Course 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.
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.
Course Goals
- Gain a foundational understanding of business analytics and various statistical concepts
- Master the R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and DPLYP functions
- Understand and use the various graphics in R for data visualization
- Understand and use linear, non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules and Apriori algorithm
- Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
Course Topics
Data Science Certification Training – R Programming – 24 hours
Lesson 01 – Introduction to Business Analytics
1.1 Introduction
1.2 Objectives
1.3 Need of Business Analytics
1.4 Business Decisions
1.5 Business Decisions (contd.)
1.6 Introduction to Business Analytics
1.7 Features of Business Analytics
1.8 Types of Business Analytics
1.9 Descriptive Analytics
1.10 Predictive Analytics
1.11 Predictive Analytics (contd.)
1.12 Prescriptive Analytics
1.13 Prescriptive Analytics (contd.)
1.14 Supply Chain Analytics
1.15 Health Care Analytics
1.16 Marketing Analytics
1.17 Human Resource Analytics
1.18 Web Analytics
1.19 Application of Business Analytics – Walmart Case Study
1.20 Application of Business Analytics – Walmart Case Study (contd.)
1.21 Application of Business Analytics – Walmart Case Study (contd.)
1.22 Application of Business Analytics – Signet Bank Case Study
1.23 Application of Business Analytics – Signet Bank Case Study (contd.)
1.24 Application of Business Analytics – Signet Bank Case Study (contd.)
1.25 Business Decisions
1.26 Business Intelligence (BI)
1.27 Data Science
1.28 Importance of Data Science
1.29 Data Science as a Strategic Asset
1.30 Big Data
1.31 Analytical Tools
1.32 Quiz
1.33 Summary
1.34 Summary (contd.)
1.35 Conclusion
Lesson 02 – 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 Installing R on Windows from CRAN Website (contd.)
2.11 Installing R on Windows from CRAN Website (contd.)
2.12 Demo – Install R
2.13 Install R
2.14 IDEs for R
2.15 Installing RStudio on Various Operating Systems
2.16 Demo – Install RStudio
2.17 Install RStudio
2.18 Steps in R Initiation
2.19 Benefits of R Workspace
2.20 Setting the Workplace
2.21 Functions and Help in R
2.22 Demo – Access the Help Document
2.23 Access the Help Document
2.24 R Packages
2.25 Installing an R Package
2.26 Demo – Install and Load a Package
2.27 Install and Load a Package
2.28 Quiz
2.29 Summary
2.30 Summary (contd.)
2.31 Conclusion
Lesson 03 – 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 Operators
3.9 Use Relational Operators
3.10 Logical Operators
3.11 Demo – Perform Logical Operations
3.12 Use Logical Operators
3.13 Assignment Operators
3.14 Demo – Use Assignment Operator
3.15 Use Assignment Operator
3.16 Conditional Statements in R
3.17 Conditional Statements in R (contd.)
3.18 Conditional Statements in R (contd.)
3.19 Ifelse() Function
3.20 Demo – Use Conditional Statements
3.21 Use Conditional Statements
3.22 Switch Function
3.23 Demo – Use the Switch Function
3.24 Use Switch Function
3.25 Loops in R
3.26 Loops in R (contd.)
3.27 Loops in R (contd.)
3.28 Loops in R (contd.)
3.29 Break Statement
3.30 Next Statement
3.31 Demo – Use Loops
3.32 Use Loops
3.33 Scan() Function
3.34 Running an R Script
3.35 Running a Batch Script
3.36 R Functions
3.37 R Functions (contd.)
3.38 Demo – Use R Functions
3.39 Use Commonly Used Functions
3.40 Demo – Use String Functions
3.41 Use Commonly-USed String Functions
3.42 Quiz
3.43 Summary
3.44 Conclusion
Lesson 04 – 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 Matrices (contd.)
4.12 Accessing Matrix Elements
4.13 Demo – Create a Matrix
4.14 Create a Matrix
4.15 Arrays
4.16 Accessing Array Elements
4.17 Demo – Create an Array
4.18 Create an Array
4.19 Data Frames
4.20 Elements of Data Frames
4.21 Demo – Create a Data Frame
4.22 Create a Data Frame
4.23 Factors
4.24 Demo – Create a Factor
4.25 Create a Factor
4.26 Lists
4.27 Demo – Create a List
4.28 Create a List
4.29 Importing Files in R
4.30 Importing an Excel File
4.31 Importing a Minitab File
4.32 Importing a Table File
4.33 Importing a CSV File
4.34 Demo – Read Data from a File
4.35 Read Data from a File
4.36 Exporting Files from R
4.37 Exporting Files from R (contd.)
4.38 Exporting Files from R (contd.)
4.39 Exporting Files from R (contd.)
4.40 Quiz
4.41 Summary
4.42 Conclusion
Lesson 05 – Apply Functions
5.1 Introduction
5.2 Objectives
5.3 Types of Apply Functions
5.4 Apply() Function
5.5 Apply() Function (contd.)
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
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.)
5.18 Demo – Use Tapply() Function
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 Overview
5.27 Dplyr Package – The Five Verbs
5.28 Installing the Dplyr Package
5.29 Functions of the Dplyr Package
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() Function
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()
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
5.44 Quiz
5.45 Summary
5.46 Conclusion
Lesson 06 – Data Visualization
6.1 Introduction
6.2 Objectives
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 Chart
6.10 Editing a Simple Bar Chart (contd.)
6.11 Editing a Simple Bar Chart (contd.)
6.12 Demo – Create a Stacked Bar Plot and Grouped Bar Plot
6.13 Create a Stacked Bar Plot and Grouped Bar Plot
6.14 Pie Charts
6.15 Editing a Pie Chart
6.16 Editing a Pie Chart (contd.)
6.17 Demo – Create a Pie Chart
6.18 Create a Pie Chart
6.19 Histograms
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
6.27 Box Plots
6.28 Creating a Box Plot
6.29 Demo – Create Line Graphs and a Box Plot
6.30 Create Line Graphs and a Box Plot
6.31 Heat Maps
6.32 Creating a Heat Map
6.33 Demo – Create a Heat Map
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
6.45 Exporting Graphs as PDFs in RStudio
6.46 Demo – Save Graphics Using RStudio
6.47 Save Graphics Using RStudio
6.48 Quiz
6.49 Summary
6.50 Conclusion
Lesson 07 – Introduction to Statistics
7.1 Introduction
7.2 Objectives
7.3 Basics of Statistics
7.4 Types of Data
7.5 Qualitative vs. Quantitative Analysis
7.6 Types of Measurements in Order
7.7 Nominal Measurement
7.8 Ordinal Measurement
7.9 Interval Measurement
7.10 Ratio Measurement
7.11 Statistical Investigation
7.12 Statistical Investigation Steps
7.13 Normal Distribution
7.14 Normal Distribution (contd.)
7.15 Example of Normal Distribution
7.16 Importance of Normal Distribution in Statistics
7.17 Use of the Symmetry Property of Normal Distribution
7.18 Standard Normal Distribution
7.19 Demo – Use Probability Distribution Functions
7.20 Use Probability Distribution Functions
7.21 Distance Measures
7.22 Distance Measures – A Comparison
7.23 Euclidean Distance
7.24 Example of Euclidean Distance
7.25 Manhattan Distance
7.26 Minkowski Distance
7.27 Mahalanobis Distance
7.28 Cosine Similarity
7.29 Correlation
7.30 Correlation Measures Explained
7.31 Pearson Product Moment Correlation (PPMC)
7.32 Pearson Product Moment Correlation (PPMC) (contd.)
7.33 Pearson Correlation – Case Study
7.34 Dist() Function in R
7.35 Demo – Perform the Distance Matrix Computations
7.36 Perform the Distance Matrix Computations
7.37 Quiz
7.38 Summary
7.39 Summary (contd.)
7.40 Conclusion
Lesson 08 – Hypothesis Testing I
8.1 Introduction
8.2 Objectives
8.3 Hypothesis
8.4 Need of Hypothesis Testing in Businesses
8.5 Null Hypothesis
8.6 Null Hypothesis (contd.)
8.7 Alternate Hypothesis
8.8 Null vs. Alternate Hypothesis
8.9 Chances of Errors in Sampling
8.10 Types of Errors
8.11 Contingency Table
8.12 Decision Making
8.13 Critical Region
8.14 Level of Significance
8.15 Confidence Coefficient
8.16 Beta Risk
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
8.25 Upper Tail Test (contd.)
8.26 Upper Tail Test (contd.)
8.27 Test Statistic
8.28 Factors Affecting Test Statistic
8.29 Factors Affecting Test Statistic (contd.)
8.30 Factors Affecting Test Statistic (contd.)
8.31 Critical Value Using Normal Probability Table
8.32 Quiz
8.33 Summary
8.34 Conclusion
Lesson 09 – Hypothesis Testing II
9.1 Introduction
9.2 Objectives
9.3 Parametric Tests
9.4 Z-Test
9.5 Z-Test in R – Case Study
9.6 T-Test
9.7 T-Test in R – Case Study
9.8 Demo – Use Normal and Student Probability Distribution Functions
9.9 Use Normal and Student Probability Distribution Functions
9.10 Testing Null Hypothesis
9.11 Testing Null Hypothesis
9.12 Testing Null Hypothesis
9.13 Testing Null Hypothesis
9.14 Testing Null Hypothesis
9.15 Testing Null Hypothesis
9.16 Objectives of Null Hypothesis Test
9.17 Three Types of Hypothesis Tests
9.18 Hypothesis Tests About Population Means
9.19 Hypothesis Tests About Population Means (contd.)
9.20 Hypothesis Tests About Population Means (contd.)
9.21 Decision Rules
9.22 Hypothesis Tests About Population Means – Case Study 1
9.23 Hypothesis Tests About Population Means – Case Study 2
9.24 Hypothesis Tests About Population Means – Case Study 2 (contd.)
9.25 Hypothesis Tests About Population Proportions
9.26 Hypothesis Tests About Population Proportions (contd.)
9.27 Hypothesis Tests About Population Proportions (contd.)
9.28 Hypothesis Tests About Population Proportions – Case Study 1
9.29 Hypothesis Tests About Population Proportions – Case Study 1 (contd.)
9.30 Chi-Square Test
9.31 Steps of Chi-Square Test
9.32 Steps of Chi-Square Test (contd.)
9.33 Important Points of Chi-Square Test (contd.)
9.34 Degree of Freedom
9.35 Chi-Square Test for Independence
9.36 Chi-Square Test for Goodness of Fit
9.37 Chi-Square Test for Independence – Case Study
9.38 Chi-Squar Test for Independence – Case Study (contd.)
9.39 Chi-Square Test in R – Case Study
9.40 Chi-Square Test in R – Case Study (contd.)
9.41 Demo – Use Chi-Squared Test Statistics
9.42 Use Chi-Squared Test Statistics
9.43 Introduction to ANOVA Test
9.44 One-Way ANOVA Test
9.45 The F-Distribution and F-Ratio
9.46 F-Ratio Test
9.47 F-Ratio Test in R – Example
9.48 One-Way ANOVA Test – Case Study
9.49 One-Way ANOVA Test – Case Study (contd.)
9.50 One-Way ANOVA Test in R – Case Study
9.51 One-Way ANOVA Test in R – Case Study (contd.)
9.52 One-Way ANOVA Test in R – Case Study (contd.)
9.53 Demo – Perform ANOVA
9.54 Perform ANOVA
9.55 Quiz
9.56 Summary
9.57 Conclusion
Lesson 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
10.10 Simple Linear Regression Model Explained
10.11 Demo – Perform Simple Linear Regression
10.12 Perform Simple Linear Regression
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 Model
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
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
10.31 Quiz
10.32 Summary
10.33 Summary (contd.)
10.34 Conclusion
Lesson 11 – Classification
11.1 Introduction
11.2 Objectives
11.3 Introduction to Classification
11.4 Examples of Classification
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
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 Size
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.)
11.23 Calculating Information Gain for Continuous-Value Attributes
11.24 Enhancing a Basic Tree
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 Model
11.29 Features of Naive Bayes Classifier Model
11.30 Bayesian Theorem
11.31 Bayesian Theorem (contd.)
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 Method
11.37 Perform Classification Using the Naive Bayes Method
11.38 Nearest Neighbor Classifiers
11.39 Nearest Neighbor Classifiers (contd.)
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
11.49 Demo – Support a Vector Machine
11.50 Support a Vector Machine
11.51 Quiz
11.52 Summary
11.53 Conclusion
Lesson 12 – Clustering
12.1 Introduction
12.2 Objectives
12.3 Introduction to Clustering
12.4 Clustering vs. Classification
12.5 Use Cases of Clustering
12.6 Clustering Models
12.7 K-means Clustering
12.8 K-means Clustering Algorithm
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 Algorithms
12.19 Agglomerative Clustering Process
12.20 Hierarchical Clustering – Case Study
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.)
12.28 DBSCAN Clustering Algorithm
12.29 DBSCAN in R
12.30 DBSCAN Clustering – Case Study
12.31 DBSCAN Clustering – Case Study (contd.)
12.32 DBSCAN Clustering – Case Study (contd.)
12.33 Quiz
12.34 Summary
12.35 Conclusion
Lesson 13 – Association
13.1 Introduction
13.2 Objectives
13.3 Association Rule Mining
13.4 Application Areas of Association Rule Mining
13.5 Parameters of Interesting Relationships
13.6 Association Rules
13.7 Association Rule Strength Measures
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.)
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
13.23 Perform Association Using the Apriori Algorithm
13.24 Demo – Perform Visualization on Associated Rules
13.25 Perform Visualization on Associated Rules
13.26 Problems with Association Mining
13.27 Quiz
13.28 Summary
13.29 Conclusion
13.30 Thank You