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

₹ 19,999

  • 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
    • Math Refresher

      30:36Preview
      • Math Refresher
        30:36
  • Free Course
  • Statistics Essential for Data Science

    Preview
    • Lesson 1 Introduction

      02:55Preview
      • 1.1 Introduction
        02:55
    • Lesson 2 Sample or population data

      03:56Preview
      • 2.1 Sample or population data
        03:56
    • Lesson 3 The fundamentals of descriptive statistics

      21:18Preview
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06
    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17Preview
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47
    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30
    • Lesson 6 Distributions

      16:17Preview
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19
    • Lesson 7 Estimators and Estimates

      23:36Preview
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38
    • Lesson 8 Confidence intervals advanced topics

      14:27Preview
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24
    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37
    • Lesson 10 Hypothesis testing Introduction

      12:36Preview
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20
    • Lesson 11 Hypothesis testing Let's start testing!

      26:39Preview
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48
    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31
    • Lesson 13 The fundamentals of regression analysis

      18:32Preview
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36
    • Lesson 14 Subtleties of regression analysis

      23:25Preview
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00
    • Lesson 15 Assumptions for linear regression analysis

      19:16Preview
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22
    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20
    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42

Course Advisor

  • Ronald van Loon

    Ronald van Loon

    Top 10 Big Data and Data Science Influencer, Director - Adversitement

    Named by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of 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.

prevNext

Exam & Certification

  • How do I get certified?

     

    It is compulsory for the candidates to meet the following conditions to become a Certified Data Scientist with SAS:

    • Complete one project and get it assessed by the lead trainer by submitting the deliverables in the LMS.
    • Attempt and one simulation test out of the three and achieve at least 60% passing score.

    Please Note:

    Simplilearn awards a three-month experience certificate for using SAS in the project implementation after a candidate completes the course successfully.

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

     

    For Self-paced learning mode, candidates need to finish 85% of the course along with one assigned project and one simulation test achieving a 60% passing score.

    For Online Classroom mode, candidates need to attend one complete batch and submit one assigned project and one simulation test achieving a 60% passing score.

  • What are the prerequisites for learning SAS ?

     

    The Data Science with SAS training course requires no previous understanding of data statistics or analytics. Candidates with keen interest in Data Science can enroll for this course.

  • How much does this online course cost?

     

    The candidates need to pay INR 9999 for online self-guided learning (OSL). If they enroll in the live virtual classroom (LVC) then an amount of INR 21,999 or $599  is to be paid.

  • Is this course accredited?

     

    No, the Data Science with SAS course in Mumbai is not officially accredited.

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

    Candidates need to complete one project and qualify one of the simulation tests with at least 60 (out of 100) passing score to pass the Data Science with SAS Exam.

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

     

    Simplilearn offers Online Self-Learning and Live Classroom training modes for the Data Science with SAS course. Both of the modes require nearly 98 hours to be completed.

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

    The candidates are allowed to appear for the Data Science with SAS exam twice. The next attempt can be taken immediately if the candidate fails in his first attempt.

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

    Simplilearn’s Data Science with SAS course completion certificate is awarded to the candidates who complete the training program of Data Science with SAS satisfactorily.

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

    Simplilearn’s Data Science with SAS certification does not need renewal. It is valid for a lifetime

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

     

    Yes, the training programs offered by Simplilearn comes with a money-back guarantee. Candidates need to go through the Refund Policy and then visit our Help and Support portal to generate the refund request.

  • How can I learn more about this training program?

     

    Simplilearn provides the options of Contact Us form and Live Chat for the candidates to get in touch with the customer service representatives and learn more about the Data Science with SAS training course in Mumbai.

  • 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

  • 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.

  • 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.

  • 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!

  • 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.

FAQs

  • What is the average salary for a Data Scientist in Mumbai?

     

    According to Payscale, a median salary of Rs 715,000 per year can be earned by Data Scientists in Mumbai. By taking the Data Science with SAS Training program, there can be considerable improvement in the salary drawn by the employee.

  • What are the System Requirements?

     

    Candidates need to visit the following page and install the SAS university edition to run SAS in their system:

    http://www.sas.com/en_us/software/university-edition.html

    To run SAS, you need to download and install the SAS university edition from

    http://www.sas.com/en_us/software/university-edition.html

     

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

    The SAS training offered by Simplilearn involves two training methods:

    • Online Self-Learning: This mode involves pre-recorded videos which the candidates can use for learning at their own comfort.

    • Live Virtual Classroom: These are live interactive sessions where the candidates can communicate with the trainers through video conferencing and get their queries resolved. This learning mode gives the option of better time management and improves the learning experience.

     

  • What if I miss a class?

     

    Simplilearn makes sure that the candidate’s learning is not affected by missing a class. For this, recordings of each class are provided to the learners for reference before the next class.

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

     

    Yes, candidates are allowed to cancel their enrollment. After deduction the administration fee, a complete refund will be provided. Read our Refund Policy to know the details.

  • Who provides the certification?

    Simplilearn awards a certificate and acknowledges the candidate as a Certified Data Scientist with SAS when he:

    • Submit the project after completing the training and getting it assessed
    • Clears the online exam with a passing score of 75%

  • Are there any group discounts for classroom training programs?

    Yes, Simplilearn provides group discounts for its training programs. Our support team can be contacted for further details via our Help and Support option.

  • What payment options are available?

     

    Candidates need to pay online to get themselves enrolled in our Data Science with SAS course in Mumbai. The payment options available are:

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

    A receipt will be sent to the candidate via email once the payment is successful.

  • Who are our Faculties and how are they selected?

     

    Simplilearn appoints faculty who have high alumni rating and those with in-depth domain knowledge. The selection process is difficult and incorporates stages like profile screening, technical assessment, and training demo. Trainers even after completing the selection process should have a proven teaching experience of 12+ years to become the faculty at Simplilearn.

  • What is Global Teaching Assistance?

    We always strive to provide the best teaching assistance so candidates get certified in their first attempt. The subject matter experts engage participants in the proper flow of the course learning. From class onboarding to project mentoring, our trainers try to enhance your learning experience. Teaching assistance for the Data Science with SAS training course is available in working hours.

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

    Simplilearn guarantees to provide 24/7 customer support through calls, emails, or chat options. We have also created a community forum with lifetime access where candidates can get the on-demand assistance regarding the topics of Data Science with SAS.

  • What is online classroom training?

     

     

    The online training classroom refers to the live training sessions conducted via high-quality video conferencing as part of the Data Science with SAS training program. During the sessions, the candidates can talk to the mentors and get their queries resolved.

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

    For self-paced e-learning, candidates need to complete the course by using pre-recorded videos. For online classroom training, Simplilearn uses live online streaming for conducting classes in addition to the pre-recorded videos to give the candidates an enhanced learning experience.

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

    The SAS for Data Science certification exam can be successfully cleared by the candidates who have undertaken Simplilearn’s Data Science with SAS training program and followed its course material.

  • * Disclaimer

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

Our Mumbai Correspondence / Mailing address

Simplilearn Solutions Pvt Ltd, 74/II, “C” Cross Road, Opp Gate No 2, Seepz, Andheri East, Mumbai- 400093, Maharashtra, India, Call us at 1800-212-7688

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