Course Description

  • Why learn Data Science with SAS?

    SAS is a leader in 2017 Gartner Magic Quadrant for Data Science Platform 
    The average salary for a Business Intelligence Developer skilled in SAS is $104k (Source - Paysa)

  • What are the course objectives?

    Simplilearn’s Data Science with SAS online training course is designed to enable learners to become adept in analytics techniques using SAS  data science tools. This online course covers a holistic overview of analytics and graphic user interface (GUI). You will learn how to combine dataset methods, understand select statements and joins in SQL, and comprehend the need for macro variables. This online training course will also teach you how to apply data manipulation and optimization techniques; advanced statistical concepts like clustering, linear regression and decision trees; data analysis methods to solve real-world business problems and predictive modeling techniques.

  • What skills will you learn?

    This course will enable you to:

    • Understand the role of data scientists, various analytics techniques, and widely used tools
    • Gain an understanding of SAS, the role of GUI, library statements, importing and exporting of data and variable attributes
    • Gain an in-depth understanding of statistics, hypothesis testing, and advanced statistical techniques like clustering, decision trees, linear regression, and logistic regression
    • Learn the various techniques for combining and modifying datasets like concatenation, interleaving, one-to-one merging and reading. You will also learn the various SAS functions and procedure for data manipulation
    • Understand PROC SQL, its syntax, and master the various PROC statements and subsequent statistical procedures used for analytics including PROC UNIVARIATE, PROC MEANS, PROC FREQ, PROC CORP, and more.
    • Understand the power of SAS Macros and how they can be used for faster data manipulation and for reducing the amount of regular SAS code required for analytics
    • Gain an in-depth understanding of the various types of Macro variables, Macro function SYMBOLGEN System options, SQL clauses, and the %Macro statement
    • Learn and perform data exploration techniques using SAS
    • Understand various time series models and work on those using SAS
    • Model, formulate and solve data optimization by using SAS and OPTMODEL procedure

  • Who should take this course?

    There is an increasing demand for skilled data scientists across all industries that make this course suitable for participants at all levels of experience. We recommend this data science training especially for the following professionals:

    • Analytics professionals who want to work with SAS
    • IT professionals looking for a career switch in the fields of analytics
    • Software developers interested in pursuing a career in analytics
    • Graduates looking to build a career in analytics and data science
    • Experienced professionals who would like to harness data science in their fields

    Prerequisites: There are no prerequisites for this course. The free SAS Base Programmer course provides some additional coding guidance.

  • What projects are included in this course?

    The SAS Certification training course includes five real-life, industry-based projects including Walmart demand prediction. Successful evaluation of one of the following projects is a part of the certification eligibility criteria.

    Project 1: Products rating prediction for Amazon

    Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

    Domain: E-commerce

    Project 2: Demand Forecasting for Walmart

    Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

    Domain: Retail

    Project 3: Improving customer experience for Comcast

    Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

    Domain: Telecom

    Project 4: Attrition Analysis for IBM

    IBM, one of the leading US-based IT companies, would like to identify the factors that influence attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

    Domain: Workforce Analytics

    Project 5: Attrition Analysis

    Analyze the employee attrition rate of a leading BPO company. The dataset is maintained for the attrition analysis, and it has records of employee id, retain indicator, sex indicator, relocation indicator, and marital status.

    Domain: Telecommunication

    Project 6: Retail Analysis

    Forecast sales based on independent variables such as profit, quantity, marketing cost, and expenses using the regression model.

    Domain: E-commerce

    Two additional projects have been provided for practice:

    Project 7: Data-driven Macro Calls

    Generate a list of all data sets in SAS which have sales-related information and pass it on as the macro variable.

    Domain: Sales

    Project 8: Customer Segmentation

    Perform customer segmentation with RFM methodology on an e-commerce website’s customer data set. Segment customers based on frequency, recency, and monetary value.

    Domain: Internet

  • What types of jobs are ideal for SAS trained professionals?

    Jobs that are ideal for SAS trained professionals include:

    • Data scientist
    • SAS Programmer
    • Data analyst
    • Cyber defense data scientist
    • Statistical and clinical data management programmer
    • Programmer Analyst

Course Preview

    • Lesson 00 - Course Introduction

      04:21
      • 0.001 Introduction
        04:21
    • Lesson 01 - Analytics Overview

      11:51
      • 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

      19:43
      • 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

      36:04
      • 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

      25:54
      • 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

      19:16
      • 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

      23:23
      • 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

      33:21
      • 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

      21:46
      • 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

      30:32
      • 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

      27:25
      • 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

      18:59
      • 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
    • Lesson 00 - Course Introduction

      04:35
      • 0.1 Introduction
        04:35
    • Lesson 01 - Introduction to SAS Base Program

      1:01:45
      • 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

      18:45
      • 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

      36:15
      • 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

      30:28
      • 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

      13:22
      • 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

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

      • Course Feedback
    • Math Refresher

      30:36
      • Math Refresher
        30:36
    • Lesson 1 Introduction

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

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

      21:18
      • 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

      25:17
      • 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

      16:17
      • 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

      23:36
      • 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

      14:27
      • 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

      12:36
      • 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!

      26:39
      • 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

      18:32
      • 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

      23:25
      • 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

      19:16
      • 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
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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.

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.

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.

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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.

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Lakshmi Saranya Gollapudi
Lakshmi Saranya Gollapudi In-House Statistician at Sunshine Hospitals, Hyderabad

Simplilearn is indeed a great platform to acquire the skills required for the fast-changing platform. The instructors, as well as the support group, are excellent. Apart from the course, you have enrolled, the instructors will acquaint with the correct usage of the technology mix. It has entailed me to learn things faster and enabled me to have the correct thought process that is required for aligning the technology with the business.

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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.

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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.

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Norberto Bayaga
Norberto Bayaga Software Engineer / Programmer at National Electrification Administration, Bangalore

"It is a wonderful experience and the best educational course that I enrolled in Simplilearn. Very easy to go through, and all the guides and ebooks are on the site. It’s easy to plan and schedule the modules your learning and manage your workload at the same time your studies. Thank you support team for helping and assisting me in the course and providing assistance and updates on other courses. Wish you all the best.

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Hema Sankaran
Hema Sankaran Business Analytics | Walgreen, Bangalore

I was recognized instantly as an expert in Data Science within my organization. The course has provided me with an opportunity to establish myself as a strong candidate for the ongoing and future data projects. I have been able to suggest and recommend confidently based on the knowledge that I got from the course. It has been a great stepping stone as well in my career path.

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Frado Sibarani
Frado Sibarani Senior IT Specialist at IBM, Singapore

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

Aravind Naick
Aravind Naick Project Manager at UST Global, Bangalore

I already had some technology and project management experience and took this training to get an understanding of the analytics domain. This course is structured and designed well, it makes the concepts easy to grasp. The statistical input was the best part as it really changes the way you look at data. The projects were really interesting and I actually went ahead and completed 2 of them. Simplilearn support is good and all my queries were handled promptly. I have already recommended this training to my team.

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Shubham Gupta
Shubham Gupta Analyst at Ernst & Young LLP, Bangalore

The data scientist training is a must for someone who wants to build a career in this field. It is filled with real world examples and exercises which helped me gain a new perspective to the subject. I am from a non-coding background and I was able to follow the course content with ease. My firm is starting a data science division and this training has now made me eligible for the role of a data science consultant in the division. I am thankful to Simplilearn for all the help.

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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.

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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!

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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.

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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.

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    FAQs

    • What are the System Requirements?

      To run SAS, your 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?

      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 Austin Correspondence / Mailing address

    106 East Sixth Street, Suite 900, Austin, Texas 78701, United States of America

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