Data Science With SAS

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Data Science with SAS is an advanced technology in the world of data analytics. The training on Data Science with SAS would help the candidates to achieve their career goals by making them understand what PROC MEANS, PROC FREQ, PROC UNIVARIATE and PROC CORP are along with data analytics concepts  such as: clustering, decision tree, and regression and their implementation in their respective organizations. Moreover, fresher graduates would get a chance to learn how to handle real time issues and resolve them with the help of advanced SAS tools.

Following are some of the highlights that the aspirants would learn from the training:

  • Learn how to use the techniques for amending and merging the datasets like concatenation, interleaving, one-to-one merging and reading. The candidates would get a chance to learn all about the SAS functions and procedure for data manipulation.
  • Learn the data exploration techniques using SAS and use them later in their project
  • Learn the OPTMODEL procedure and make use of it
  • Developing understanding on formulating and model and solve the data optimization by using SAS.
  • Understanding the types of Macro variables, SQL clauses, Macro function SYMBOLGEN System options  and the %Macro statement
  • Understanding the role of GUI, Library statements, SAS, importing and exporting of data and variable attributes
  • Learn what is hypothesis testing, statistics and advanced statistics techniques. Learn How to implement the techniques such as: linear regression, Clustering, logistic regression and decision trees.
Target audience
  • The Analytics professionals, who are willing to work with SAS
  • IT professionals looking forward to switch their career in the fields of analytics
  • The Software developers, who are  interested in making and continuing their career in analytics
Prerequisites

There is no prerequisite for this course, anyone willing to make their career in data analytics and want to gain expertise on the data science tools should join this course.

1. Analytics Overview

  • An Introduction
  • An Introduction to the Business Analytics
  • The Types of Analytics
  • The Areas of Analytics
  • About Analytical Tools
  • About Analytical Techniques

2. Introduction to SAS

  • An Overview of the SAS
  • The Navigation in the SAS Console
  • About the SAS Language Input Files
  • What is DATA Step?  
  • The PROC Step and DATA Step - Example
  • All About DATA Step Processing
  • The SAS Libraries

3. Combining and Modifying Datasets

  • Necessity of Combining or Modifying the Data
  • Concatenating the Datasets
  • About Interleaving Method
  • What is Data Manipulation?
  • Modifying the Variable Attributes

4. PROC SQL

  • What is PROC SQL?
  • How to Retrieve Data from a Table?
  • How to Select Columns in a Table?
  • Retrieving the Data from Multiple Tables
  • Selecting the Data from Multiple Tables
  • Concatenating Query Results

5. SAS Macros

  • Need for SAS Macros
  • Macro Functions
  • SQL Clauses for Macros
  • The % Macro Statement
  • The Conditional Statement

6. Basics of Statistics

  • Introduction to the Statistics
  • The Statistical Terms
  • Procedures in the SAS for Descriptive Statistics
  • About Hypothesis Testing
  • About Variable Types
  • The Hypothesis Testing - Process
  • The Parametric and Non - parametric Tests
  • What are Parametric Tests?
  • What are Non - parametric Tests?
  • Parametric Tests – the Advantages and the Disadvantages

7. Statistical Procedures

  • The Statistical Procedures
  • What do PROC Means?
  • What is PROC FREQ?
  • About PROC UNIVARIATE
  • About PROC CORR
  • About PROC CORR Options
  • About PROC REG
  • The PROC REG Options
  • The PROC ANOVA

8. Data Exploration

  • Data Exploration: An Overview
  • What is Data Preparation?  
  • The General Comments and Observations on Data Cleaning
  • Data Type Conversion
  • Character Functions
  • What is SCAN Function?
  • About Date/Time Functions
  • Missing Value Treatment
  • Various Functions to Handle Missing Value
  • Data Summarization

9. Advanced Statistics

  • An Introduction to the Advanced Statistics
  • Introduction to the Cluster
  • The Clustering Methodologies
  • What is K Means Clustering?
  • About the Decision Tree
  • The Regression
  • The Logistic Regression

10. Working with Time Series Data

  • An Introduction to the Working with Time Series Data
  • Need for Time Series Analysis
  • About the Time Series Analysis — Options
  • Reading Date and Datetime Values
  • What is the White Noise Process?
  • The Stationarity of a Time Series
  • The Plot Transform Transpose and Interpolating Time Series Data

11. Designing Optimization Models

  • Introduction to the Designing Optimization Models
  • About the Need for the Optimization
  • About Optimization Problems
  • What is PROC OPTMODEL?

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