About the course

Learn and enhance your data analytics by utilizing R programming language which is widely used tool for data analysis and visualization. With this data science course, you'll get hands-on training on R CloudLab by executing different real-life, industry-based projects in the areas of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

What are the requirements?

No prior knowledge of Statistics the language of R programming or analytic techniques is required.

What I am going to get from this course

  • Great understanding of business analytics
  • You will learn how to Install R, R-studio
  • Understand how various statements are executed in R
  • Understand workspace setup, and learn about the various R packages
  • Gain an in-depth understanding of data structure used in R and learn to import/export data in R
  • Understand and use the various graphics in R for data visualization

Who is the target audience?

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

Curriculum


Module 1: Introduction to Business Analytics

  • 1.1  Introduction
  • 1.2  Objectives
  • 1.3  Need of Business Analytics
  • 1.4  Business Decisions
  • 1.5  Introduction to Business Analytics
  • 1.6  Features of Business Analytics
  • 1.7  Types of Business Analytics
  • 1.8  Descriptive Analytics
  • 1.9  Predictive Analytics
  • 1.10  Predictive Analytics (contd.)
  • 1.11  Prescriptive Analytics
  • 1.12  Prescriptive Analytics (contd.)
  • 1.13  Supply Chain Analytics
  • 1.14  Health Care Analytics
  • 1.15  Marketing Analytics
  • 1.16  Human Resource Analytics
  • 1.17  Web Analytics
  • 1.18  Application of Business Analytics - Wal-Mart Case Study
  • 1.19  Application of Busi
  • 1.20  Business Intelligence (BI)
  • 1.21  Data Science
  • 1.22  Importance of Data Science
  • 1.23  Data Science as a Strategic Asset
  • 1.24  Big Data

Module 2: Introduction to R

  • 2.1  Introduction
  • 2.2  Objectives
  • 2.3  An Introduction to R
  • 2.4  Comprehensive R Archive Network (CRAN)
  • 2.5  Cons of R
  • 2.6  Companies Using R
  • 2.7  Understanding R
  • 2.8  Installing R on Various Operating Systems
  • 2.9  Installing R on Windows from CRAN Website
  • 2.10  Demo - Install R
  • 2.11  Install R
  • 2.12  IDEs for R
  • 2.13  Installing RStudio on Various Operating Systems
  • 2.14  Demo - Install RStudio
  • 2.15  Install RStudio
  • 2.16  Steps in R Initiation
  • 2.17  Benefits of R Workspace
  • 2.18  Setting the Workplace
  • 2.19  Functions and Help in R
  • 2.20  Demo - Access the Help Document
  • 2.21  Access the Help Document
  • 2.22  R Packages
  • 2.23  Installing an R Package
  • 2.24  Demo - Install and Load a Package
  • 2.25  Install and Load a Package

Module 3: R Programming

  • 3.1  Introduction
  • 3.2  Objectives
  • 3.3  Operators in R
  • 3.4  Arithmetic Operators
  • 3.5  Demo - Perform Arithmetic Operations
  • 3.6  Use Arithmetic Operations
  • 3.7  Relational Operators
  • 3.8  Demo - Use Relational Operator
  • 3.9  
  • 3.10  Use Relational Operators
  • 3.11  Logical Operators
  • 3.12  Demo - Perform Logical Operations
  • 3.13  Use Logical Operators
  • 3.14  Assignment Operators
  • 3.15  Demo - Use Assignment Operator
  • 3.16  Use Assignment Operator
  • 3.17  Conditional Statements in R
  • 3.18  Demo - Use Conditional Statements
  • 3.19  Use Conditional Statements
  • 3.20  Switch Function
  • 3.21  Demo - Use the Switch Function
  • 3.22  Use Switch Function
  • 3.23  Loops in R
  • 3.24  Break Statement
  • 3.25  Next Statement
  • 3.26  Demo - Use Loops
  • 3.27  Use Loops
  • 3.28  Scan() Function
  • 3.29  Running an R Script
  • 3.30  Running a Batch Script
  • 3.31  R Functions
  • 3.32  Demo - Use R Functions
  • 3.33  Use Commonly Used Functions
  • 3.34  Demo - Use String Function
  • 3.35  Use Commonly-USed String Functions

Module 4: R Data Structure

  • 4.1  Introduction
  • 4.2  Objectives
  • 4.3  Types of Data Structures in R
  • 4.4  Vectors
  • 4.5  Demo - Create a Vector
  • 4.6  Create a Vector
  • 4.7  Scalars
  • 4.8  Colon Operator
  • 4.9  Accessing Vector Elements
  • 4.10  Matrices
  • 4.11  Accessing Matrix Elements
  • 4.12  Demo - Create a Matrix
  • 4.13  Create a Matrix
  • 4.14  Arrays
  • 4.15  Accessing Array Elements
  • 4.16  Demo - Create an Array
  • 4.17  Create an Array
  • 4.18  Data Frames Preview
  • 4.19  Elements of Data Frames
  • 4.20  Demo - Create a Data Frame
  • 4.21  Create a Data Frame
  • 4.22  Factors
  • 4.23  Demo - Create a Factor
  • 4.24  Create a Factor
  • 4.25  List
  • 4.26  Demo - Create a List
  • 4.27  Create a List
  • 4.28  Importing Files in R
  • 4.29  Importing an Excel File
  • 4.30  Importing a Minitab File Preview
  • 4.31  Importing a Table File Preview
  • 4.32  Importing a CSV File
  • 4.33  Demo - Read Data from a File
  • 4.34  Read Data from a File Preview
  • 4.35  Exporting Files fr

Module 5: Apply Functions

  • 5.1  Introduction
  • 5.2  Objectives
  • 5.3  Types of Apply Functions
  • 5.4  Apply() Function
  • 5.5  Apply() Function (contd.) Previe
  • 5.6  Apply() Function (contd.)
  • 5.7  Demo - Use Apply() Function
  • 5.8  Use Apply Function
  • 5.9  Lapply() Function
  • 5.10  Demo - Use Lapply() Function
  • 5.11  Use Lapply Function Previe
  • 5.12  Sapply() Function
  • 5.13  Demo - Use Sapply() Function
  • 5.14  Use Sapply Function
  • 5.15  Tapply() Function
  • 5.16  Tapply() Function (contd.)
  • 5.17  Tapply() Function (contd.) Previe
  • 5.18  Demo - Use Tapply() Function Previe
  • 5.19  Use Tapply Function
  • 5.20  Vapply() Function
  • 5.21  Demo - Use Vapply() Function
  • 5.22  Use Vapply Function
  • 5.23  Mapply() Function
  • 5.24  Mapply() Function (contd.)
  • 5.25  Mapply() Function (contd.)
  • 5.26  Dplyr Package - An Overvi
  • 5.27  Dplyr Package - The Five Verbs
  • 5.28  Installing the Dplyr Package
  • 5.29  Functions of the Dplyr Package Previe
  • 5.30  Functions of the Dplyr Package - Select()
  • 5.31  Demo - Use the Select() Function
  • 5.32  Use the Select Function
  • 5.33  Functions of Dplyr-Package - Filter()
  • 5.34  Demo - Use the Filter() Function
  • 5.35  Use Select Functi
  • 5.36  Functions of Dplyr Package - Arrange()
  • 5.37  Demo - Use the Arrange() Function
  • 5.38  Use Arrange Function
  • 5.39  Functions of Dplyr Package - Mutate() Previe
  • 5.40  Functions of Dply Package - Summarise()
  • 5.41  Functions of Dplyr Package - Summarise() (contd.)
  • 5.42  Demo - Use the Summarise() Function
  • 5.43  Use Summarise Function

Module 6: Data Visualization

  • 6.1  Introduction
  • 6.2  Objectives 00:17
  • 6.3  Graphics in R
  • 6.4  Types of Graphics
  • 6.5  Bar Charts
  • 6.6  Creating Simple Bar Charts
  • 6.7  Editing a Simple Bar Chart
  • 6.8  Demo - Create a Bar Chart
  • 6.9  Create a Bar Cha
  • 6.10  Editing a Simple Bar Chart (contd.)
  • 6.11  Editing a Simple Bar Chart (contd.) 00:26
  • 6.12  Demo - Create a Stacked Bar Plot and Grouped Bar Plot 00:07
  • 6.13  Create a Stacked Bar Plot and Grouped Bar Plot 01:58
  • 6.14  Pie Charts 00:51
  • 6.15  Editing a Pie Chart
  • 6.16  Editing a Pie Chart (contd.) Previe
  • 6.17  Demo - Create a Pie Chart
  • 6.18  Create a Pie Chart
  • 6.19  Histograms Previe
  • 6.20  Creating a Histogram
  • 6.21  Kernel Density Plots
  • 6.22  Creating a Kernel Density Plot
  • 6.23  Demo - Create Histograms and a Density Plot
  • 6.24  Create Histograms and a Density Plot
  • 6.25  Line Charts
  • 6.26  Creating a Line Chart Previe
  • 6.27  Box Plots
  • 6.28  Creating a Box Plot
  • 6.29  Demo - Create Line Graphs and a Box Plot 00:07
  • 6.30  Create Line Graphs and a Box Plot
  • 6.31  Heat Maps Previe
  • 6.32  Creating a Heat Map
  • 6.33  Demo - Create a Heat Map Previe
  • 6.34  Create a Heatmap
  • 6.35  Word Clouds
  • 6.36  Creating a Word Cloud
  • 6.37  Demo - Create a Word Cloud
  • 6.38  Create a Word Cloud
  • 6.39  File Formats for Graphic Outputs
  • 6.40  Saving a Graphic Output as a File
  • 6.41  Saving a Graphic Output as a File (contd.)
  • 6.42  Demo - Save Graphics to a File
  • 6.43  Save Graphics to a File
  • 6.44  Exporting Graphs in RStudio Previe
  • 6.45  Exporting Graphs as PDFs in RStudio Previe
  • 6.46  Demo - Save Graphics Using RStudio
  • 6.47  Save Graphics Using RStudio

Module 7: Introduction to Statistics

  • 7.1  Introduction
  • 7.2  Objectives
  • 7.3  Hypothesi1
  • 7.4  Need of Hypothesis Testing in Businesses
  • 7.5  Null Hypothesis
  • 7.6  Null Hypothesis (contd.)
  • 7.7  Alternate Hypothesis Previe
  • 7.8  Null vs. Alternate Hypothesis
  • 7.9  Chances of Errors in Sampling
  • 7.10  Types of Errors Previe
  • 7.11  Contingency Table
  • 7.12  Decision Making
  • 7.13  Critical Region Previe
  • 7.14  Level of Significance
  • 7.15  Confidence Coefficient
  • 7.16  Bita Risk Previe
  • 7.17  Power of Test
  • 7.18  Factors Affecting the Power of Test
  • 7.19  Types of Statistical Hypothesis Tests
  • 7.20  An Example of Statistical Hypothesis Tests
  • 7.21  An Example of Statistical Hypothesis Tests (contd.)
  • 7.22  An Example of Statistical Hypothesis Tests (contd.)
  • 7.23  An Example of Statistical Hypothesis Tests (contd.)
  • 7.24  Upper Tail Test Previe
  • 7.25  Upper Tail Test (contd.)
  • 7.26  Upper Tail Test (contd.)
  • 7.27  Test Statistic
  • 7.28  Factors Affecting Test Statistic Previe
  • 7.29  Factors Affecting Test Statistic (contd.)
  • 7.30  Factors Affecting Test Statistic (contd.)
  • 7.31  Critical Value Using Normal Probability Table

Module 8: Hypothesis Testing I

  • 8.1  Introduction
  • 8.2  Objectives
  • 8.3  Hypothesi1
  • 8.4  Need of Hypothesis Testing in Businesses
  • 8.5  Null Hypothesis
  • 8.6  Null Hypothesis (contd.)
  • 8.7  Alternate Hypothesis Previe
  • 8.8  Null vs. Alternate Hypothesis
  • 8.9  Chances of Errors in Sampling
  • 8.10  Types of Errors Previe
  • 8.11  Contingency Table
  • 8.12  Decision Making
  • 8.13  Critical Region Previe
  • 8.14  Level of Significance
  • 8.15  Confidence Coefficient
  • 8.16  Bita Risk Previe
  • 8.17  Power of Test
  • 8.18  Factors Affecting the Power of Test
  • 8.19  Types of Statistical Hypothesis Tests
  • 8.20  An Example of Statistical Hypothesis Tests
  • 8.21  An Example of Statistical Hypothesis Tests (contd.)
  • 8.22  An Example of Statistical Hypothesis Tests (contd.)
  • 8.23  An Example of Statistical Hypothesis Tests (contd.)
  • 8.24  Upper Tail Test Previe
  • 8.25  Upper Tail Test (contd.)
  • 8.26  Upper Tail Test (contd.)
  • 8.27  Test Statistic
  • 8.28  Factors Affecting Test Statistic Previe
  • 8.29  Factors Affecting Test Statistic (contd.)
  • 8.30  Factors Affecting Test Statistic (contd.)
  • 8.31  Critical Value Using Normal Probability Table

Module 9: Hypothesis Testing II

  • 9.1  Introduction 00:11
  • 9.2  Objectives 00:15
  • 9.3  Parametric Tests 00:35
  • 9.4  Z-Test Preview00:23
  • 9.5  Z-Test in R - Case Study 00:50
  • 9.6  T-Test Preview00:30
  • 9.7  T-Test in R - Case Study 00:35
  • 9.8  Demo - Use Normal and Student Probability Distribution Functions 00:08
  • 9.9  Use Normal and Student Probability Distribution Functions :32
  • 9.10  Testing Null Hypothesis Preview00:50
  • 9.11  Testing Null Hypothesis 00:08
  • 9.12  Testing Null Hypothesis 00:09
  • 9.13  Testing Null Hypothesis 00:20
  • 9.14  Testing Null Hypothesis 00:14
  • 9.15  Testing Null Hypothesis 01:00
  • 9.16  Objectives of Null Hypothesis Test 00:58
  • 9.17  Three Types of Hypothesis Tests 00:17
  • 9.18  Hypothesis Tests About Population Means 00:42
  • 9.19  Hypothesis Tests About Population Means (contd.) Preview00:50
  • 9.20  Hypothesis Tests About Population Means (contd.) 00:27
  • 9.21  Decision Rules 01:21
  • 9.22  Hypothesis Tests About Population Means - Case Study 1 01:30
  • 9.23  Hypothesis Tests About Population Means - Case Study 2 :21
  • 9.24  Hypothesis Tests About Population Means - Case Study 2 (contd.) 00:22
  • 9.25  Hypothesis Tests About Population Proportions 00:28
  • 9.26  Hypothesis Tests About Population Proportions (contd.) 00:29
  • 9.27  Hypothesis Tests About Population Proportions (contd.) 01:03
  • 9.28  Hypothesis Tests About Population Proportions - Case Study 1 00:22
  • 9.29  Hypothesis Tests About Population Proportions - Case Study 1 (contd.) Preview00:55
  • 9.30  Chi-Square Test 00:28
  • 9.31  Steps of Chi-Square Test 00:38
  • 9.32  Steps of Chi-Square Test (contd.) 00:30
  • 9.33  Important Points of Chi-Square Test (contd.) 00:31
  • 9.34  Degree of Freedom 00:35
  • 9.35  Chi-Square Test for Independence 00:51
  • 9.36  Chi-Square Test for Goodness of Fit Preview00:42
  • 9.37  Chi-Square Test for Independence - Case Study 00:28
  • 9.38  Chi-Squar Test for Independence - Case Study (contd.) 00:26
  • 9.39  Chi-Square Test in R - Case Study 00:38
  • 9.40  Chi-Square Test in R - Case Study (contd.) 00:31
  • 9.41  Demo - Use Chi-Squared Test Statistics 00:10
  • 9.42  Use Chi-Squared Test Statistics 02:35
  • 9.43  Introduction to ANOVA Test 01:03
  • 9.44  One-Way ANOVA Test 01:10
  • 9.45  The F-Distribution and F-Ratio :22
  • 9.46  F-Ratio Test 00:37
  • 9.47  F-Ratio Test in R - Example Preview00:22
  • 9.48  One-Way ANOVA Test - Case Study 00:20
  • 9.49  One-Way ANOVA Test - Case Study (contd.) 00:45
  • 9.50  One-Way ANOVA Test in R - Case Study 00:49
  • 9.51  One-Way ANOVA Test in R - Case Study (contd.) 00:29
  • 9.52  One-Way ANOVA Test in R - Case Study (contd.) Preview00:35
  • 9.53  Demo - Perform ANOVA Preview00:07
  • 9.54  Perform A

Module 10: Regression Analysis

  • 10.1  Introduction
  • 10.2  Objectives
  • 10.3  Introduction to Regression Analysis
  • 10.4  Use of Regression Analysis - Examples
  • 10.5  Use of Regression Analysis - Examples (contd.)
  • 10.6  Types Regression Analysis
  • 10.7  Simple Regression Analysis
  • 10.8  Multiple Regression Models
  • 10.9  Simple Linear Regression Model Preview
  • 10.10  Simple Linear Regression Model Explained Preview
  • 10.11  Demo - Perform Simple Linear Regression
  • 10.12  Perform Simple Linear Regression Previe
  • 10.13  Correlation
  • 10.14  Correlation Between X and Y
  • 10.15  Correlation Between X and Y (contd.)
  • 10.16  Demo - Find Correlation
  • 10.17  Find Correlation
  • 10.18  Method of Least Squares Regression Mod
  • 10.19  Coefficient of Multiple Determination Regression Model
  • 10.20  Standard Error of the Estimate Regression Model
  • 10.21  Dummy Variable Regression Model
  • 10.22  Interaction Regression Model Preview
  • 10.23  Non-Linear Regression
  • 10.24  Non-Linear Regression Models
  • 10.25  Non-Linear Regression Models (contd.)
  • 10.26  Non-Linear Regression Models (contd.)
  • 10.27  Demo - Perform Regression Analysis with Multiple Variables
  • 10.28  Perform Regression Analysis with Multiple Variables
  • 10.29  Non-Linear Models to Linear Models
  • 10.30  Algorithms for Complex Non-Linear Models

Module 11: Classification

  • 11.1  Introduction
  • 11.2  Objectives
  • 11.3  Introduction to Classification
  • 11.4  Examples of Classification Preview
  • 11.5  Classification vs. Prediction
  • 11.6  Classification System
  • 11.7  Classification Process
  • 11.8  Classification Process - Model Construction
  • 11.9  Classification Process - Model Usage in Prediction Preview
  • 11.10  Issues Regarding Classification and Prediction
  • 11.11  Data Preparation Issues
  • 11.12  Evaluating Classification Methods Issues
  • 11.13  Decision Tree
  • 11.14  Decision Tree - Dataset
  • 11.15  Decision Tree - Dataset (contd.)
  • 11.16  Classification Rules of Trees
  • 11.17  Overfitting in Classification
  • 11.18  Tips to Find the Final Tree Si
  • 11.19  Basic Algorithm for a Decision Tree
  • 11.20  Statistical Measure - Information Gain
  • 11.21  Calculating Information Gain - Example
  • 11.22  Calculating Information Gain - Example (contd.) Previe
  • 11.23  Calculating Information Gain for Continuous-Value Attributes
  • 11.24  Enhancing a Basic Tree Previe
  • 11.25  Decision Trees in Data Mining
  • 11.26  Demo - Model a Decision Tree
  • 11.27  Model a Decision Tree
  • 11.28  Naive Bayes Classifier Mod
  • 11.29  Features of Naive Bayes Classifier Model
  • 11.30  Bayesian Theorem
  • 11.31  Bayesian Theorem (contd.) Previe
  • 11.32  Naive Bayes Classifier
  • 11.33  Applying Naive Bayes Classifier - Example
  • 11.34  Applying Naive Bayes Classifier - Example (contd.)
  • 11.35  Naive Bayes Classifier - Advantages and Disadvantages
  • 11.36  Demo - Perform Classification Using the Naive Bayes Metho
  • 11.37   Perform Classification Using the Naive Bayes Method
  • 11.38  Nearest Neighbor Classifiers
  • 11.39  Nearest Neighbor Classifiers (contd.) Previe
  • 11.40  Nearest Neighbor Classifiers (contd.)
  • 11.41  Computing Distance and Determining Class
  • 11.42  Choosing the Value of K
  • 11.43  Scaling Issues in Nearest Neighbor Classification
  • 11.44  Support Vector Machines
  • 11.45  Advantages of Support Vector Machines
  • 11.46  Geometric Margin in SVMs
  • 11.47  Linear SVMs
  • 11.48  Non-Linear SVMs Previe
  • 11.49  Demo - Support a Vector Machine
  • 11.50  Support a Vector Machine

Module 12: Clustering

  • 12.1  Introduction
  • 12.2  Objective
  • 12.3  Introduction to Clustering Preview
  • 12.4  Clustering vs. Classificatio
  • 12.5  Use Cases of Clustering
  • 12.6  Clustering Model
  • 12.7  K-means Cluster
  • 12.8  K-means Clustering Algorithm Preview
  • 12.9  Pseudo Code of K-means
  • 12.10  K-means Clustering Using R
  • 12.11  K-means Clustering - Case Study
  • 12.12  K-means Clustering - Case Study (contd.)
  • 12.13  K-means Clustering - Case Study (contd.)
  • 12.14  Demo - Perform Clustering Using K-means
  • 12.15  Perform Clustering Using Kmeans
  • 12.16  Hierarchical Clustering
  • 12.17  Hierarchical Clustering Algorithms
  • 12.18  Requirements of Hierarchical Clustering Algorith
  • 12.19  Agglomerative Clustering Process
  • 12.20  Hierarchical Clustering - Case Study Previe
  • 12.21  Hierarchical Clustering - Case Study (contd.)
  • 12.22  Hierarchical Clustering - Case Study (contd.)
  • 12.23  Demo - Perform Hierarchical Clustering
  • 12.24  Perform Hierarchical Clustering
  • 12.25  DBSCAN Clustering
  • 12.26  Concepts of DBSCAN
  • 12.27  Concepts of DBSCAN (contd.) Previe
  • 12.28  DBSCAN Clustering Algorithm
  • 12.29  DBSCAN in R
  • 12.30  DBSCAN Clustering - Case Study

Module 13: Association

  • 13.1  Introduction
  • 13.2  Objectives Previe
  • 13.3  Association Rule Mining
  • 13.4  Application Areas of Association Rule Mini
  • 13.5  Parameters of Interesting Relationships
  • 13.6  Association Rules
  • 13.7  Association Rule Strength Measur
  • 13.8  Limitations of Support and Confidence
  • 13.9  Apriori Algorithm
  • 13.10  Apriori Algorithm - Example
  • 13.11  Applying Aprior Algorithm
  • 13.12  Step 1 - Mine All Frequent Item Sets
  • 13.13  Algorithm to Find Frequent Item Set
  • 13.14  Finding Frequent Item Set - Example
  • 13.15  Ordering Items
  • 13.16  Ordering Items (contd.) Previe
  • 13.17  Candidate Generation
  • 13.18  Candidate Generation (contd.)
  • 13.19  Candidate Generation - Example
  • 13.20  Step 2 - Generate Rules from Frequent Item Sets
  • 13.21  Generate Rules from Frequent Item Sets - Example
  • 13.22  Demo - Perform Association Using the Apriori Algorithm Preview00:08
  • 13.23  Perform Association Using the Apriori Algorit
  • 13.24  Demo - Perform Visualization on Associated Rules
  • 13.25  Perform Visualization on Associated Rules
  • 13.26  Problems with Association Mining
Request a detailed syllabus.

Get Answers (Answering their questions)

What kind of learning does ITlearn360.com provide?

ITlearn360.com offers instructor-led online live sessions and classroom-based corporate trainings and bootcamps for various courses and certifications to the learners.

Who are the instructors @ITlearn360.com?

@ITlearn360.com, we have an instructor community of industry professionals who are working in leading organizations and are veterans in their respective fields. These experts belong to various industries and are willing to share their talent with learners like you.

Are classes @ITlearn360.com conducted through online video streaming?

Yes, the classes @ITlearn360.com are conducted through online video streaming where there is two-way communication between users and instructors. The users can speak by using a microphone, chat by sending a message through a chat window and share their screens with an instructor. For better understanding, users also get recorded video of the class.

Sign for next demo class