Data Science With SAS 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.

• 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

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?