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Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.

Course Advisor

Ronald van Loon
Top 10 Big Data & Data Science Influencer, Director – Adversitement

Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.

  Accredited by 

Key Features 

32 hours of instructor-led training (for Live Virtual Classroom)

24 hours of self-paced video

8 real-life industry projects in retail, insurance, finance, airlines and other domains

Hands-on practice with R CloudLab

Includes statistical concepts such as regression and cluster analysis

Includes “Business Analytics with Excel” course”

Mode of learning

Online self paced learning:

  • 180 days of access to high-quality, self-paced learning content designed by industry experts



Live virtual classroom:

  • 90 days of access to 8+ instructor-led online training classes
  • 180 days of access to high-quality, self-paced learning content designed by industry experts
  • Flexible weekend class weekly


USD 799


The Data Science Certification with R has been designed to give you 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 CloudLab for practice.

Mastering R language: The data science 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 data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

As a part of the data science with R training 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 you get practical, hands-on experience with your new skills. Four additional projects are also available for further practice.

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/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
  • 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

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this 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.

CloudLab is a cloud-based R lab offered with this data science course to ensure hassle-free execution of the project work included. With CloudLab, you do not need 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 Simplilearn LMS (Learning Management System) for the duration of the course.

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

Project 1:
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.

Project 2:
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.

Project 3:
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.

Project 4:
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.

Project 5:
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.

Project 6:
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.

Project 7:
Unemployment: Analyze the monthly, seasonally-adjusted unemployment rates for 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.

Project 8:
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 data set helps with a number of variables including airports and flight times.

Course Curriculum

No curriculum found !


Data Science with R

1.1 Introduction00:10

1.2 Objectives00:15

1.3 Need of Business Analytics01:28

1.4 Business Decisions00:22

1.5 Business Decisions (contd.)00:07

1.6 Introduction to Business Analytics01:09

1.7 Features of Business Analytics01:20

1.8 Types of Business Analytics00:19

1.9 Descriptive Analytics00:55

1.10 Predictive Analytics01:09

1.11 Predictive Analytics (contd.)00:41

1.12 Prescriptive Analytics01:13

1.13 Prescriptive Analytics (contd.)00:24

1.14 Supply Chain Analytics00:56

1.15 Health Care Analytics00:40

1.16 Marketing Analytics00:44

1.17 Human Resource Analytics00:36

1.18 Web Analytics00:46

1.19 Application of Business Analytics – Wal-Mart Case Study00:16

1.20 Application of Business Analytics – Wal-Mart Case Study (contd.)00:29

1.21 Application of Business Analytics – Wal-Mart Case Study (contd.)00:35

1.22 Application of Business Analytics – Signet Bank Case Study00:29

1.23 Application of Business Analytics – Signet Bank Case Study (contd.)00:46

1.24 Application of Business Analytics – Signet Bank Case Study (contd.)00:44

1.25 Business Decisions00:37

1.26 Business Intelligence (BI)01:09

1.27 Data Science00:33

1.28 Importance of Data Science00:35

1.29 Data Science as a Strategic Asset00:25

1.30 Big Data00:39

1.31 Analytical Tools00:16

1.32 Quiz

1.33 Summary00:52

1.34 Summary (contd.)00:39

1.35 Conclusion00:11

2.1 Introduction00:11

2.2 Objectives00:21

2.3 An Introduction to R00:56

2.4 Comprehensive R Archive Network (CRAN)00:39

2.5 Cons of R01:00

2.6 Companies Using R01:16

2.7 Understanding R00:55

2.8 Installing R on Various Operating Systems00:09

2.9 Installing R on Windows from CRAN Website00:15

2.10 Installing R on Windows from CRAN Website (contd.)00:20

2.11 Installing R on Windows from CRAN Website (contd.)00:09

2.12 Demo – Install R00:06

2.13 Install R01:02

2.14 IDEs for R00:50

2.15 Installing RStudio on Various Operating Systems00:34

2.16 Demo – Install RStudio00:06

2.17 Install RStudio00:51

2.18 Steps in R Initiation00:20

2.19 Benefits of R Workspace00:40

2.20 Setting the Workplace00:08

2.21 Functions and Help in R00:28

2.22 Demo – Access the Help Document00:05

2.23 Access the Help Document01:11

2.24 R Packages00:48

2.25 Installing an R Package00:10

2.26 Demo – Install and Load a Package00:05

2.27 Install and Load a Package00:56

2.28 Quiz

2.29 Summary00:33

2.30 Summary (contd.)00:21

2.31 Conclusion00:11

3.1 Introduction00:10

3.2 Objectives00:20

3.3 Operators in R00:15

3.4 Arithmetic Operators00:21

3.5 Demo – Perform Arithmetic Operations00:05

3.6 Use Arithmetic Operations02:00

3.7 Relational Operators00:16

3.8 Demo – Use Relational Operators00:05

3.9 Use Relational Operators01:00

3.10 Logical Operators00:41

3.11 Demo – Perform Logical Operations00:05

3.12 Use Logical Operators01:22

3.13 Assignment Operators00:13

3.14 Demo – Use Assignment Operator00:05

3.15 Use Assignment Operator00:32

3.16 Conditional Statements in R00:24

3.17 Conditional Statements in R (contd.)00:34

3.18 Conditional Statements in R (contd.)00:32

3.19 Ifelse() Function00:18

3.20 Demo – Use Conditional Statements00:06

3.21 Use Conditional Statements01:44

3.22 Switch Function00:45

3.23 Demo – Use the Switch Function00:05

3.24 Use Switch Function01:39

3.25 Loops in R00:14

3.26 Loops in R (contd.)00:33

3.27 Loops in R (contd.)00:18

3.28 Loops in R (contd.)00:31

3.29 Break Statement00:38

3.30 Next Statement00:35

3.31 Demo – Use Loops00:05

3.32 Use Loops02:37

3.33 Scan() Function01:04

3.34 Running an R Script00:40

3.35 Running a Batch Script00:20

3.36 R Functions00:33

3.37 R Functions (contd.)00:05

3.38 Demo – Use R Functions00:06

3.39 Use Commonly Used Functions01:37

3.40 Demo – Use String Functions00:07

3.41 Use Commonly-USed String Functions00:53

3.42 Quiz

3.43 Summary00:39

3.44 Conclusion00:11

4.1 Introduction00:10

4.2 Objectives00:16

4.3 Types of Data Structures in R00:41

4.4 Vectors00:47

4.5 Demo – Create a Vector00:05

4.6 Create a Vector01:24

4.7 Scalars00:12

4.8 Colon Operator00:15

4.9 Accessing Vector Elements00:44

4.10 Matrices00:35

4.11 Matrices (contd.)00:18

4.12 Accessing Matrix Elements00:23

4.13 Demo – Create a Matrix00:05

4.14 Create a Matrix01:45

4.15 Arrays00:33

4.16 Accessing Array Elements00:14

4.17 Demo – Create an Array00:05

4.18 Create an Array01:31

4.19 Data Frames00:57

4.20 Elements of Data Frames00:13

4.21 Demo – Create a Data Frame00:05

4.22 Create a Data Frame01:54

4.23 Factors00:41

4.24 Demo – Create a Factor00:05

4.25 Create a Factor01:49

4.26 Lists00:20

4.27 Demo – Create a List00:05

4.28 Create a List01:14

4.29 Importing Files in R00:22

4.30 Importing an Excel File00:53

4.31 Importing a Minitab File00:20

4.32 Importing a Table File00:29

4.33 Importing a CSV File00:43

4.34 Demo – Read Data from a File00:05

4.35 Read Data from a File03:50

4.36 Exporting Files from R00:33

4.37 Exporting Files from R (contd.)00:37

4.38 Exporting Files from R (contd.)00:17

4.39 Exporting Files from R (contd.)00:38

4.40 Quiz

4.41 Summary00:27

4.42 Conclusion00:10

5.1 Introduction00:13

5.2 Objectives00:15

5.3 Types of Apply Functions00:31

5.4 Apply() Function00:13

5.5 Apply() Function (contd.)00:57

5.6 Apply() Function (contd.)00:31

5.7 Demo – Use Apply() Function00:05

5.8 Use Apply Function01:10

5.9 Lapply() Function01:05

5.10 Demo – Use Lapply() Function00:05

5.11 Use Lapply Function00:54

5.12 Sapply() Function00:56

5.13 Demo – Use Sapply() Function00:05

5.14 Use Sapply Function01:10

5.15 Tapply() Function00:28

5.16 Tapply() Function (contd.)00:23

5.17 Tapply() Function (contd.)00:19

5.18 Demo – Use Tapply() Function00:05

5.19 Use Tapply Function01:22

5.20 Vapply() Function00:47

5.21 Demo – Use Vapply() Function00:05

5.22 Use Vapply Function01:57

5.23 Mapply() Function00:21

5.24 Mapply() Function (contd.)00:16

5.25 Mapply() Function (contd.)00:34

5.26 Dplyr Package – An Overview01:08

5.27 Dplyr Package – The Five Verbs00:51

5.28 Installing the Dplyr Package00:15

5.29 Functions of the Dplyr Package00:20

5.30 Functions of the Dplyr Package – Select()00:30

5.31 Demo – Use the Select() Function00:06

5.32 Use the Select Function01:35

5.33 Functions of Dplyr-Package – Filter()00:59

5.34 Demo – Use the Filter() Function00:05

5.35 Use Select Function01:14

5.36 Functions of Dplyr Package – Arrange()00:10

5.37 Demo – Use the Arrange() Function00:06

5.38 Use Arrange Function01:29

5.39 Functions of Dplyr Package – Mutate()00:21

5.40 Functions of Dply Package – Summarise()00:53

5.41 Functions of Dplyr Package – Summarise() (contd.)00:40

5.42 Demo – Use the Summarise() Function00:06

5.43 Use Summarise Function01:42

5.44 Quiz

5.45 Summary00:33

5.46 Conclusion00:11

6.1 Introduction00:11

6.2 Objectives00:17

6.3 Graphics in R00:38

6.4 Types of Graphics00:25

6.5 Bar Charts00:34

6.6 Creating Simple Bar Charts00:33

6.7 Editing a Simple Bar Chart00:34

6.8 Demo – Create a Bar Chart00:06

6.9 Create a Bar Chart01:50

6.10 Editing a Simple Bar Chart (contd.)00:39

6.11 Editing a Simple Bar Chart (contd.)00:26

6.12 Demo – Create a Stacked Bar Plot and Grouped Bar Plot00:07

6.13 Create a Stacked Bar Plot and Grouped Bar Plot01:58

6.14 Pie Charts00:51

6.15 Editing a Pie Chart00:27

6.16 Editing a Pie Chart (contd.)00:28

6.17 Demo – Create a Pie Chart00:05

6.18 Create a Pie Chart03:01

6.19 Histograms00:53

6.20 Creating a Histogram00:37

6.21 Kernel Density Plots00:19

6.22 Creating a Kernel Density Plot00:29

6.23 Demo – Create Histograms and a Density Plot00:07

6.24 Create Histograms and a Density Plot02:23

6.25 Line Charts00:30

6.26 Creating a Line Chart00:21

6.27 Box Plots00:47

6.28 Creating a Box Plot00:53

6.29 Demo – Create Line Graphs and a Box Plot00:07

6.30 Create Line Graphs and a Box Plot01:59

6.31 Heat Maps00:48

6.32 Creating a Heat Map00:28

6.33 Demo – Create a Heat Map00:06

6.34 Create a Heatmap01:10

6.35 Word Clouds00:28

6.36 Creating a Word Cloud00:52

6.37 Demo – Create a Word Cloud00:06

6.38 Create a Word Cloud01:23

6.39 File Formats for Graphic Outputs00:51

6.40 Saving a Graphic Output as a File01:02

6.41 Saving a Graphic Output as a File (contd.)00:43

6.42 Demo – Save Graphics to a File00:06

6.43 Save Graphics to a File00:49

6.44 Exporting Graphs in RStudio00:27

6.45 Exporting Graphs as PDFs in RStudio00:17

6.46 Demo – Save Graphics Using RStudio00:06

6.47 Save Graphics Using RStudio00:53

6.48 Quiz

6.49 Summary00:27

6.50 Conclusion00:11

7.1 Introduction00:10

7.2 Objectives00:21

7.3 Basics of Statistics02:03

7.4 Types of Data01:20

7.5 Qualitative vs. Quantitative Analysis00:52

7.6 Types of Measurements in Order00:35

7.7 Nominal Measurement00:46

7.8 Ordinal Measurement00:43

7.9 Interval Measurement00:49

7.10 Ratio Measurement00:59

7.11 Statistical Investigation00:13

7.12 Statistical Investigation Steps01:03

7.13 Normal Distribution00:58

7.14 Normal Distribution (contd.)00:36

7.15 Example of Normal Distribution00:08

7.16 Importance of Normal Distribution in Statistics00:34

7.17 Use of the Symmetry Property of Normal Distribution00:52

7.18 Standard Normal Distribution00:33

7.19 Demo – Use Probability Distribution Functions00:07

7.20 Use Probability Distribution Functions06:52

7.21 Distance Measures00:42

7.22 Distance Measures – A Comparison00:26

7.23 Euclidean Distance00:24

7.24 Example of Euclidean Distance00:37

7.25 Manhattan Distance00:31

7.26 Minkowski Distance00:15

7.27 Mahalanobis Distance00:27

7.28 Cosine Similarity00:26

7.29 Correlation00:43

7.30 Correlation Measures Explained01:10

7.31 Pearson Product Moment Correlation (PPMC)00:41

7.32 Pearson Product Moment Correlation (PPMC) (contd.)00:35

7.33 Pearson Correlation – Case Study00:35

7.34 Dist() Function in R00:40

7.35 Demo – Perform the Distance Matrix Computations00:08

7.36 Perform the Distance Matrix Computations03:44

7.37 Quiz

7.38 Summary00:35

7.39 Summary (contd.)00:35

7.40 Conclusion00:11

8.1 Introduction00:11

8.2 Objectives00:22

8.3 Hypothesis02:01

8.4 Need of Hypothesis Testing in Businesses00:52

8.5 Null Hypothesis00:26

8.6 Null Hypothesis (contd.)00:34

8.7 Alternate Hypothesis00:37

8.8 Null vs. Alternate Hypothesis00:33

8.9 Chances of Errors in Sampling00:30

8.10 Types of Errors00:57

8.11 Contingency Table01:15

8.12 Decision Making00:24

8.13 Critical Region00:42

8.14 Level of Significance00:51

8.15 Confidence Coefficient00:49

8.16 Bita Risk00:26

8.17 Power of Test00:28

8.18 Factors Affecting the Power of Test00:23

8.19 Types of Statistical Hypothesis Tests01:05

8.20 An Example of Statistical Hypothesis Tests00:31

8.21 An Example of Statistical Hypothesis Tests (contd.)00:17

8.22 An Example of Statistical Hypothesis Tests (contd.)00:19

8.23 An Example of Statistical Hypothesis Tests (contd.)00:23

8.24 Upper Tail Test00:30

8.25 Upper Tail Test (contd.)00:27

8.26 Upper Tail Test (contd.)00:19

8.27 Test Statistic00:47

8.28 Factors Affecting Test Statistic00:12

8.29 Factors Affecting Test Statistic (contd.)00:39

8.30 Factors Affecting Test Statistic (contd.)00:09

8.31 Critical Value Using Normal Probability Table00:17

8.32 Quiz

8.33 Summary01:02

8.34 Conclusion00:11

9.1 Introduction00:11

9.2 Objectives00:15

9.3 Parametric Tests00:35

9.4 Z-Test00:23

9.5 Z-Test in R – Case Study00:50

9.6 T-Test00:30

9.7 T-Test in R – Case Study00:35

9.8 Demo – Use Normal and Student Probability Distribution Functions00:08

9.9 Use Normal and Student Probability Distribution Functions01:32

9.10 Testing Null Hypothesis00:50

9.11 Testing Null Hypothesis00:08

9.12 Testing Null Hypothesis00:09

9.13 Testing Null Hypothesis00:20

9.14 Testing Null Hypothesis00:14

9.15 Testing Null Hypothesis01:00

9.16 Objectives of Null Hypothesis Test00:58

9.17 Three Types of Hypothesis Tests00:17

9.18 Hypothesis Tests About Population Means00:42

9.19 Hypothesis Tests About Population Means (contd.)00:50

9.20 Hypothesis Tests About Population Means (contd.)00:27

9.21 Decision Rules01:21

9.22 Hypothesis Tests About Population Means – Case Study 101:30

9.23 Hypothesis Tests About Population Means – Case Study 201:21

9.24 Hypothesis Tests About Population Means – Case Study 2 (contd.)00:22

9.25 Hypothesis Tests About Population Proportions00:28

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 100:22

9.29 Hypothesis Tests About Population Proportions – Case Study 1 (contd.)00:55

9.30 Chi-Square Test00:28

9.31 Steps of Chi-Square Test00:38

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 Freedom00:35

9.35 Chi-Square Test for Independence00:51

9.36 Chi-Square Test for Goodness of Fit00:42

9.37 Chi-Square Test for Independence – Case Study00:28

9.38 Chi-Squar Test for Independence – Case Study (contd.)00:26

9.39 Chi-Square Test in R – Case Study00:38

9.40 Chi-Square Test in R – Case Study (contd.)00:31

9.41 Demo – Use Chi-Squared Test Statistics00:10

9.42 Use Chi-Squared Test Statistics02:35

9.43 Introduction to ANOVA Test01:03

9.44 One-Way ANOVA Test01:10

9.45 The F-Distribution and F-Ratio01:22

9.46 F-Ratio Test00:37

9.47 F-Ratio Test in R – Example00:22

9.48 One-Way ANOVA Test – Case Study00:20

9.49 One-Way ANOVA Test – Case Study (contd.)00:45

9.50 One-Way ANOVA Test in R – Case Study00:49

9.51 One-Way ANOVA Test in R – Case Study (contd.)00:29

9.52 One-Way ANOVA Test in R – Case Study (contd.)00:35

9.53 Demo – Perform ANOVA00:07

9.54 Perform ANOVA02:55

9.55 Quiz

9.56 Summary01:12

9.57 Conclusion00:11

10.1 Introduction00:11

10.2 Objectives00:14

10.3 Introduction to Regression Analysis00:53

10.4 Use of Regression Analysis – Examples00:24

10.5 Use of Regression Analysis – Examples (contd.)00:23

10.6 Types Regression Analysis00:39

10.7 Simple Regression Analysis00:27

10.8 Multiple Regression Models00:25

10.9 Simple Linear Regression Model00:37

10.10 Simple Linear Regression Model Explained00:29

10.11 Demo – Perform Simple Linear Regression00:06

10.12 Perform Simple Linear Regression02:13

10.13 Correlation00:20

10.14 Correlation Between X and Y00:27

10.15 Correlation Between X and Y (contd.)00:24

10.16 Demo – Find Correlation00:06

10.17 Find Correlation01:23

10.18 Method of Least Squares Regression Model01:02

10.19 Coefficient of Multiple Determination Regression Model00:29

10.20 Standard Error of the Estimate Regression Model00:44

10.21 Dummy Variable Regression Model01:07

10.22 Interaction Regression Model00:23

10.23 Non-Linear Regression00:29

10.24 Non-Linear Regression Models01:24

10.25 Non-Linear Regression Models (contd.)01:03

10.26 Non-Linear Regression Models (contd.)00:23

10.27 Demo – Perform Regression Analysis with Multiple Variables00:07

10.28 Perform Regression Analysis with Multiple Variables01:46

10.29 Non-Linear Models to Linear Models00:13

10.30 Algorithms for Complex Non-Linear Models00:53

10.31 Quiz

10.32 Summary00:26

10.33 Summary (contd.)00:28

10.34 Conclusion00:10

11.1 Introduction00:10

11.2 Objectives00:17

11.3 Introduction to Classification00:40

11.4 Examples of Classification00:23

11.5 Classification vs. Prediction00:45

11.6 Classification System00:10

11.7 Classification Process00:54

11.8 Classification Process – Model Construction01:03

11.9 Classification Process – Model Usage in Prediction00:22

11.10 Issues Regarding Classification and Prediction00:15

11.11 Data Preparation Issues01:06

11.12 Evaluating Classification Methods Issues00:34

11.13 Decision Tree00:51

11.14 Decision Tree – Dataset00:14

11.15 Decision Tree – Dataset (contd.)00:15

11.16 Classification Rules of Trees00:34

11.17 Overfitting in Classification01:13

11.18 Tips to Find the Final Tree Size01:13

11.19 Basic Algorithm for a Decision Tree00:42

11.20 Statistical Measure – Information Gain01:16

11.21 Calculating Information Gain – Example00:08

11.22 Calculating Information Gain – Example (contd.)00:05

11.23 Calculating Information Gain for Continuous-Value Attributes01:44

11.24 Enhancing a Basic Tree00:32

11.25 Decision Trees in Data Mining00:18

11.26 Demo – Model a Decision Tree00:05

11.27 Model a Decision Tree02:06

11.28 Naive Bayes Classifier Model01:02

11.29 Features of Naive Bayes Classifier Model00:41

11.30 Bayesian Theorem00:40

11.31 Bayesian Theorem (contd.)00:14

11.32 Naive Bayes Classifier00:29

11.33 Applying Naive Bayes Classifier – Example00:14

11.34 Applying Naive Bayes Classifier – Example (contd.)00:25

11.35 Naive Bayes Classifier – Advantages and Disadvantages00:28

11.36 Demo – Perform Classification Using the Naive Bayes Method00:07

11.37 Perform Classification Using the Naive Bayes Method02:31

11.38 Nearest Neighbor Classifiers01:05

11.39 Nearest Neighbor Classifiers (contd.)00:20

11.40 Nearest Neighbor Classifiers (contd.)00:12

11.41 Computing Distance and Determining Class00:34

11.42 Choosing the Value of K00:21

11.43 Scaling Issues in Nearest Neighbor Classification00:35

11.44 Support Vector Machines01:19

11.45 Advantages of Support Vector Machines00:29

11.46 Geometric Margin in SVMs00:47

11.47 Linear SVMs00:08

11.48 Non-Linear SVMs00:26

11.49 Demo – Support a Vector Machine00:05

11.50 Support a Vector Machine01:51

11.51 Quiz

11.52 Summary00:36

11.53 Conclusion00:09

12.1 Introduction00:11

12.2 Objectives00:10

12.3 Introduction to Clustering00:42

12.4 Clustering vs. Classification00:58

12.5 Use Cases of Clustering00:33

12.6 Clustering Models01:47

12.7 K-means Clustering01:29

12.8 K-means Clustering Algorithm00:57

12.9 Pseudo Code of K-means00:33

12.10 K-means Clustering Using R00:40

12.11 K-means Clustering – Case Study00:26

12.12 K-means Clustering – Case Study (contd.)00:44

12.13 K-means Clustering – Case Study (contd.)01:23

12.14 Demo – Perform Clustering Using K-means00:05

12.15 Perform Clustering Using Kmeans01:38

12.16 Hierarchical Clustering01:12

12.17 Hierarchical Clustering Algorithms00:36

12.18 Requirements of Hierarchical Clustering Algorithms01:15

12.19 Agglomerative Clustering Process00:37

12.20 Hierarchical Clustering – Case Study00:37

12.21 Hierarchical Clustering – Case Study (contd.)00:10

12.22 Hierarchical Clustering – Case Study (contd.)00:22

12.23 Demo – Perform Hierarchical Clustering00:05

12.24 Perform Hierarchical Clustering01:24

12.25 DBSCAN Clustering01:01

12.26 Concepts of DBSCAN00:54

12.27 Concepts of DBSCAN (contd.)00:51

12.28 DBSCAN Clustering Algorithm01:06

12.29 DBSCAN in R00:36

12.30 DBSCAN Clustering – Case Study00:29

12.31 DBSCAN Clustering – Case Study (contd.)00:09

12.32 DBSCAN Clustering – Case Study (contd.)00:56

12.33 Quiz

12.34 Summary00:26

12.35 Conclusion00:10

13.1 Introduction00:12

13.2 Objectives00:17

13.3 Association Rule Mining00:39

13.4 Application Areas of Association Rule Mining01:09

13.5 Parameters of Interesting Relationships01:10

13.6 Association Rules00:54

13.7 Association Rule Strength Measures01:29

13.8 Limitations of Support and Confidence00:16

13.9 Apriori Algorithm00:40

13.10 Apriori Algorithm – Example00:35

13.11 Applying Aprior Algorithm00:36

13.12 Step 1 – Mine All Frequent Item Sets00:17

13.13 Algorithm to Find Frequent Item Set01:02

13.14 Finding Frequent Item Set – Example00:08

13.15 Ordering Items00:27

13.16 Ordering Items (contd.)00:06

13.17 Candidate Generation01:19

13.18 Candidate Generation (contd.)00:06

13.19 Candidate Generation – Example00:07

13.20 Step 2 – Generate Rules from Frequent Item Sets00:26

13.21 Generate Rules from Frequent Item Sets – Example00:13

13.22 Demo – Perform Association Using the Apriori Algorithm00:08

13.23 Perform Association Using the Apriori Algorithm01:41

13.24 Demo – Perform Visualization on Associated Rules00:07

13.25 Perform Visualization on Associated Rules01:24

13.26 Problems with Association Mining00:59

13.27 Quiz

13.28 Summary00:50

13.29 Conclusion00:06

13.30 Thank You00:06


Free Course

Business Analytics with Excel

0.1 Course Introduction05:27

1.1 Introduction02:15

1.2 What Is in It for Me00:10

1.3 Types of Analytics02:18

1.4 Areas of Analytics04:06

1.5 Quiz

1.6 Key Takeaways00:52

1.7 Conclusion00:11

2.1 Introduction02:12

2.2 What Is in It for Me00:21

2.3 Custom Formatting Introduction00:55

2.4 Custom Formatting Example03:24

2.5 Conditional Formatting Introduction00:44

2.6 Conditional Formatting Example101:47

2.7 Conditional Formatting Example202:43

2.8 Conditional Formatting Example301:37

2.9 Logical Functions04:00

2.10 Lookup and Reference Functions00:28

2.11 VLOOKUP Function02:14

2.12 HLOOKUP Function01:19

2.13 MATCH Function03:13

2.14 INDEX and OFFSET Function03:50

2.15 Statistical Function00:24

2.16 SUMIFS Function01:27

2.17 COUNTIFS Function01:13


2.19 STDEV, MEDIAN and RANK Function03:02

2.20 Exercise Intro00:35

2.21 Exercise

2.22 Quiz

2.23 Key Takeaways00:53

2.24 Conclusion00:09

3.1 Introduction01:47

3.2 What Is in It for Me00:22

3.3 Pivot Table Introduction01:03

3.4 Concept Video of Creating a Pivot Table02:47

3.5 Grouping in Pivot Table Introduction00:24

3.6 Grouping in Pivot Table Example 101:42

3.7 Grouping in Pivot Table Example 201:57

3.8 Custom Calculation01:14

3.9 Calculated Field and Calculated Item00:25

3.10 Calculated Field Example01:22

3.11 Calculated Item Example02:52

3.12 Slicer Intro00:35

3.13 Creating a Slicer01:22

3.14 Exercise Intro00:58

3.15 Exercise

3.16 Quiz

3.17 Key Takeaways00:35

3.18 Conclusion00:07

4.1 Introduction01:18

4.2 What Is in It for Me00:18

4.3 What is a Dashboard00:45

4.4 Principles of Great Dashboard Design02:16

4.5 How to Create Chart in Excel02:26

4.6 Chart Formatting01:45

4.7 Thermometer Chart03:32

4.8 Pareto Chart02:26

4.9 Form Controls in Excel01:08

4.10 Interactive Dashboard with Form Controls04:13

4.11 Chart with Checkbox05:48

4.12 Interactive Chart04:37

4.13 Exercise Intro00:55

4.14 Exercise1

4.15 Exercise2

4.16 Quiz

4.17 Key Takeaways00:34

4.18 Conclusion00:06

5.1 Introduction02:12

5.2 What Is in It for Me00:24

5.3 Concept Video Histogram05:18

5.4 Concept Video Solver Addin05:00

5.5 Concept Video Goal Seek02:57

5.6 Concept Video Scenario Manager04:16

5.7 Concept Video Data Table02:03

5.8 Concept Video Descriptive Statistics01:58

5.9 Exercise Intro00:52

5.10 Exercise

5.11 Quiz

5.12 Key Takeaways00:39

5.13 Conclusion00:09

6.1 Introduction01:51

6.2 What Is in It for Me00:21

6.3 Moving Average02:50

6.4 Hypothesis Testing04:20

6.5 ANOVA02:47

6.6 Covariance01:56

6.7 Correlation03:38

6.8 Regression05:15

6.9 Normal Distribution06:49

6.10 Exercise1 Intro00:34

6.11 Exercise 1

6.12 Exercise2 Intro00:17

6.13 Exercise 2

6.14 Exercise3 Intro00:19

6.15 Exercise 3

6.16 Quiz

6.17 Key Takeaways00:52

6.18 Conclusion00:08

7.1 Introduction01:17

7.2 What Is in It for Me00:18

7.3 Power Pivot04:16

7.4 Power View02:36

7.5 Power Query02:45

7.6 Power Map02:06

7.7 Quiz

7.8 Key Takeaways00:32

7.9 Conclusion00:11

Exam & Certification


To become a Certified Data Scientist with R, you must fulfill 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 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.

Live Virtual 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%.”


You can enroll for the training online. Upon successful payment you will receive an email from Yan Academy with an activation link to access the SimpliLearn online learning platform where all learnings are conducted. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.

  • Visa debit/credit card
  • American express and Diners club card
  • Master Card, or
  • PayPal


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.

All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty for data science online training.

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 the option to attend the course remotely from your desktop via video conferencing. This format reduces productivity challenges and decreases your time spent away from work or home.

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

We record the class sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

At the end of the training, subject to satisfactory evaluation of the project and passing the online exam (minimum 80%), you will receive a certificate from Yan Academy and Simplilearn stating that you are a certified data scientist with R programming.

Yes, we offer group discounts for our online training programs. Get in touch with us over the Drop us a Query/Request a Callback/Live Chat channels to find out more about our group discount packages.


Contact us using the form on the right of any page on the website, or select the Live Chat link. Our customer service representatives can provide you with more details.

Expert Assistance includes:

Mentoring Sessions: Live Interaction with a subject matter expert to help participants with queries regarding project implementation and the course in general

Guidance on forum: Industry experts to respond to participant queries regarding technical concepts, projects and case studies.

Teaching Assistance includes:

Project Assistance: Queries related to solving and completing projects and case studies, which are part of the Data Scientist with R programming course

Technical Assistance: Queries related to technical, installation and administration issues in Data Scientist with R programming training. In cases of critical issues, support will be rendered through a remote desktop.

R Programming: Queries related to R programming while solving and completing projects and case studies

Submit a request through any of following channels: Help & Support, Simplitalk, or Live Chat. A teaching assistant will get in touch with you within 48 hours.

Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.


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