Data Science Certification Training - R Programming

To Become the data analytics cognoscenti using the R programming language in this data science certification training course. You’ll dab hand at data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. through the journey of this data science course, you’ll get hands-on practice on R Cloud Lab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

Key features:
  • 32 hours of instructor-led training
  • 24 hours of self-paced video
  • 8 real-life industry projects in retail, insurance, finance, airlines and other domains
  • Hands-on practice with R Cloud Lab
  • Includes statistical concepts such as regression and cluster analysis
  • Includes “Business Analytics with Excel” course

Course description:

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 - Wal-Mart Case Study
  • 1.20 Application of Business Analytics - Wal-Mart Case Study (contd.)
  • 1.21 Application of Business Analytics - Wal-Mart 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
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
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
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
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
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
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
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 Bita 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
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 (co
  • 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
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.)
  • 0.26 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
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
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
  • 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
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
What are the course objectives?

The Data Science Certification with R has been streamlined to impart in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies and includes R Cloud Lab for practice.

Mastering R language: The data science course provides an aggregate 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 data science training course is inclusive of statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You get an opportunity to learn hypothesis testing. As a part of the data science with R training course, you will be going through with real-life projects using Cloud Lab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. R Cloud Lab has been provided to ensure you get practical, hands-on experience with your new skills. Four additional projects are also available for further practice.

What skills will you learn?

This data science training course will enable you to:

  • Gain a foundational understanding of business analytics
  • Install R, R-studio, and workspace setup, and learn about the various R packages
  • Master 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/exportdata in R
  • Define, understand and use the various application functions and DPLYR functions
  • Understand and use the various graphics in R for data visualization
  • Gain a basic understanding of various statistical concepts
  • Understand and use hypothesis testing method to drive business decisions
  • 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
Who should take this course?

This course is on the march for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We give someone to steer the Data Science training particularly for the following professionals:

  • 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
Prerequisites:
  • There are no prerequisites for this data science online training course. If you are new in the field of data science, this is the best course to start with.
What is CloudLab?

CloudLab is a cloud-based R lab presented with this data science course to ensure hassle-free execution of the project work included. With CloudLab, you need not to install and maintain R on a virtual machine. Instead, you’ll be able to access a preconfigured environment on CloudLab via your browser. You can access CloudLab from the 4CLearn.com LMS (Learning Management System) for the duration of the course.

Why Should I Learn Data Science with R from 4CLearn.com?
  • This course is par excellent for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will cultivate a 360-degree overview of business analytics and R by edging concepts like data exploration, data visualization, predictive analytics, etc
  • According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
  • Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
  • Randstad reports that pay hikes in the analytics industry are 50% higher than IT
What projects are included in this course?

The data science certification course is inclusive of eight real-life, industry-based projects on R CloudLab. Successful evaluation of one of the following four projects is a term of the certification eligibility criteria.

Project1:

Healthcare:Predictive analytics can be used in healthcare to mediate hospital readmissions. In healthcare and other industries, predictors are most useful when they can be transferred into action. But historical and real-time data alone are worthless without intervention. More importantly, to judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.

Project2:

Insurance:Use of predictive analytics has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling, compared to 69% of companies with less than that amount of premium.

Project3:

Retail: Analytics is used in optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them insights into regular occurrences in the retail sector.

Project4:

Internet: Internet analytics is the collection, modeling, and analysis of user data in large-scale online services such as social networking, e-commerce, search, and advertisement. In this class, we explore a number of key functions of such online services that have become ubiquitous over the last couple of years. Specifically, we look at social and information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing and online ad auctions.

Four additional projects have been provided to help learners master the R language.

Project5:

Music Industry: Details of listener preferences are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.

Project6:

Finance: You’ll predict whether someone will default or not default on a loan based on user demographic data. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.

Project7:

Unemployment: Analyze the monthly, seasonally-adjusted unemployment rates for the U.S. employment data of all 50 states, covering the period from January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.

Project8:

Airline: Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with a number of variables including airports and flight times.

Exam & amp; certification:
How do I get certified?

To become a Certified Data Scientist with R, you must conform with the following criteria:

  • Complete any one project out of the four provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer
  • Score a minimum grade of 60% in any one of the two simulation tests
  • Complete 85% of the data science course
  • Note: When you have completed the data science certification course, you will receive a three-month experience certificate for implementing the projects using R.
  • It is mandatory that you fulfill both the criteria (completion of any one project and passing the online exam with minimum score of 80%) to become a certified data scientist.
What do I need to do to unlock my 4CLearn.com certificate?
Online Classroom:
  • Attend one complete batch.
  • Complete 1 project and 1 simulation test with a minimum score of 60%.
Online Self-Learning:
  • Complete 85% of the course.
  • Complete 1 project and 1 simulation test with a minimum score of 60%.

FAQs?


1. What are the System Requirements?

You will need to download R from the CRAN website and RStudio for your operating system. These are both open source and the installation guidelines are presented in the data science course.

2. Who are our instructors and how are they selected?

we are blessed to have dedicated, experienced and expertise subject matter experts and industry professionals who have a successful track record of more than 12 years. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us.

3. What are training formats used for this course?

We offer this data science with R certification course in the following formats:

Live Virtual Classroom or Online Classroom: With online classroom training, you have an option to attend the course remotely from your desktop via video conferencing. This format condenses productivity challenges and shrinks your time spent on work or home.

Online Self-Learning: In this mode, you’ll receive lecture videos that you can view at your own pace.

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