Section 1
##### Introduction to SAS

1

What is SAS?

2

User interface of various SAS products: SAS 9.2

3

Concept of Permanent and Temporary Libraries

4

Introduction to Key Concepts on SAS Data Sets

5

Data Comprehension

6

Types of variables in SAS

7

Creating temporary and permanent SAS data sets

8

Introduction to Metadata Structures of a data set: INFORMATS, FORMATS, LENGTH, LABEL, RENAME statements

9

Copying SAS data sets from one library to another library

Section 2
##### Subsetting of Data

10

Creating subsets of a data set using FIRSTOBS, OBS, KEEP, DROP

11

Creating Data Sets and Variables based on conditionality

12

Use of Where, If and When conditional statements to create subsets

13

Use of And, Or, In, Contains logical operators and the sign of inequalities

14

Introduction to IF Then Do Else Then Do

15

Separate out data sets using SAS conditional statements

16

Import various type of data sets like Text files (.txt, .csv), Excel files (.xls) etc using SAS codes

17

Export SAS data sets into different type of file like Text files (.txt, .csv), Excel files (.xls) etc

18

Generate HTML, RTF and PDF reports outside SAS environment using ODS statement

19

Generating listing reports using Print Procedure

20

VAR statement in Print Procedure

21

Suppressing observation column in Print Procedure

22

Getting the number of observation in the output

23

Using custom labels in Print Procedure

24

Using Arithmetic operators like SUM

Section 3
##### Generating listing and summary reports using Report Procedure

Section 4
##### Accessing the Metadata report using Contents Procedure

Section 5
##### Generate Frequency Distribution using Frequency Procedure

Section 6
##### Miscellaneous Topics

Section 7
##### SAS Functions

Section 8
##### Combining SAS Datasets

Section 9
##### Reading Raw Data Sets

Section 10
##### Array in SAS

Section 11
##### Debugging Techniques in SAS

Section 12
##### Structured Query Language (SQL) in SAS using Proc SQL

59

Introduction to Structured Query Language in SAS

60

Advantages of using Proc SQL over Traditional SAS Codes

61

Creating new variables using Proc SQL

62

Use of select statement to display Column headings from a table

63

Creating outputs and new tables using Proc SQL statement

64

Selecting Duplicate/unique values

65

Use of Calculated option, label, format option in Proc SQL

66

Query for sorting a report and Data sets in a specified order of magnitude

67

Compare solving a problem using the SQL procedure versus using traditional SAS programming techniques

68

Use of Customised formula for Calculations and creating subsets of the data sets

69

Using other conditional operators like Between – And, Contains, Missing, Like

70

Use of Case expression on Select statement

71

Application of Where clause, Having Clause

72

Construct sub queries within a PROC SQL step Combining Queries with Set Operators

73

Introduction to SQL Joins and a comparison between SAS merge and SQL join

74

Use of Inner Join, Left Join, Right Join, Full join

75

Creating and updating tables using Proc SQL

76

Editing observations and Data table management

77

Updating Data Values, deleting rows, Altering columns, deleting a table using Proc SQL

Section 13
##### SAS Macros

78

Getting started with Macro facility

79

Introduction to SAS programs and Macro Processing

80

Generating SAS Codes with Macro Language

81

Defining and Calling Macros

82

Introduction to Macro parameters and the concept of Positional and Keyword parameters

83

Introduction to Macro Variables and the Concept of Global and Local Macro variables

84

Defining Arithmetic and Logical expressions in SAS Macro

85

Evaluation of Arithmetic and Logical expressions in SAS Macro

86

Macro functions

87

Introduction to Storing and Reusing Macros

88

Creating Macro in Data Step

89

Creating macro variables with Proc SQL

Welcome to the module of SAS base and advanced!

• SAS or Statistical Analysis System, is a software suite developed by SAS Institute for advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics.

• SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated. SAS was further developed in the 1980s and 1990s with the addition of new statistical procedures, additional components.

• It enables us to perform the following tasks :-

i) Data Entry, Retrieval and Management

ii) Report Writing and Graphics Design

iii) Business Forecasting and Decision Support

iv) Operations Research and Project Management

v) Applications Development

• Companies which use SAS include

i) Accenture

ii) Genpact

iii) Tata Consultancy Services

iv) Google

v) Mu-sigma

vi) Facebook, etc

• SAS, today, is the most widely used Business Intelligence Software. There are other software in the market like EXCEL, BUSINESS OBJECT(BO),ORACLE,COGNOS etc., but there are a number of reasons why SAS is preferred over all the others.

• It is an ETL (Extraction, Transformation and Loading) tool. The data (in raw format) extracted from its storage location and then ‘Transformation‘ of the data takes place. Transformation can pertain to treatment of missing values (e.g. putting ‘0‘ or ‘NA‘) when an observation is missing and all sorts of data manipulation can be done here. The data (after cleaning) can be finally loaded to the data warehouse.

Hence at a glance, SAS is capable of performing the following tasks:

• SAS can fetch/access data from a lot of sources including: Oracle, Excel, Raw Databases and SAS files

• SAS Data management capabilities include: Subsetting Data, Creating new variables and Cleaning and Validating Data

• We can rely on SAS for a lot of statistical analysis of big data, starting from basic calculation of descriptive statistics (mean, median, mode) to complex topics including Prediction and Forecasting

• Data presentation is extremely advanced in SAS. There are a lot of data presentation tools available in this software. We can create different reports also in SAS: List reports, summary reports, graph reports and print reports being the majorly used ones.

• SAS can also collate the datasets using a common characteristic , say, Customer ID and it can also merge or append data sets. For example, there is one dataset on Customer ID, Age , Gender and Educational Qualifications of customers (which is stored in one location) and there is another dataset on Customer ID, Units sold etc. and SAS can also merge or append such data sets by the common variable, Customer ID.