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Data Science With SAS

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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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