World’s #1 Online Bootcamp

Course Overview

Key Features

  • 24 hours of self-paced learning videos
  • 4 real-life industry projects on customer segmentation, macro calls, attrition, and retail analysis
  • Learn SAS Macros and PROC SQL
  • Includes advanced statistical concepts like linear and logistic regression, cluster analysis, and forecasting
  • Includes a free SAS Base Programmer course
  • Lifetime access to self-paced learning *

Training Options

Self-Paced Learning

$ 748

  • Lifetime access to high-quality self-paced e-learning content curated by industry experts
  • 24x7 learner assistance and support

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Course Curriculum

Course Content

  • Data Science with SAS

    Preview

    • Lesson 00 - Course Introduction

      04:21Preview
      • 0.001 Introduction
        04:21

    • Lesson 01 - Analytics Overview

      11:51Preview
      • 1.001 Introduction
        00:55
      • 1.002 Introduction to Business Analytics
        02:04
      • Types of Analytics
        02:39
      • 1.004 Areas of Analytics
        02:46
      • 1.005 Analytical Tools
        00:50
      • Analytical Techniques
        01:46
      • 1.7 Quiz
      • 1.008 Key Takeaways
        00:51

    • Lesson 02 - Introduction to SAS

      19:43Preview
      • 2.001 Introduction
        00:40
      • 2.002 What is SAS
        02:34
      • 2.003 Navigating in the SAS Console
        01:47
      • 2.004 SAS Language Input Files
        01:55
      • Data Step
        00:56
      • 2.006 PROC Step and DATA Step - Example
        01:44
      • 2.007 DATA Step Processing
        03:51
      • 2.008 SAS Libraries
        03:00
      • 2.009 Demo - Importing Data
        01:15
      • 2.010 Demo - Exporting Data
        00:59
      • Knowledge Check
      • 2.13 Quiz
      • 2.014 Key Takeaways
        01:02

    • Lesson 03 - Combining and Modifying Datasets

      36:04Preview
      • 3.001 Introduction
        00:29
      • 3.002 Why Combine or Modify Data
        00:55
      • 3.003 Concatenating Datasets
        08:14
      • 3.004 Interleaving Method
        03:05
      • 3.006 One - to - one Reading
        03:09
      • 3.007 One - to - one Merging
        02:57
      • Knowledge Check
      • 3.009 Data Manipulation
        06:51
      • 3.010 Modifying Variable Attributes
        03:57
      • 3.012 Assignment 1 Solution
        01:04
      • Assignment
        00:23
      • 3.014 Assignment 2 Solution
        03:50
      • 3.16 Quiz
      • 3.017 Key Takeaways
        00:39

    • Lesson 04 - PROC SQL

      25:54Preview
      • 4.001 Introduction
        00:35
      • 4.002 What is PROC SQL
        01:56
      • Retrieving Data from a Table
        02:07
      • 4.004 Demo - Retrieve Data from a Table
        01:44
      • 4.006 Selecting Columns in a Table
        04:28
      • Knowledge Check
      • 4.008 Retrieving Data from Multiple Tables
        00:50
      • 4.009 Selecting Data from Multiple Tables
        03:36
      • 4.010 Concatenating Query Results
        02:28
      • 4.013 Assignment 1 Solution
        01:47
      • Assignment
        01:30
      • 4.015 Assignment 2 Solution
        02:13
      • 4.16 Quiz
      • 4.017 Key Takeaways
        01:05

    • Lesson 05 - SAS Macros

      19:16Preview
      • 5.001 Introduction
        00:41
      • 5.002 Need for SAS Macros
        04:39
      • 5.003 Macro Functions
        01:41
      • 5.004 Macro Functions Examples
        03:03
      • 5.005 SQL Clauses for Macros
        00:59
      • Knowledge Check
      • 5.007 The Macro Statement
        01:27
      • 5.008 The Conditional Statement
        01:24
      • Assignment
        01:09
      • 5.011 Assignment Solution
        03:29
      • 5.12 Quiz
      • 5.013 Key Takeaways
        00:44

    • Lesson 06 - Basics of Statistics

      23:23Preview
      • 6.001 Introduction
        00:42
      • 6.002 Introduction to Statistics
        02:31
      • 6.003 Statistical Terms
        02:29
      • 6.004 Procedures in SAS for Descriptive Statistics
        02:04
      • 6.005 Demo - Descriptive Statistics
        01:10
      • 6.007 Hypothesis Testing
        01:56
      • 6.008 Variable Types
        01:56
      • Hypothesis Testing - Process
        02:01
      • Knowledge Check
      • 6.011 Demo - Hypothesis Testing
        01:45
      • 6.012 Parametric and Non - parametric Tests
        00:51
      • 6.013 Parametric Tests
        03:05
      • 6.014 Non - parametric Tests
        00:46
      • 6.015 Parametric Tests - Advantages and Disadvantages
        01:10
      • 6.16 Quiz
      • 6.017 Key Takeaways
        00:57

    • Lesson 07 - Statistical Procedures

      33:21Preview
      • 7.001 Introduction
        00:44
      • 7.002 Statistical Procedures
        00:27
      • 7.003 PROC Means
        01:12
      • 7.004 PROC Means - Examples
        04:05
      • 7.006 PROC FREQ
        01:56
      • 7.007 Demo - PROC FREQ
        01:23
      • 7.008 PROC UNIVARIATE
        02:16
      • 7.009 Demo - PROC UNIVARIATE
        01:27
      • Knowledge Check
      • 7.011 PROC CORR
        01:21
      • Proc Corr Options
        00:57
      • 7.013 Demo - PROC CORR
        02:21
      • 7.014 PROC REG
        01:14
      • Proc Reg Options
        00:34
      • 7.016 Demo - PROC REG
        01:43
      • 7.018 PROC ANOVA
        01:30
      • 7.019 Demo - PROC ANOVA
        02:55
      • 7.022 Assignment 1 Solution
        02:36
      • Assignment
        01:03
      • 7.024 Assignment 2 Solution
        01:08
      • 7.25 Quiz
      • 7.026 Key Takeaways
        00:55

    • Lesson 08 - Data Exploration

      21:46Preview
      • 8.001 Introduction
        00:41
      • 8.002 Data Preparation
        02:15
      • 8.003 General Comments and Observations on Data Cleaning
        00:43
      • Knowledge Check
      • 8.005 Data Type Conversion
        04:39
      • Character Functions
        01:37
      • 8.007 SCAN Function
        01:17
      • 8.008 DateTime Functions
        01:52
      • 8.009 Missing Value Treatment
        01:50
      • Various Functions to Handle Missing Value
        01:06
      • 8.011 Data Summarization
        01:22
      • Assignment
        01:13
      • 8.013 Assignment Solution
        02:23
      • 8.14 Quiz
      • 8.015 Key Takeaways
        00:48

    • Lesson 09 - Advanced Statistics

      30:32Preview
      • 9.001 Introduction
        00:41
      • 9.002 Introduction to Cluster
        03:30
      • Clustering Methodologies
        01:47
      • Demo - Clustering Method
        03:07
      • 9.005 K Means Clustering
        02:06
      • Knowledge Check
      • 9.007 Decision Tree
        04:01
      • 9.008 Regression
        04:47
      • 9.009 Logistic Regression
        04:06
      • 9.011 Assignment 1 Solution
        01:44
      • Assignment
        00:51
      • 9.013 Assignment 2 Solution
        01:48
      • 9.14 Quiz
      • 9.015 Key Takeaways
        00:51

    • Lesson 10 - Working with Time Series Data

      27:25Preview
      • 10.001 Introduction
        00:45
      • 10.002 Need for Time Series Analysis
        03:43
      • Time Series Analysis - Options
        01:57
      • 10.004 Reading Date and Datetime Values
        02:47
      • 10.006 White Noise Process
        03:57
      • 10.007 Stationarity of a Time Series
        03:21
      • Knowledge Check
      • 10.009 Demo — Stages of ARIMA Modelling
        05:47
      • Plot Transform Transpose and Interpolating Time Series Data
        01:05
      • 10.012 Assignment Solution
        02:09
      • 10.13 Quiz
      • 10.014 Key Takeaways
        00:54
      • Assignment
        01:00

    • Lesson 11 - Designing Optimization Models

      18:59Preview
      • 11.001 Introduction
        00:36
      • 11.002 Need for Optimization
        02:32
      • 11.003 Optimization Problems
        02:52
      • 11.004 PROC OPTMODEL
        04:18
      • Optimization - Example
        02:26
      • Assignment
        01:30
      • 11.008 Assignment Solution
        00:32
      • 11.9 Quiz
      • 11.010 Key Takeaways
        00:57
  • Free Course

  • Certified SAS Base Programmer

    Preview

    • Lesson 00 - Course Introduction

      04:35Preview
      • 0.1 Introduction
        04:35

    • Lesson 01 - Introduction to SAS Base Program

      01:01:45Preview
      • 1.1 Introduction
        00:57
      • 1.2 SAS Installation and Access
        01:51
      • 1.3 Opening SAS University Edition
        03:05
      • 1.4 SAS Input Statements
        02:15
      • 1.5 DATA Step Statement
        01:18
      • 1.6 Reading Data
        05:04
      • 1.7 Options Available in the Input Statement
        05:43
      • 1.8 SAS Libraries
        02:38
      • 1.10 Combining Datasets
        01:17
      • 1.11 Concatenating Datasets
        08:07
      • 1.12 Interleaving Method
        03:13
      • 1.14 One-to-One Reading
        03:16
      • 1.15 One-to-One Merging
        03:14
      • 1.17 Data Manipulation
        00:53
      • 1.18 Delete and Group Observations
        04:52
      • 1.19 Modifying Variable Attributes
        03:54
      • 1.20 Access Excel Workbook
        02:54
      • 1.22 Assignment 1 Solution
        02:33
      • Assignment
        00:39
      • 1.24 Assignment 2 Solution
        01:31
      • Knowledge Check
      • 1.25 Quiz
      • 1.26 Key Takeaways
        01:14

    • Lesson 02 - Creating Data Structures

      18:45Preview
      • 2.1 Introduction
        00:47
      • 2.2 SAS Dataset
        03:04
      • 2.4 Create and Manipulate SAS Date Values
        02:52
      • 2.5 YearCutOff Option
        02:48
      • 2.6 Export SAS Dataset
        03:02
      • 2.7 Controlling Observation and Variables
        02:24
      • Activity
        00:31
      • Activity Exercise
      • Assignment
        01:00
      • 2.10 Assignment Solution
        01:18
      • Knowledge Check
      • 2.11 Quiz
      • 2.12 Key Takeaways
        00:59

    • Lesson 03 - Managing Data

      36:15Preview
      • 3.1 Introduction
        00:51
      • 3.2 Proc Contents
        01:45
      • 3.3 Proc Datasets
        03:32
      • 3.4 Proc Sort
        01:28
      • 3.6 Loop Statements
        08:46
      • 3.7 Data Type Conversion
        05:17
      • Character Functions
        01:53
      • 3.9 SCAN function
        01:26
      • 3.10 Date Time Functions - Example
        03:05
      • 3.12 SAS Arrays
        03:36
      • Assignment
        01:01
      • 3.14 Assignment Solution
        02:27
      • Knowledge Check
      • 3.15 Quiz
      • 3.16 Key Takeaways
        01:08

    • Lesson 04 - Generating Reports

      30:28Preview
      • 4.1 Introduction
        00:40
      • 4.2 Need for Reports
        03:21
      • 4.3 Proc Print
        04:30
      • 4.5 PROC Means
        04:09
      • 4.7 Proc Freq
        03:06
      • 4.8 Proc Univariate
        04:01
      • 4.10 Proc Report
        01:48
      • 4.11 Output Delivery System (ODS)
        03:51
      • Activity
        00:19
      • Activity Exercise
      • Assignment
        01:16
      • 4.14 Assignment Solution
        02:19
      • Knowledge Check
      • 4.15 Quiz
      • 4.16 Key Takeaways
        01:08

    • Lesson 05 - Handling Errors

      13:22Preview
      • 5.1 Introduction
        00:42
      • 5.2 Errors in SAS Program
        01:34
      • 5.3 Logical Errors
        04:44
      • 5.4 Syntax Errors
        03:25
      • 5.5 Data Errors
        01:47
      • Activity
        00:21
      • Activity Exercise
      • 5.7 Quiz
      • 5.8 Key Takeaways
        00:49

    • Project

      03:57Preview
      • Generate Descriptive Analytics Report 01
        00:47
      • Project Solution 01
        03:10

    • Course Feedback

      • Course Feedback
  • Free Course

  • Math Refresher

    Preview

    • Lesson 01: Course Introduction

      06:23Preview
      • 1.01 About Simplilearn
        00:28
      • 1.02 Introduction to Mathematics
        01:18
      • 1.03 Types of Mathematics
        02:39
      • 1.04 Applications of Math in Data Industry
        01:17
      • 1.05 Learning Path
        00:25
      • 1.06 Course Components
        00:16

    • Lesson 02: Probability and Statistics

      32:38Preview
      • 2.01 Learning Objectives
        00:29
      • 2.02 Basics of Statistics and Probability
        03:08
      • 2.03 Introduction to Descriptive Statistics
        02:12
      • 2.04 Measures of Central Tendencies​
        04:50
      • 2.05 Measures of Asymmetry
        02:24
      • 2.06 Measures of Variability​
        04:55
      • 2.07 Measures of Relationship​
        05:22
      • 2.08 Introduction to Probability
        08:36
      • 2.09 Key Takeaways
        00:42
      • 2.10 Knowledge check

    • Lesson 03: Coordinate Geometry

      06:31Preview
      • 3.01 Learning Objectives
        00:35
      • 3.02 Introduction to Coordinate Geometry​
        03:16
      • 3.03 Coordinate Geometry Formulas​
        01:51
      • 3.04 Key Takeaways
        00:49
      • 3.05 Knowledge Check

    • Lesson 04: Linear Algebra

      29:53Preview
      • 4.01 Learning Objectives
        00:29
      • 4.02 Introduction to Linear Algebra
        03:21
      • 4.03 Forms of Linear Equation
        05:21
      • 4.04 Solving a Linear Equation
        05:21
      • 4.05 Introduction to Matrices
        02:05
      • 4.06 Matrix Operations
        07:07
      • 4.07 Introduction to Vectors
        01:00
      • 4.08 Types and Properties of Vectors
        01:52
      • 4.09 Vector Operations
        02:39
      • 4.10 Key Takeaways
        00:38
      • 4.11 Knowledge Check

    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      08:56Preview
      • 5.01 Learning Objectives
        00:29
      • 5.02 Eigenvalues
        01:19
      • 5.03 Eigenvectors
        04:09
      • 5.04 Eigendecomposition
        02:21
      • 5.05 Key Takeaways
        00:38
      • 5.06 Knowledge Check

    • Lesson 06: Introduction to Calculus

      09:47
      • 6.01 Learning Objectives
        00:30
      • 6.02 Basics of Calculus
        01:20
      • 6.03 Differential Calculus
        03:01
      • 6.04 Differential Formulas
        01:01
      • 6.05 Integral Calculus
        02:33
      • 6.06 Integration Formulas
        00:47
      • 6.07 Key Takeaways
        00:35
      • 6.08 Knowledge Check
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 01: Course Introduction

      07:05Preview
      • 1.01 Course Introduction
        05:19
      • 1.02 What Will You Learn
        01:46

    • Lesson 02: Introduction to Statistics

      25:49Preview
      • 2.01 Learning Objectives
        01:16
      • 2.02 What Is Statistics
        01:50
      • 2.03 Why Statistics
        02:06
      • 2.04 Difference between Population and Sample
        01:20
      • 2.05 Different Types of Statistics
        02:42
      • 2.06 Importance of Statistical Concepts in Data Science
        03:20
      • 2.07 Application of Statistical Concepts in Business
        02:11
      • 2.08 Case Studies of Statistics Usage in Business
        03:09
      • 2.09 Applications of Statistics in Business: Time Series Forecasting
        03:50
      • 2.10 Applications of Statistics in Business Sales Forecasting
        03:19
      • 2.11 Recap
        00:46

    • Lesson 03: Understanding the Data

      17:29Preview
      • 3.01 Learning Objectives
        01:12
      • 3.02 Types of Data in Business Contexts
        02:11
      • 3.03 Data Categorization and Types of Data
        03:13
      • 3.03 Types of Data Collection
        02:14
      • 3.04 Types of Data
        02:01
      • 3.05 Structured vs. Unstructured Data
        01:46
      • 3.06 Sources of Data
        02:17
      • 3.07 Data Quality Issues
        01:38
      • 3.08 Recap
        00:57

    • Lesson 04: Descriptive Statistics

      34:51Preview
      • 4.01 Learning Objectives
        01:26
      • 4.02 Descriptive Statistics
        02:03
      • 4.03 Mathematical and Positional Averages
        03:15
      • 4.04 Measures of Central Tendancy: Part A
        02:17
      • 4.05 Measures of Central Tendancy: Part B
        02:41
      • 4.06 Measures of Dispersion
        01:15
      • 4.07 Range Outliers Quartiles Deviation
        02:30
      • 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
        03:37
      • 4.09 Z Score and Empirical Rule
        02:14
      • 4.10 Coefficient of Variation and Its Application
        02:06
      • 4.11 Measures of Shape
        02:39
      • 4.12 Summarizing Data
        02:03
      • 4.13 Recap
        00:54
      • 4.14 Case Study One: Descriptive Statistics
        05:51

    • Lesson 05: Data Visualization

      23:36Preview
      • 5.01 Learning Objectives
        00:57
      • 5.02 Data Visualization
        02:15
      • 5.03 Basic Charts
        01:52
      • 5.04 Advanced Charts
        02:19
      • 5.05 Interpretation of the Charts
        02:57
      • 5.06 Selecting the Appropriate Chart
        02:25
      • 5.07 Charts Do's and Dont's
        02:47
      • 5.08 Story Telling With Charts
        01:29
      • 5.09 Data Visualization: Example
        02:41
      • 5.10 Recap
        00:50
      • 5.11 Case Study Two: Data Visualization
        03:04

    • Lesson 06: Probability

      21:51Preview
      • 6.01 Learning Objectives
        00:55
      • 6.02 Introduction to Probability
        03:10
      • 6.03 Probability Example
        02:02
      • 6.04 Key Terms in Probability
        02:25
      • 6.05 Conditional Probability
        02:11
      • 6.06 Types of Events: Independent and Dependent
        02:59
      • 6.07 Addition Theorem of Probability
        01:58
      • 6.08 Multiplication Theorem of Probability
        02:08
      • 6.09 Bayes Theorem
        03:10
      • 6.10 Recap
        00:53

    • Lesson 07: Probability Distributions

      24:45Preview
      • 7.01 Learning Objectives
        00:52
      • 7.02 Probability Distribution
        01:25
      • 7.03 Random Variable
        02:21
      • 7.04 Probability Distributions Discrete vs.Continuous: Part A
        01:44
      • 7.05 Probability Distributions Discrete vs.Continuous: Part B
        01:45
      • 7.06 Commonly Used Discrete Probability Distributions: Part A
        03:18
      • 7.07 Discrete Probability Distributions: Poisson
        03:16
      • 7.08 Binomial by Poisson Theorem
        02:28
      • 7.09 Commonly Used Continuous Probability Distribution
        03:22
      • 7.10 Application of Normal Distribution
        02:49
      • 7.11 Recap
        01:25

    • Lesson 08: Sampling and Sampling Techniques

      36:45Preview
      • 8.01 Learnning Objectives
        00:51
      • 8.02 Introduction to Sampling and Sampling Errors
        03:05
      • 8.03 Advantages and Disadvantages of Sampling
        01:31
      • 8.04 Probability Sampling Methods: Part A
        02:32
      • 8.05 Probability Sampling Methods: Part B
        02:27
      • 8.06 Non-Probability Sampling Methods: Part A
        01:42
      • 8.07 Non-Probability Sampling Methods: Part B
        01:25
      • 8.08 Uses of Probability Sampling and Non-Probability Sampling
        02:08
      • 8.09 Sampling
        01:08
      • 8.10 Probability Distribution
        02:53
      • 8.11 Theorem Five Point One
        00:52
      • 8.12 Center Limit Theorem
        02:14
      • 8.13 Sampling Stratified: Sampling Example
        04:35
      • 8.14 Probability Sampling: Example
        01:17
      • 8.15 Recap
        01:07
      • 8.16 Case Study Three: Sample and Sampling Techniques
        05:16
      • 8.17 Spotlight
        01:42

    • Lesson 09: Inferential Statistics

      37:08Preview
      • 9.01 Learning Objectives
        01:04
      • 9.02 Inferential Statistics
        03:09
      • 9.03 Hypothesis and Hypothesis Testing in Businesses
        03:24
      • 9.04 Null and Alternate Hypothesis
        01:44
      • 9.05 P Value
        03:22
      • 9.06 Levels of Significance
        01:16
      • 9.07 Type One and Two Errors
        01:37
      • 9.08 Z Test
        02:24
      • 9.09 Confidence Intervals and Percentage Significance Level: Part A
        02:52
      • 9.10 Confidence Intervals: Part B
        01:20
      • 9.11 One Tail and Two Tail Tests
        04:43
      • 9.12 Notes to Remember for Null Hypothesis
        01:02
      • 9.13 Alternate Hypothesis
        01:51
      • 9.14 Recap
        00:56
      • 9.15 Case Study 4: Inferential Statistics
        06:24
      • Hypothesis Testing

    • Lesson 10: Application of Inferential Statistics

      27:20Preview
      • 10.01 Learning Objectives
        00:50
      • 10.02 Bivariate Analysis
        02:01
      • 10.03 Selecting the Appropriate Test for EDA
        02:29
      • 10.04 Parametric vs. Non-Parametric Tests
        01:54
      • 10.05 Test of Significance
        01:38
      • 10.06 Z Test
        04:27
      • 10.07 T Test
        00:54
      • 10.08 Parametric Tests ANOVA
        03:26
      • 10.09 Chi-Square Test
        02:31
      • 10.10 Sign Test
        01:58
      • 10.11 Kruskal Wallis Test
        01:04
      • 10.12 Mann Whitney Wilcoxon Test
        01:18
      • 10.13 Run Test for Randomness
        01:53
      • 10.14 Recap
        00:57

    • Lesson 11: Relation between Variables

      20:07Preview
      • 11.01 Learning Objectives
        01:06
      • 11.02 Correlation
        01:54
      • 11.03 Karl Pearson's Coefficient of Correlation
        02:36
      • 11.04 Karl Pearsons: Use Cases
        01:30
      • 11.05 Correlation Example
        01:59
      • 11.06 Spearmans Rank Correlation Coefficient
        02:14
      • 11.07 Causation
        01:47
      • 11.08 Example of Regression
        02:28
      • 11.09 Coefficient of Determination
        01:12
      • 11.10 Quantifying Quality
        02:29
      • 11.11 Recap
        00:52

    • Lesson 12: Application of Statistics in Business

      17:25Preview
      • 12.01 Learning Objectives
        00:53
      • 12.02 How to Use Statistics In Day to Day Business
        03:29
      • 12.03 Example: How to Not Lie With Statistics
        02:34
      • 12.04 How to Not Lie With Statistics
        01:49
      • 12.05 Lying Through Visualizations
        02:15
      • 12.06 Lying About Relationships
        03:31
      • 12.07 Recap
        01:06
      • 12.08 Spotlight
        01:48

    • Lesson 13: Assisted Practice

      11:47
      • Assisted Practice: Problem Statement
        02:10
      • Assisted Practice: Solution
        09:37

Contact Us

+65 31 585082

Exam & Certification

  • How do I get certified?

    To become a Certified Data Scientist with SAS, you must fulfill the following criteria:
    • Complete any one project out of the two provided in the course. Submit the deliverables in the LMS to be evaluated by our lead trainer
    • Score a minimum of 60% in any one of the three simulation tests
    Please Note:
    • When you have completed the course, you will receive a three-month experience certificate for implementing the projects using SAS
    • It is mandatory that you fulfill both the criteria: completion of any one project and passing the online exam with minimum score of 60% to become a certified data scientist

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

    Online Classroom:
    • Attend one complete batch.
    • Complete one project and one simulation test with a minimum score of 60%.
    Online Self-Learning:
    • Complete 85% of the course.
    • Complete one project and one simulation test with a minimum score of 60%.

  • What are the eligibility requirements for this SAS training course?

    There are no eligibility requirements for this Data Science with SAS training course. No prior experience in data analytics or statistics is required to take this online training course.

  • How much does this online course cost?

    The course is priced at INR 9999 for online self-guided learning (OSL)  and INR 21,999 or $599 for the live virtual classroom (LVC).

  • Is this course accredited?

    No, this course is not officially accredited.

  • How do I pass the Data Science with SAS Exam?

    To pass the examination, you must have a minimum score of 60 (out of 100) in one of the simulation test papers, and you must complete a project successfully.

  • How long does it take to complete this online training course?

    It will take you approximately 98 hours to complete both the OSL and LVC training modes of this online training course.

  • How many attempts do I have to pass the Data Science with SAS exam?

    You have a maximum of two total attempts to pass the exam. You may re-attempt it immediately if you fail the first time.

  • How long does it take to receive the Data Science with SAS course certification?

    Upon successful completion of Simplilearn’s Data Science with SAS online training, you will immediately receive the Data Science with SAS course completion certificate.

  • How long is the Data Science with SAS certification valid for?

    The Data Science with SAS certification from Simplilearn has lifelong validity.

  • Do you offer a money-back guarantee for the training program?

    Yes. We do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support portal.

  • How can I learn more about this training program?

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

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this free SAS Certification Exam Practice Test to understand the type of tests that are part of the course curriculum.

Reviews

  • Vivek Sharma

    Vivek Sharma

    Business Consulting & Technology at Grant Thornton LLP, New York City

    I have done Data Science with SAS training certification from Simplilearn. The course content was very useful and industry appropriate. The support team was friendly and was always there to guide me. This certification helped me achieve my goals. Thanks Simplilearn.

  • Mahesh Sukumaran

    Mahesh Sukumaran

    Chennai

    Thanks to the trainer, Mr. Sam, for delivering an excellent session. He covered topics with real-time examples and also provided a hands-on experience during the sessions. Happy to be a part of your batch, Mr. Sam.

  • Rashmi Pal

    Rashmi Pal

    Bangalore

    The course content is excellent. You can easily learn and understand, even if you are a beginner. The instructors have good knowledge about the subject. Self-learning videos help a lot. I am delighted to have joined and successfully finished the 'data science' course, all thanks to Simplilearn.

  • Shyam Sunder

    Shyam Sunder

    President at Iwcl, Hyderabad

    The training was excellent. The trainer was well paced and very flexible. He was highly competent. The content was rich. I really enjoyed the training from Simplilearn.

  • Praveen Kumar

    Praveen Kumar

    Product Design & Development at Mytrah Energy, Hyderabad

    Everything about this course is perfect. The material is good. This certification helped to boost my career goals. I highly recommend Simplilearn.

  • Namita Das

    Namita Das

    Associate at JPMorgan Chase & Co., Mumbai

    I had enrolled with Simplilearn for Data Science with SAS course (self-learning). The content provided was very good and the explanation provided were simple to understand. I also got frequent meeting invites from Simplilearn to clear any doubts on project submission. Great way to add stars to your resume by taking up the certifications.

  • Frado Sibarani

    Frado Sibarani

    Senior IT Specialist at IBM, Singapore

    Good course and material. It has installation steps, demo, and real-life study case.

  • Naveen N

    Naveen N

    Student at Xavier Institute Of Management and Enterpreneurship, Sivakasi

    Course was very good and I learned everything about data analysis from nothing. You can learn everything about SAS and Excel from this course.

  • Deepak Subramanian

    Deepak Subramanian

    Sr.Engagement Manager at Capgemini, Chennai

    This exposure on SAS and Excel is a must before taking advanced course in analytics.

  • Madhavi Sistla

    Madhavi Sistla

    Technical Architect at Cognizant Technology Solutions, Hyderabad

    The course was amazing. The trainer had a very good knowledge about the course. She was very clear in her explanations and engaging. She answered our questions patiently. These online classes made it easier to understand the concepts. Thank you Simplilearn!

  • Vipindas Mungath

    Vipindas Mungath

    Assistant Manager, IT Division at IPS Securex Pte Ltd, Singapore

    I have enrolled in Simplilearn's Data Science certification. The quality of the material was extremely superior. The trainers are really helpful. The way he explains using real-life examples keep the team engaged. He always helped us in solving the queries.

  • Deepak Sharma

    Deepak Sharma

    Account Delivery Manager at DXC Technology, Bangalore

    Simplilearn's Data Science certification is awesome. The basics are covered very nicely keeping in mind different students. Thank you Simplilearn.

  • Kiran Dash

    Kiran Dash

    Data Warehousing at MindTree, Bangalore

    The session was nice. The course orientation and topic insight was very informative. I would definitely suggest this R programming to my colleagues. Thanks to the trainer for making this course so interesting.

prevNext

Why Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

FAQs

  • What are the System Requirements?

    To run SAS, your need to download and install the SAS university edition from https://www.sas.com/en_us/software/on-demand-for-academics.html

  • What are the modes of training offered for this course?

    We offer this SAS training in the following modes:

    • Live Virtual Classroom or Online Classroom: In online classroom training, you can attend the SAS course remotely from your desktop via video conferencing. This format saves time and reduces the time spent away from work or home.
    • Online Self-Learning: In this mode, you can go through the lecture videos at your convenience.

  • What if I miss a class?

    We provide recordings of each class after the session is conducted. If you miss a class, you can go through the recordings before the next session.

  • Can I cancel my enrolment? Do I get a refund?

    Yes, you can cancel your enrolment. We provide a complete refund after deducting the administration fee. To know more, please go through our Refund Policy.

  • Who provides the certification?

    At the end of the SAS training, after satisfactory evaluation of the project and after passing the online exam (minimum 75%), you will receive a certificate from Simplilearn stating that you are a Certified Data Scientist with SAS.

  • Are there any group discounts for classroom training programs?

    Yes, we have group discount packages for online classroom training programs. Contact Help and Support to learn more about group discounts.

  • What payment options are available?

    Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diner’s Club
    • PayPal

  • Who are our Faculties and how are they selected?

    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 continue to train for us.

  • What is Global Teaching Assistance?

    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 for this Data Science with SAS training course.

  • 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 SAS training course with us to discuss Data Science with SAS related topics.

  • What is online classroom training?

    All of the classes are conducted via live online streaming. They are interactive sessions that enable you to ask questions and participate in discussions during class time.

  • Is this live training or will I watch pre-recorded videos?

    If you enroll for self-paced e-learning, you will have access to pre-recorded videos. If you enroll for the online classroom Flexi-Pass, you will have access to live training conducted online as well as the pre-recorded videos.

  • Are the training and course material effective in preparing me for the Data Science with SAS exam?

    Yes, Simplilearn’s training and course materials guarantee success with the Data Science with SAS certification exam.

  • * Disclaimer

    * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Our Riyadh Correspondence / Mailing address

Simplilearn's Data Science with SAS Certification Training in Riyadh, Saudi Arabia

Nimr Al Nakheel Centre, Building A 1st floor, Imam Saud Bin Abdulaziz Bin Muhammad Road Riyadh Saudi Arabia

View Location

Related Programs

Find Data Science & Business Analytics Programs in Riyadh

Post Graduate Program in Data SciencePost Graduate Program in Data AnalyticsData ScientistData AnalystApplied Data Science with Python
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.