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

Course description

  • What’s the focus of this course?

    The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.

    Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.

    Mastering advanced statistical concepts:   The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.

    As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience.  Additionally, we have four more projects for further practice.

  • What are the course objectives?

    This course will enable you to:
    • Gain a foundational understanding of business analytics
    • Install R, R-studio, and workspace setup. You will also learn about the various R packages
    • 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
    • Gain a basic understanding of the 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?

    There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially 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 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 offered along with the course to ensure a 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.

  • What projects are included in this course?

    The course includes eight real-life, industry-based projects. R CloudLab has been provided for a hassle-free execution of these projects. 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 an insight of the regular happenings 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: To understand listener preferences, the details 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 success and failure based on user demographic data; in this case, for defaulting on a loan or not defaulting. 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 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.

    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 preview

    • Lesson 01 - Introduction to Business Analytics 22:29
      • 1.1 Introduction 00:10
      • 1.2 Objectives 00:15
      • 1.3 Need of Business Analytics 01:28
      • 1.4 Business Decisions 00:22
      • 1.5 Business Decisions (contd.) 00:07
      • 1.6 Introduction to Business Analytics 01:09
      • 1.7 Features of Business Analytics 01:20
      • 1.8 Types of Business Analytics 00:19
      • 1.9 Descriptive Analytics 00:55
      • 1.10 Predictive Analytics 01:09
      • 1.11 Predictive Analytics (contd.) 00:41
      • 1.12 Prescriptive Analytics 01:13
      • 1.13 Prescriptive Analytics (contd.) 00:24
      • 1.14 Supply Chain Analytics 00:56
      • 1.15 Health Care Analytics 00:40
      • 1.16 Marketing Analytics 00:44
      • 1.17 Human Resource Analytics 00:36
      • 1.18 Web Analytics 00:46
      • 1.19 Application of Business Analytics - Wal-Mart Case Study 00:16
      • 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 Study 00:29
      • 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 Decisions 00:37
      • 1.26 Business Intelligence (BI) 01:09
      • 1.27 Data Science 00:33
      • 1.28 Importance of Data Science 00:35
      • 1.29 Data Science as a Strategic Asset 00:25
      • 1.30 Big Data 00:39
      • 1.31 Analytical Tools 00:16
      • 1.32 Quiz
      • 1.33 Summary 00:52
      • 1.34 Summary (contd.) 00:39
      • 1.35 Conclusion 00:11
    • Lesson 02 - Introduction to R 15:36
      • 2.1 Introduction 00:11
      • 2.2 Objectives 00:21
      • 2.3 An Introduction to R 00:56
      • 2.4 Comprehensive R Archive Network (CRAN) 00:39
      • 2.5 Cons of R 01:00
      • 2.6 Companies Using R 01:16
      • 2.7 Understanding R 00:55
      • 2.8 Installing R on Various Operating Systems 00:09
      • 2.9 Installing R on Windows from CRAN Website 00:15
      • 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 R 00:06
      • 2.13 Install R 01:02
      • 2.14 IDEs for R 00:50
      • 2.15 Installing RStudio on Various Operating Systems 00:34
      • 2.16 Demo - Install RStudio 00:06
      • 2.17 Install RStudio 00:51
      • 2.18 Steps in R Initiation 00:20
      • 2.19 Benefits of R Workspace 00:40
      • 2.20 Setting the Workplace 00:08
      • 2.21 Functions and Help in R 00:28
      • 2.22 Demo - Access the Help Document 00:05
      • 2.23 Access the Help Document 01:11
      • 2.24 R Packages 00:48
      • 2.25 Installing an R Package 00:10
      • 2.26 Demo - Install and Load a Package 00:05
      • 2.27 Install and Load a Package 00:56
      • 2.28 Quiz
      • 2.29 Summary 00:33
      • 2.30 Summary (contd.) 00:21
      • 2.31 Conclusion 00:11
    • Lesson 03 - R Programming 25:23
      • 3.1 Introduction 00:10
      • 3.2 Objectives 00:20
      • 3.3 Operators in R 00:15
      • 3.4 Arithmetic Operators 00:21
      • 3.5 Demo - Perform Arithmetic Operations 00:05
      • 3.6 Use Arithmetic Operations 02:00
      • 3.7 Relational Operators 00:16
      • 3.8 Demo - Use Relational Operators 00:05
      • 3.9 Use Relational Operators 01:00
      • 3.10 Logical Operators 00:41
      • 3.11 Demo - Perform Logical Operations 00:05
      • 3.12 Use Logical Operators 01:22
      • 3.13 Assignment Operators 00:13
      • 3.14 Demo - Use Assignment Operator 00:05
      • 3.15 Use Assignment Operator 00:32
      • 3.16 Conditional Statements in R 00:24
      • 3.17 Conditional Statements in R (contd.) 00:34
      • 3.18 Conditional Statements in R (contd.) 00:32
      • 3.19 Ifelse() Function 00:18
      • 3.20 Demo - Use Conditional Statements 00:06
      • 3.21 Use Conditional Statements 01:44
      • 3.22 Switch Function 00:45
      • 3.23 Demo - Use the Switch Function 00:05
      • 3.24 Use Switch Function 01:39
      • 3.25 Loops in R 00:14
      • 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 Statement 00:38
      • 3.30 Next Statement 00:35
      • 3.31 Demo - Use Loops 00:05
      • 3.32 Use Loops 02:37
      • 3.33 Scan() Function 01:04
      • 3.34 Running an R Script 00:40
      • 3.35 Running a Batch Script 00:20
      • 3.36 R Functions 00:33
      • 3.37 R Functions (contd.) 00:05
      • 3.38 Demo - Use R Functions 00:06
      • 3.39 Use Commonly Used Functions 01:37
      • 3.40 Demo - Use String Functions 00:07
      • 3.41 Use Commonly-USed String Functions 00:53
      • 3.42 Quiz
      • 3.43 Summary 00:39
      • 3.44 Conclusion 00:11
    • Lesson 04 - R Data Structure 26:50
      • 4.1 Introduction 00:10
      • 4.2 Objectives 00:16
      • 4.3 Types of Data Structures in R 00:41
      • 4.4 Vectors 00:47
      • 4.5 Demo - Create a Vector 00:05
      • 4.6 Create a Vector 01:24
      • 4.7 Scalars 00:12
      • 4.8 Colon Operator 00:15
      • 4.9 Accessing Vector Elements 00:44
      • 4.10 Matrices 00:35
      • 4.11 Matrices (contd.) 00:18
      • 4.12 Accessing Matrix Elements 00:23
      • 4.13 Demo - Create a Matrix 00:05
      • 4.14 Create a Matrix 01:45
      • 4.15 Arrays 00:33
      • 4.16 Accessing Array Elements 00:14
      • 4.17 Demo - Create an Array 00:05
      • 4.18 Create an Array 01:31
      • 4.19 Data Frames 00:57
      • 4.20 Elements of Data Frames 00:13
      • 4.21 Demo - Create a Data Frame 00:05
      • 4.22 Create a Data Frame 01:54
      • 4.23 Factors 00:41
      • 4.24 Demo - Create a Factor 00:05
      • 4.25 Create a Factor 01:49
      • 4.26 Lists 00:20
      • 4.27 Demo - Create a List 00:05
      • 4.28 Create a List 01:14
      • 4.29 Importing Files in R 00:22
      • 4.30 Importing an Excel File 00:53
      • 4.31 Importing a Minitab File 00:20
      • 4.32 Importing a Table File 00:29
      • 4.33 Importing a CSV File 00:43
      • 4.34 Demo - Read Data from a File 00:05
      • 4.35 Read Data from a File 03:50
      • 4.36 Exporting Files from R 00:33
      • 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 Summary 00:27
      • 4.42 Conclusion 00:10
    • Lesson 05 - Apply Functions 28:01
      • 5.1 Introduction 00:13
      • 5.2 Objectives 00:15
      • 5.3 Types of Apply Functions 00:31
      • 5.4 Apply() Function 00:13
      • 5.5 Apply() Function (contd.) 00:57
      • 5.6 Apply() Function (contd.) 00:31
      • 5.7 Demo - Use Apply() Function 00:05
      • 5.8 Use Apply Function 01:10
      • 5.9 Lapply() Function 01:05
      • 5.10 Demo - Use Lapply() Function 00:05
      • 5.11 Use Lapply Function 00:54
      • 5.12 Sapply() Function 00:56
      • 5.13 Demo - Use Sapply() Function 00:05
      • 5.14 Use Sapply Function 01:10
      • 5.15 Tapply() Function 00:28
      • 5.16 Tapply() Function (contd.) 00:23
      • 5.17 Tapply() Function (contd.) 00:19
      • 5.18 Demo - Use Tapply() Function 00:05
      • 5.19 Use Tapply Function 01:22
      • 5.20 Vapply() Function 00:47
      • 5.21 Demo - Use Vapply() Function 00:05
      • 5.22 Use Vapply Function 01:57
      • 5.23 Mapply() Function 00:21
      • 5.24 Mapply() Function (contd.) 00:16
      • 5.25 Mapply() Function (contd.) 00:34
      • 5.26 Dplyr Package - An Overview 01:08
      • 5.27 Dplyr Package - The Five Verbs 00:51
      • 5.28 Installing the Dplyr Package 00:15
      • 5.29 Functions of the Dplyr Package 00:20
      • 5.30 Functions of the Dplyr Package - Select() 00:30
      • 5.31 Demo - Use the Select() Function 00:06
      • 5.32 Use the Select Function 01:35
      • 5.33 Functions of Dplyr-Package - Filter() 00:59
      • 5.34 Demo - Use the Filter() Function 00:05
      • 5.35 Use Select Function 01:14
      • 5.36 Functions of Dplyr Package - Arrange() 00:10
      • 5.37 Demo - Use the Arrange() Function 00:06
      • 5.38 Use Arrange Function 01:29
      • 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() Function 00:06
      • 5.43 Use Summarise Function 01:42
      • 5.44 Quiz
      • 5.45 Summary 00:33
      • 5.46 Conclusion 00:11
    • Lesson 06 - Data Visualization 33:48
      • 6.1 Introduction 00:11
      • 6.2 Objectives 00:17
      • 6.3 Graphics in R 00:38
      • 6.4 Types of Graphics 00:25
      • 6.5 Bar Charts 00:34
      • 6.6 Creating Simple Bar Charts 00:33
      • 6.7 Editing a Simple Bar Chart 00:34
      • 6.8 Demo - Create a Bar Chart 00:06
      • 6.9 Create a Bar Chart 01:50
      • 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 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 00:27
      • 6.16 Editing a Pie Chart (contd.) 00:28
      • 6.17 Demo - Create a Pie Chart 00:05
      • 6.18 Create a Pie Chart 03:01
      • 6.19 Histograms 00:53
      • 6.20 Creating a Histogram 00:37
      • 6.21 Kernel Density Plots 00:19
      • 6.22 Creating a Kernel Density Plot 00:29
      • 6.23 Demo - Create Histograms and a Density Plot 00:07
      • 6.24 Create Histograms and a Density Plot 02:23
      • 6.25 Line Charts 00:30
      • 6.26 Creating a Line Chart 00:21
      • 6.27 Box Plots 00:47
      • 6.28 Creating a Box Plot 00:53
      • 6.29 Demo - Create Line Graphs and a Box Plot 00:07
      • 6.30 Create Line Graphs and a Box Plot 01:59
      • 6.31 Heat Maps 00:48
      • 6.32 Creating a Heat Map 00:28
      • 6.33 Demo - Create a Heat Map 00:06
      • 6.34 Create a Heatmap 01:10
      • 6.35 Word Clouds 00:28
      • 6.36 Creating a Word Cloud 00:52
      • 6.37 Demo - Create a Word Cloud 00:06
      • 6.38 Create a Word Cloud 01:23
      • 6.39 File Formats for Graphic Outputs 00:51
      • 6.40 Saving a Graphic Output as a File 01:02
      • 6.41 Saving a Graphic Output as a File (contd.) 00:43
      • 6.42 Demo - Save Graphics to a File 00:06
      • 6.43 Save Graphics to a File 00:49
      • 6.44 Exporting Graphs in RStudio 00:27
      • 6.45 Exporting Graphs as PDFs in RStudio 00:17
      • 6.46 Demo - Save Graphics Using RStudio 00:06
      • 6.47 Save Graphics Using RStudio 00:53
      • 6.48 Quiz
      • 6.49 Summary 00:27
      • 6.50 Conclusion 00:11
    • Lesson 07 - Introduction to Statistics 33:59
      • 7.1 Introduction 00:10
      • 7.2 Objectives 00:21
      • 7.3 Basics of Statistics 02:03
      • 7.4 Types of Data 01:20
      • 7.5 Qualitative vs. Quantitative Analysis 00:52
      • 7.6 Types of Measurements in Order 00:35
      • 7.7 Nominal Measurement 00:46
      • 7.8 Ordinal Measurement 00:43
      • 7.9 Interval Measurement 00:49
      • 7.10 Ratio Measurement 00:59
      • 7.11 Statistical Investigation 00:13
      • 7.12 Statistical Investigation Steps 01:03
      • 7.13 Normal Distribution 00:58
      • 7.14 Normal Distribution (contd.) 00:36
      • 7.15 Example of Normal Distribution 00:08
      • 7.16 Importance of Normal Distribution in Statistics 00:34
      • 7.17 Use of the Symmetry Property of Normal Distribution 00:52
      • 7.18 Standard Normal Distribution 00:33
      • 7.19 Demo - Use Probability Distribution Functions 00:07
      • 7.20 Use Probability Distribution Functions 06:52
      • 7.21 Distance Measures 00:42
      • 7.22 Distance Measures - A Comparison 00:26
      • 7.23 Euclidean Distance 00:24
      • 7.24 Example of Euclidean Distance 00:37
      • 7.25 Manhattan Distance 00:31
      • 7.26 Minkowski Distance 00:15
      • 7.27 Mahalanobis Distance 00:27
      • 7.28 Cosine Similarity 00:26
      • 7.29 Correlation 00:43
      • 7.30 Correlation Measures Explained 01:10
      • 7.31 Pearson Product Moment Correlation (PPMC) 00:41
      • 7.32 Pearson Product Moment Correlation (PPMC) (contd.) 00:35
      • 7.33 Pearson Correlation - Case Study 00:35
      • 7.34 Dist() Function in R 00:40
      • 7.35 Demo - Perform the Distance Matrix Computations 00:08
      • 7.36 Perform the Distance Matrix Computations 03:44
      • 7.37 Quiz
      • 7.38 Summary 00:35
      • 7.39 Summary (contd.) 00:35
      • 7.40 Conclusion 00:11
    • Lesson 08 - Hypothesis Testing I 19:29
      • 8.1 Introduction 00:11
      • 8.2 Objectives 00:22
      • 8.3 Hypothesis 02:01
      • 8.4 Need of Hypothesis Testing in Businesses 00:52
      • 8.5 Null Hypothesis 00:26
      • 8.6 Null Hypothesis (contd.) 00:34
      • 8.7 Alternate Hypothesis 00:37
      • 8.8 Null vs. Alternate Hypothesis 00:33
      • 8.9 Chances of Errors in Sampling 00:30
      • 8.10 Types of Errors 00:57
      • 8.11 Contingency Table 01:15
      • 8.12 Decision Making 00:24
      • 8.13 Critical Region 00:42
      • 8.14 Level of Significance 00:51
      • 8.15 Confidence Coefficient 00:49
      • 8.16 Bita Risk 00:26
      • 8.17 Power of Test 00:28
      • 8.18 Factors Affecting the Power of Test 00:23
      • 8.19 Types of Statistical Hypothesis Tests 01:05
      • 8.20 An Example of Statistical Hypothesis Tests 00:31
      • 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 Test 00:30
      • 8.25 Upper Tail Test (contd.) 00:27
      • 8.26 Upper Tail Test (contd.) 00:19
      • 8.27 Test Statistic 00:47
      • 8.28 Factors Affecting Test Statistic 00:12
      • 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 Table 00:17
      • 8.32 Quiz
      • 8.33 Summary 01:02
      • 8.34 Conclusion 00:11
    • Lesson 09 - Hypothesis Testing II 39:45
      • 9.1 Introduction 00:11
      • 9.2 Objectives 00:15
      • 9.3 Parametric Tests 00:35
      • 9.4 Z-Test 00:23
      • 9.5 Z-Test in R - Case Study 00:50
      • 9.6 T-Test 00:30
      • 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 01:32
      • 9.10 Testing Null Hypothesis 00:50
      • 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.) 00:50
      • 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 01:21
      • 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.) 00:55
      • 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 00:42
      • 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 01:22
      • 9.46 F-Ratio Test 00:37
      • 9.47 F-Ratio Test in R - Example 00:22
      • 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.) 00:35
      • 9.53 Demo - Perform ANOVA 00:07
      • 9.54 Perform ANOVA 02:55
      • 9.55 Quiz
      • 9.56 Summary 01:12
      • 9.57 Conclusion 00:11
    • Lesson 10 - Regression Analysis 20:48
      • 10.1 Introduction 00:11
      • 10.2 Objectives 00:14
      • 10.3 Introduction to Regression Analysis 00:53
      • 10.4 Use of Regression Analysis - Examples 00:24
      • 10.5 Use of Regression Analysis - Examples (contd.) 00:23
      • 10.6 Types Regression Analysis 00:39
      • 10.7 Simple Regression Analysis 00:27
      • 10.8 Multiple Regression Models 00:25
      • 10.9 Simple Linear Regression Model 00:37
      • 10.10 Simple Linear Regression Model Explained 00:29
      • 10.11 Demo - Perform Simple Linear Regression 00:06
      • 10.12 Perform Simple Linear Regression 02:13
      • 10.13 Correlation 00:20
      • 10.14 Correlation Between X and Y 00:27
      • 10.15 Correlation Between X and Y (contd.) 00:24
      • 10.16 Demo - Find Correlation 00:06
      • 10.17 Find Correlation 01:23
      • 10.18 Method of Least Squares Regression Model 01:02
      • 10.19 Coefficient of Multiple Determination Regression Model 00:29
      • 10.20 Standard Error of the Estimate Regression Model 00:44
      • 10.21 Dummy Variable Regression Model 01:07
      • 10.22 Interaction Regression Model 00:23
      • 10.23 Non-Linear Regression 00:29
      • 10.24 Non-Linear Regression Models 01:24
      • 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 Variables 00:07
      • 10.28 Perform Regression Analysis with Multiple Variables 01:46
      • 10.29 Non-Linear Models to Linear Models 00:13
      • 10.30 Algorithms for Complex Non-Linear Models 00:53
      • 10.31 Quiz
      • 10.32 Summary 00:26
      • 10.33 Summary (contd.) 00:28
      • 10.34 Conclusion 00:10
    • Lesson 11 - Classification 33:43
      • 11.1 Introduction 00:10
      • 11.2 Objectives 00:17
      • 11.3 Introduction to Classification 00:40
      • 11.4 Examples of Classification 00:23
      • 11.5 Classification vs. Prediction 00:45
      • 11.6 Classification System 00:10
      • 11.7 Classification Process 00:54
      • 11.8 Classification Process - Model Construction 01:03
      • 11.9 Classification Process - Model Usage in Prediction 00:22
      • 11.10 Issues Regarding Classification and Prediction 00:15
      • 11.11 Data Preparation Issues 01:06
      • 11.12 Evaluating Classification Methods Issues 00:34
      • 11.13 Decision Tree 00:51
      • 11.14 Decision Tree - Dataset 00:14
      • 11.15 Decision Tree - Dataset (contd.) 00:15
      • 11.16 Classification Rules of Trees 00:34
      • 11.17 Overfitting in Classification 01:13
      • 11.18 Tips to Find the Final Tree Size 01:13
      • 11.19 Basic Algorithm for a Decision Tree 00:42
      • 11.20 Statistical Measure - Information Gain 01:16
      • 11.21 Calculating Information Gain - Example 00:08
      • 11.22 Calculating Information Gain - Example (contd.) 00:05
      • 11.23 Calculating Information Gain for Continuous-Value Attributes 01:44
      • 11.24 Enhancing a Basic Tree 00:32
      • 11.25 Decision Trees in Data Mining 00:18
      • 11.26 Demo - Model a Decision Tree 00:05
      • 11.27 Model a Decision Tree 02:06
      • 11.28 Naive Bayes Classifier Model 01:02
      • 11.29 Features of Naive Bayes Classifier Model 00:41
      • 11.30 Bayesian Theorem 00:40
      • 11.31 Bayesian Theorem (contd.) 00:14
      • 11.32 Naive Bayes Classifier 00:29
      • 11.33 Applying Naive Bayes Classifier - Example 00:14
      • 11.34 Applying Naive Bayes Classifier - Example (contd.) 00:25
      • 11.35 Naive Bayes Classifier - Advantages and Disadvantages 00:28
      • 11.36 Demo - Perform Classification Using the Naive Bayes Method 00:07
      • 11.37 Perform Classification Using the Naive Bayes Method 02:31
      • 11.38 Nearest Neighbor Classifiers 01:05
      • 11.39 Nearest Neighbor Classifiers (contd.) 00:20
      • 11.40 Nearest Neighbor Classifiers (contd.) 00:12
      • 11.41 Computing Distance and Determining Class 00:34
      • 11.42 Choosing the Value of K 00:21
      • 11.43 Scaling Issues in Nearest Neighbor Classification 00:35
      • 11.44 Support Vector Machines 01:19
      • 11.45 Advantages of Support Vector Machines 00:29
      • 11.46 Geometric Margin in SVMs 00:47
      • 11.47 Linear SVMs 00:08
      • 11.48 Non-Linear SVMs 00:26
      • 11.49 Demo - Support a Vector Machine 00:05
      • 11.50 Support a Vector Machine 01:51
      • 11.51 Quiz
      • 11.52 Summary 00:36
      • 11.53 Conclusion 00:09
    • Lesson 12 - Clustering 25:12
      • 12.1 Introduction 00:11
      • 12.2 Objectives 00:10
      • 12.3 Introduction to Clustering 00:42
      • 12.4 Clustering vs. Classification 00:58
      • 12.5 Use Cases of Clustering 00:33
      • 12.6 Clustering Models 01:47
      • 12.7 K-means Clustering 01:29
      • 12.8 K-means Clustering Algorithm 00:57
      • 12.9 Pseudo Code of K-means 00:33
      • 12.10 K-means Clustering Using R 00:40
      • 12.11 K-means Clustering - Case Study 00:26
      • 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-means 00:05
      • 12.15 Perform Clustering Using Kmeans 01:38
      • 12.16 Hierarchical Clustering 01:12
      • 12.17 Hierarchical Clustering Algorithms 00:36
      • 12.18 Requirements of Hierarchical Clustering Algorithms 01:15
      • 12.19 Agglomerative Clustering Process 00:37
      • 12.20 Hierarchical Clustering - Case Study 00:37
      • 12.21 Hierarchical Clustering - Case Study (contd.) 00:10
      • 12.22 Hierarchical Clustering - Case Study (contd.) 00:22
      • 12.23 Demo - Perform Hierarchical Clustering 00:05
      • 12.24 Perform Hierarchical Clustering 01:24
      • 12.25 DBSCAN Clustering 01:01
      • 12.26 Concepts of DBSCAN 00:54
      • 12.27 Concepts of DBSCAN (contd.) 00:51
      • 12.28 DBSCAN Clustering Algorithm 01:06
      • 12.29 DBSCAN in R 00:36
      • 12.30 DBSCAN Clustering - Case Study 00:29
      • 12.31 DBSCAN Clustering - Case Study (contd.) 00:09
      • 12.32 DBSCAN Clustering - Case Study (contd.) 00:56
      • 12.33 Quiz
      • 12.34 Summary 00:26
      • 12.35 Conclusion 00:10
    • Lesson 13 - Association 17:29
      • 13.1 Introduction 00:12
      • 13.2 Objectives 00:17
      • 13.3 Association Rule Mining 00:39
      • 13.4 Application Areas of Association Rule Mining 01:09
      • 13.5 Parameters of Interesting Relationships 01:10
      • 13.6 Association Rules 00:54
      • 13.7 Association Rule Strength Measures 01:29
      • 13.8 Limitations of Support and Confidence 00:16
      • 13.9 Apriori Algorithm 00:40
      • 13.10 Apriori Algorithm - Example 00:35
      • 13.11 Applying Aprior Algorithm 00:36
      • 13.12 Step 1 - Mine All Frequent Item Sets 00:17
      • 13.13 Algorithm to Find Frequent Item Set 01:02
      • 13.14 Finding Frequent Item Set - Example 00:08
      • 13.15 Ordering Items 00:27
      • 13.16 Ordering Items (contd.) 00:06
      • 13.17 Candidate Generation 01:19
      • 13.18 Candidate Generation (contd.) 00:06
      • 13.19 Candidate Generation - Example 00:07
      • 13.20 Step 2 - Generate Rules from Frequent Item Sets 00:26
      • 13.21 Generate Rules from Frequent Item Sets - Example 00:13
      • 13.22 Demo - Perform Association Using the Apriori Algorithm 00:08
      • 13.23 Perform Association Using the Apriori Algorithm 01:41
      • 13.24 Demo - Perform Visualization on Associated Rules 00:07
      • 13.25 Perform Visualization on Associated Rules 01:24
      • 13.26 Problems with Association Mining 00:59
      • 13.27 Quiz
      • 13.28 Summary 00:50
      • 13.29 Conclusion 00:06
      • 13.30 Thank You 00:06
    • Lesson 00 - Introduction 05:27
      • 0.1 Course Introduction 05:27
    • Lesson 01 - Introduction to Business Analytics 09:52
      • 1.1 Introduction 02:15
      • 1.2 What Is in It for Me 00:10
      • 1.3 Types of Analytics 02:18
      • 1.4 Areas of Analytics 04:06
      • 1.5 Quiz
      • 1.6 Key Takeaways 00:52
      • 1.7 Conclusion 00:11
    • Lesson 02 - Formatting Conditional Formatting and Important Fuctions 38:29
      • 2.1 Introduction 02:12
      • 2.2 What Is in It for Me 00:21
      • 2.3 Custom Formatting Introduction 00:55
      • 2.4 Custom Formatting Example 03:24
      • 2.5 Conditional Formatting Introduction 00:44
      • 2.6 Conditional Formatting Example1 01:47
      • 2.7 Conditional Formatting Example2 02:43
      • 2.8 Conditional Formatting Example3 01:37
      • 2.9 Logical Functions 04:00
      • 2.10 Lookup and Reference Functions 00:28
      • 2.11 VLOOKUP Function 02:14
      • 2.12 HLOOKUP Function 01:19
      • 2.13 MATCH Function 03:13
      • 2.14 INDEX and OFFSET Function 03:50
      • 2.15 Statistical Function 00:24
      • 2.16 SUMIFS Function 01:27
      • 2.17 COUNTIFS Function 01:13
      • 2.18 PERCENTILE and QUARTILE 01:59
      • 2.19 STDEV, MEDIAN and RANK Function 03:02
      • 2.20 Exercise Intro 00:35
      • 2.21 Exercise
      • 2.22 Quiz
      • 2.23 Key Takeaways 00:53
      • 2.24 Conclusion 00:09
    • Lesson 03 - Analyzing Data with Pivot Tables 19:32
      • 3.1 Introduction 01:47
      • 3.2 What Is in It for Me 00:22
      • 3.3 Pivot Table Introduction 01:03
      • 3.4 Concept Video of Creating a Pivot Table 02:47
      • 3.5 Grouping in Pivot Table Introduction 00:24
      • 3.6 Grouping in Pivot Table Example 1 01:42
      • 3.7 Grouping in Pivot Table Example 2 01:57
      • 3.8 Custom Calculation 01:14
      • 3.9 Calculated Field and Calculated Item 00:25
      • 3.10 Calculated Field Example 01:22
      • 3.11 Calculated Item Example 02:52
      • 3.12 Slicer Intro 00:35
      • 3.13 Creating a Slicer 01:22
      • 3.14 Exercise Intro 00:58
      • 3.15 Exercise
      • 3.16 Quiz
      • 3.17 Key Takeaways 00:35
      • 3.18 Conclusion 00:07
    • Lesson 04 - Dashboarding 32:07
      • 4.1 Introduction 01:18
      • 4.2 What Is in It for Me 00:18
      • 4.3 What is a Dashboard 00:45
      • 4.4 Principles of Great Dashboard Design 02:16
      • 4.5 How to Create Chart in Excel 02:26
      • 4.6 Chart Formatting 01:45
      • 4.7 Thermometer Chart 03:32
      • 4.8 Pareto Chart 02:26
      • 4.9 Form Controls in Excel 01:08
      • 4.10 Interactive Dashboard with Form Controls 04:13
      • 4.11 Chart with Checkbox 05:48
      • 4.12 Interactive Chart 04:37
      • 4.13 Exercise Intro 00:55
      • 4.14 Exercise1
      • 4.15 Exercise2
      • 4.16 Quiz
      • 4.17 Key Takeaways 00:34
      • 4.18 Conclusion 00:06
    • Lesson 05 - Business Analytics With Excel 25:48
      • 5.1 Introduction 02:12
      • 5.2 What Is in It for Me 00:24
      • 5.3 Concept Video Histogram 05:18
      • 5.4 Concept Video Solver Addin 05:00
      • 5.5 Concept Video Goal Seek 02:57
      • 5.6 Concept Video Scenario Manager 04:16
      • 5.7 Concept Video Data Table 02:03
      • 5.8 Concept Video Descriptive Statistics 01:58
      • 5.9 Exercise Intro 00:52
      • 5.10 Exercise
      • 5.11 Quiz
      • 5.12 Key Takeaways 00:39
      • 5.13 Conclusion 00:09
    • Lesson 06 - Data Analysis Using Statistics 31:57
      • 6.1 Introduction 01:51
      • 6.2 What Is in It for Me 00:21
      • 6.3 Moving Average 02:50
      • 6.4 Hypothesis Testing 04:20
      • 6.5 ANOVA 02:47
      • 6.6 Covariance 01:56
      • 6.7 Correlation 03:38
      • 6.8 Regression 05:15
      • 6.9 Normal Distribution 06:49
      • 6.10 Exercise1 Intro 00:34
      • 6.11 Exercise 1
      • 6.12 Exercise2 Intro 00:17
      • 6.13 Exercise 2
      • 6.14 Exercise3 Intro 00:19
      • 6.15 Exercise 3
      • 6.16 Quiz
      • 6.17 Key Takeaways 00:52
      • 6.18 Conclusion 00:08
    • Lesson 07 - Power BI 14:01
      • 7.1 Introduction 01:17
      • 7.2 What Is in It for Me 00:18
      • 7.3 Power Pivot 04:16
      • 7.4 Power View 02:36
      • 7.5 Power Query 02:45
      • 7.6 Power Map 02:06
      • 7.7 Quiz
      • 7.8 Key Takeaways 00:32
      • 7.9 Conclusion 00:11
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Exam & certification FREE PRACTICE TEST

  • How to get certified?

    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 course
    Note:
    • When you have completed the course, you will receive a three-month experience certificate for implementing the projects using R.
    • It is mandatory that you fulfill both the criteria i.e., completion of any one project and clearing the online exam with minimum score of 80%, to become a certified data scientist.

  • What do I need to do to unlock my Simplilearn certificate?

    Online Classroom:
    • You need to 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%.

Reviews

I took the R, SAS and Excel Course for Data Analytics. I was out of the workforce for a few months and had a background in statistics but I needed to refresh some skills before applying for jobs. Overall, the course was very strong. I liked how it was straight to the point without any bells and whistles. It often focused on concepts and the broader picture of learning. It spanned in complexity so one can kind push themselves to continue investing themselves in the subject matter at their own pace. They seem to really care that you want to learn and help you get there. In terms of ease of use and customer service Simplilearn was very strong. It is a matter of simply clicking your course and learning. The support team was great and responded to all my questions via live chat quickly, nicely, and easily. If I had one comment, I would say indicate on your settings when your course runs out. I also had trouble some trouble navigating screams on my surface pro but that was all minimal compared to the benefits. Would definitely recommend.

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Simplilearn has been a great help for me in my professional and academic progress. I have enjoyed taking their courses and can recommend them to anyone. Currently I have taken their Data Science course and I will now be looking into taking their CAPM program. Their tutors are also top-notch.

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It was Great!!! My tutors were phenomenal. I took a project overview class and it really helped sharpened my approach on how I world present my final project. The class has been great. I’ve done some self-study on Data Science but then realized that taking it as a course with experts would add some substance to my learning curve.I must admit that my decision to take it with Simplilearn has been the right choice. There is so much detail and hands-on practice in R, SAS and Excel in these classes during the training session. I continue to refresh my reading and benefit from group discussions from SimpliLearn. I’ll absolutely recommend to anyone to give it a try and take one class, and I promise you’ll get more than you expect in content and value."

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Excellent instructor with the ability to provide real world experience and insights. Emphasis on the tools along with practical and useful insights. It is not an easy course for those without programming experience, but it does take away some of the mystery and confusion associated with using these tools.

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Level of granularity. Tutor knowledge. Class size. Tutor's confidence, subject knowledge, and high level of commitment to student understanding of the material. Tutor assisted students who had issues with SAS installation. Great Tutor-Student interaction.

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My instructor is obviously a Pro at what she does. I wish I had someone around like her to mentor me when I was younger. Some of the technical aspects of the course are a little challenging, but the concepts for doing what is being taught is becoming clear to me. I hope this will make all the difference as I delve into the coursework even more.

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Great experience with the provider, enjoyed learning, very helpful application, and staff support. Good start for mastering R, SAS, and Excel.

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Simplilearn is an awesome platform to learn things online. This was the first time I tried for an online course, and I was not so sure about the process and support. However, during the session, I did not realize that this was online. The presentations by the trainer made me feel that I was in a classroom. All my questions were always answered within 33 hours. The best part of joining Simplilearn was that I completed the course by attending the classes from home. I have become more confident in data analysis. Thank you so much team for all your support. I will definitely recommend Simplilearn to all my colleagues and friends.

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I am Data Analyst with 7 years of work experience, but I didn't have the chance to work with Statistics like I am in this course. Its a good course even for beginners. Overall, the training is very good. Thank you Simplilearn.

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The course material of the Data Science program was well designed for beginners. The presentations were precise and to the point. The mentors in the various sessions were helpful and kept close to the basics. The examples used mirror real life scenarios, hence are very useful. Finally, the members of the CD team were truly delightful!

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"It was a great experience to learn Data Science with Simplilearn. It is really convenient to learn at your own pace and time. The material is also good. This course has helped me boost my career. Coming from a Consulting firm, it was mandatory to learn data analytics skills. I really feel confident and I truly appreciate Simplilearn for it. Simplilearn is a Master. The reminders and guidance that I received from them was just beyond expectations! Whenever I had any query, it was resolved! Right on time... THANKS SO MUCH!"

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I enrolled for Data Science with R, SAS and Excel program at Simplilearn after researching a lot of similar websites who offered similar course. The uniqueness of Simplilearn is their course depth combined with good pricing and friendly customer service. I mean this is what one expects when searching for a good online course. My advice: Take their courses. It will give you a good heads-up to your career.

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I could recommend Simplilearn to anyone without hesitation. The courses have great depth and they allow you do work on them at your own pace. The course content is regularly updated and the content is presented in a crisp manner. Simplilearn’s course has added weight to my CV. Customer service support provided by them is top-notch. I would like to especially mention Sheena, as she helped me with the various queries I had. Highly recommend people reading this to go through all the courses they have, pick the course that suits you best, and go ahead with Simplilearn.

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It was a very useful and good learning experience. I have learnt the basic statistical concepts and real-time data analysis and interpretation. Got proper knowledge of R and SAS tools. Thanks to the entire Simplilearn Team.

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I love the fact that the trainer has worked an extra mile to create a practice session for us -- it's helpful!

Course advisor

Simon Tavasoli Analytics Lead at Cancer Care Ontario

Simon is a Data Scientist with 12 years of experience in Healthcare analytics. He is a Masters in Biostatistics from the University of Western Ontario. Simon is passionate about teaching data science and has a number of journal publications in preventive medicine analytics.

FAQs

  • 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 present in the course.

  • Who are the trainers?

    Our training courses are delivered by highly qualified, certified instructors with relevant industry experience.

  • What training formats are used for this course?

    We offer this 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 saves 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.

  • What if I miss a class?

    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.

  • Can I cancel my enrollment? Will I get a refund?

    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.

  • Who provides the certification?

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

  • Are there any group discounts for classroom training programs?

    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.

  • What are the payment options?

    Payments can be made using any of the following options and a receipt of the same will be issued to you automatically via email.
    1. Visa Debit/credit Card
    2. American Express and Diners Club Card
    3. Master Card, Or
    4. PayPal

  • I want to know more about the training program. Whom do I contact?

    Please join our Live Chat for instant support, call us, or Request a Call Back to have your query resolved.

  • What is the Expert Assistant Support provided by Simplilearn?

    Expert Assistance:

    • 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 on forum regarding technical concepts, projects and case-studies.
    Teaching Assistance:
    • Project Assistance: Queries related to solving & completing Projects, case-studies which are part of Data Scientist with R programming course offered by Simplilearn
    • Technical Assistance: Queries related to technical, installation, administration issues in Data Scientist with R programming training. In case of critical issues, support will be rendered through a remote desktop.
    • R Programming: Queries related to R programming while solving & completing Projects, case-studies which are part of the Data Scientist Certification offered by Simplilearn.
    How to avail the Support?
    To avail Support, submit a query to Simplilearn through any of following channels of Simplilearn’s Help & Support team. A Teaching Assistant will get in touch with you to assist with query resolution within 48 hours.

    Help & Support
    Simplitalk
    Live Chat

  • Who are our Faculties and how are they selected?

    All our trainers are working professionals and industry experts with at least 10-12 years of relevant teaching experience.

    Each of them have 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.

  • What is Global Teaching Assistance?

    Our teaching assistants are here to help you get certified in your first attempt.

    They are a dedicated team of subject matter experts to help you at every step and enrich your learning experience from class onboarding to project mentoring and job assistance.

    They engage with the students proactively to ensure the course path is followed.

    Teaching Assistance is available during business hours.

  • What is covered under the 24/7 Support promise?

    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.

Contact Us

+1-844-532-7688

(Toll Free)

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  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
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