demo - Create a Bar Chart
  • Create a Bar Chart
  • Demo - Create a Stacked Bar Plot and Grouped Bar Plot
  • Create a Stacked Bar Plot and Grouped Bar Plot
  • Pie Charts
  • Editing a Pie Chart
  • Editing a Pie Chart (contd.)
  • Demo - Create a Pie Chart
  • Create a Pie Chart
  • Histograms
  • Creating a Histogram
  • Kernel Density Plots
  • Creating a Kernel Density Plot
  • Demo - Create Histograms and a Density Plot
  • Create Histograms and a Density Plot
  • Line Charts
  • Creating a Line Chart
  • Box Plots
  • Creating a Box Plot
  • Demo - Create Line Graphs and a Box Plot
  • Create Line Graphs and a Box Plot
  • Heat Maps
  • Creating a Heat Map
  • Demo - Create a Heat Map
  • Create a Heatmap
  • Word Clouds
  • Creating a Word Cloud
  • Demo - Create a Word Cloud
  • Create a Word Cloud
  • File Formats for Graphic Outputs
  • Saving a Graphic Output as a File
  • Demo - Save Graphics to a File
  • Save Graphics to a File
  • Exporting Graphs in RStudiz
  • Exporting Graphs as PDFs in RStudio
  • Demo - Save Graphics Using RStudio
  • Save Graphics Using RStudio
  • Quiz
  • Summary
  • Conclusion
Introduction to Statistics
  • Introduction
  • Objectives
  • Basics of Statistics
  • Types of Data
  • Qualitative vs. Quantitative Analysis
  • Types of Measurements in Order
  • Nominal Measurement
  • Ordinal Measurement
  • Interval Measurement
  • Ratio Measurement
  • Statistical Investigation
  • Statistical Investigation Steps
  • Normal Distribution00:58
  • Example of Normal Distribution
  • Importance of Normal Distribution in Statistics
  • Use of the Symmetry Property of Normal Distribution
  • Standard Normal Distribution
  • Demo - Use Probability Distribution Functions
  • Use Probability Distribution Functions
  • Distance Measures
  • Distance Measures - A Comparison
  • Euclidean Distance
  • Example of Euclidean Distance
  • Manhattan Distance
  • Minkowski Distance
  • Mahalanobis Distance
  • Cosine Similarity
  • Correlation
  • Correlation Measures Explained
  • Pearson Product Moment Correlation (PPMC)
  • Pearson Correlation - Case Study
  • Dist() Function in R
  • Demo - Perform the Distance Matrix Computations
  • Perform the Distance Matrix Computations
  • Quiz
  • Summary
  • Conclusion
Hypothesis Testing I
  • Introduction
  • Objectives
  • Hypothesis
  • Need of Hypothesis Testing in Businesses
  • Null Hypothesis00:26
  • Alternate Hypothesis
  • Null vs. Alternate Hypothesis
  • Chances of Errors in Sampling
  • Types of Errors
  • Contingency Table
  • Decision Making
  • Critical Region
  • Level of Significance
  • Confidence Coefficient
  • Bita Risk
  • Power of Test
  • Factors Affecting the Power of Test
  • Types of Statistical Hypothesis Tests
  • An Example of Statistical Hypothesis Tests
  • Upper Tail Test
  • Test Statistic
  • Factors Affecting Test Statistic
  • Critical Value Using Normal Probability Table
  • Quiz
  • Summary
  • Conclusion
Hypothesis Testing II
  • Introduction
  • Objectives
  • Parametric Tests
  • Z-Test
  • Z-Test in R - Case Study
  • T-Test
  • T-Test in R - Case Study
  • Demo - Use Normal and Student Probability Distribution Functions
  • Use Normal and Student Probability Distribution Functions
  • Testing Null Hypothesis
  • Testing Null Hypothesis
  • Testing Null Hypothesis
  • Testing Null Hypothesis
  • Testing Null Hypothesis
  • Testing Null Hypothesis
  • Objectives of Null Hypothesis Test
  • Three Types of Hypothesis Tests
  • Hypothesis Tests About Population Means
  • Decision Rules
  • Hypothesis Tests About Population Means - Case Study 1
  • Hypothesis Tests About Population Means - Case Study 2
  • Hypothesis Tests About Population Proportions
  • Hypothesis Tests About Population Proportions - Case Study 1
  • Chi-Square Test
  • Steps of Chi-Square Test
  • Important Points of Chi-Square Test (contd.)
  • Degree of Freedom
  • Chi-Square Test for Independence
  • Chi-Square Test for Goodness of Fit
  • Chi-Square Test for Independence - Case Study
  • Chi-Square Test in R - Case Study
  • Demo - Use Chi-Squared Test Statistics
  • Use Chi-Squared Test Statistics
  • Introduction to ANOVA Test
  • One-Way ANOVA Test
  • The F-Distribution and F-Ratio
  • F-Ratio Test
  • F-Ratio Test in R - Example
  • One-Way ANOVA Test - Case Study
  • One-Way ANOVA Test in R - Case Study
  • Demo - Perform ANOVA
  • Perform ANOVA
  • Quiz
  • Summary
  • Conclusion
Regression Analysis
  • Introduction
  • Objectives
  • Introduction to Regression Analysis
  • Use of Regression Analysis - Examples
  • Types Regression Analysis
  • Simple Regression Analysis
  • Multiple Regression Models
  • Simple Linear Regression Model
  • Simple Linear Regression Model Explained
  • Demo - Perform Simple Linear Regression
  • Perform Simple Linear Regression
  • Correlation
  • Correlation Between X and Y
  • Demo - Find Correlation
  • Find Correlation
  • Method of Least Squares Regression Model
  • Coefficient of Multiple Determination Regression Model
  • Standard Error of the Estimate Regression Model
  • Dummy Variable Regression Model
  • Interaction Regression Model
  • Non-Linear Regression
  • Non-Linear Regression Models
  • Demo - Perform Regression Analysis with Multiple Variables
  • Perform Regression Analysis with Multiple Variables
  • Non-Linear Models to Linear Models
  • Algorithms for Complex Non-Linear Models
  • Quiz
  • Summary
  • Conclusion
Classification
  • Introduction
  • Objectives
  • Introduction to Classification
  • Examples of Classification
  • Classification vs. Prediction
  • Classification System
  • Classification Process
  • Classification Process - Model Construction
  • Classification Process - Model Usage in Prediction
  • Issues Regarding Classification and Prediction
  • Data Preparation Issues
  • Evaluating Classification Methods Issues
  • Decision Tree
  • Decision Tree - Dataset
  • Classification Rules of Trees
  • Overfitting in Classification
  • Tips to Find the Final Tree Size
  • Basic Algorithm for a Decision Tree
  • Statistical Measure - Information Gain
  • Calculating Information Gain - Example
  • Calculating Information Gain for Continuous-Value Attributes
  • Enhancing a Basic Tree
  • Decision Trees in Data Mining
  • Demo - Model a Decision Tree
  • Model a Decision Tree
  • Naive Bayes Classifier Model
  • Features of Naive Bayes Classifier Model
  • Bayesian Theorem
  • Naive Bayes Classifier
  • Applying Naive Bayes Classifier - Example
  • Naive Bayes Classifier - Advantages and Disadvantages
  • Demo - Perform Classification Using the Naive Bayes Method
  • Perform Classification Using the Naive Bayes Method
  • Nearest Neighbor Classifiers
  • Computing Distance and Determining Class
  • Choosing the Value of K
  • Scaling Issues in Nearest Neighbor Classification
  • Support Vector Machines
  • Advantages of Support Vector Machines
  • Geometric Margin in SVMs
  • Linear SVMs
  • Non-Linear SVMs
  • Demo - Support a Vector Machine
  • Support a Vector Machine
  • Quiz
  • Summary
  • Conclusion
Clustering
  • Introduction
  • Objectives
  • Introduction to Clustering
  • Clustering vs. Classification
  • Use Cases of Clustering
  • Clustering Models
  • K-means Clustering
  • K-means Clustering Algorithm
  • Pseudo Code of K-means
  • K-means Clustering Using R
  • K-means Clustering - Case Study
  • Demo - Perform Clustering Using K-means
  • Perform Clustering Using Kmeans
  • Hierarchical Clustering
  • Hierarchical Clustering Algorithms
  • Requirements of Hierarchical Clustering Algorithms
  • Agglomerative Clustering Process
  • Hierarchical Clustering - Case Study
  • Demo - Perform Hierarchical Clustering
  • Perform Hierarchical Clustering
  • DBSCAN Clustering
  • Concepts of DBSCAN
  • DBSCAN Clustering Algorithm
  • DBSCAN in R
  • DBSCAN Clustering - Case Study
  • Quiz
  • Summary
  • Conclusion
Association
  • Introduction
  • Objectives
  • Association Rule Mining
  • Application Areas of Association Rule Mining
  • Parameters of Interesting Relationships
  • Association Rules
  • Association Rule Strength Measures
  • Limitations of Support and Confidence
  • Apriori Algorithm
  • Apriori Algorithm - Example
  • Applying Aprior Algorithm
  • Step 1 - Mine All Frequent Item Sets
  • Algorithm to Find Frequent Item Set
  • Finding Frequent Item Set - Example
  • Ordering Items
  • Candidate Generation
  • Candidate Generation - Example
  • Step 2 - Generate Rules from Frequent Item Sets
  • Generate Rules from Frequent Item Sets - Example
  • Demo - Perform Association Using the Apriori Algorithm
  • Perform Association Using the Apriori Algorithm
  • Demo - Perform Visualization on Associated Rules
  • Perform Visualization on Associated Rules
  • Problems with Association Mining
  • Quiz
  • Summary
  • Conclusion