About Data Sciences with SAS

Lesson 1: Analytics Overview
 Definition of Analytics
 Types of Analytics
 Analytics Problem Types
 Widely used tools and analytical techniques
Lesson 2: SAS Introduction
 Introduction of GUI
 Library statement, understanding of PDV
 Import / Export of Data
 Variable Attributes
 Basic Procedures
Lesson 3: Combining/Modifying Datasets
 Combining Data Sets Methods
 Concatenation
 Interleaving
 One to One Reading
 One to One Merging
 Data Manipulation steps and tools
 SAS Functions and Procedures for Data Manipulations
Lesson 4: PROC SQL
 Introduction and Advantages
 Options and Syntax – Understanding of Select Statement
 Joins in SQL
 Merge v/s Join
Lesson 5: SAS Macros
 Need for SAS Macros
 Macro Variables
 Automatic Macro Variables
 User-defined Macro Variables
 Macro Functions
 SYMBOLGEN System Option
 SQL Clauses for Macros
 The %Macro Statement
Lesson 6: Basic Statistics
 Descriptive Statistics
 Inferential Statistics
 Hypothesis testing
 Non parametric tests
Lesson 7: Basic Statistical Procedures
 PROC UNIVARIATE
 PROC MEANS
 PROC FREQ
 PROC CORR
 PROC REG
 PROC ANOVA
Lesson 8: Data Exploration
 Data Preparation
 Data Type Conversion
 Missing Value Treatment
 Summarizing Data
Lesson 9: Advanced Statistical Techniques
 Clustering
 Introduction
 Clustering Methodology
 Data Preparation
 K Means Clustering
 Cluster Profiling
 Decision Trees
 Introduction
 Creating Decision Trees
 Linear Regression
 Introduction
 Linear Regression in SAS
 Diagnostics
 Logistic Regression
 Introduction
 Logistic Regression in SAS
 Diagnostics
Lesson 10: Working with Time series Data
 Reading Simple Time Series
 Dating time series and working with SAS date and date time values
 Sub setting data and selecting observations
 Storing time series data in SAS data sets
 Plotting, transforming, transposing, interpolating
Lesson 11: Data Optimization using SAS
 Data Optimization
 Creating optimization models commonly used in industry
 Formulating and solving a data envelopment analysis
 Solving optimization problems using the OPTMODEL procedure in SAS