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

1

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

2

Creating Data Sets and Variables based on conditionality

3

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

4

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

5

Introduction to IF Then Do Else Then Do

6

Separate out data sets using SAS conditional statements

7

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

8

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

9

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

10

Generating listing reports using Print Procedure

11

VAR statement in Print Procedure

12

Suppressing observation column in Print Procedure

13

Getting the number of observation in the output

14

Using custom labels in Print Procedure

15

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

1

Introduction to Structured Query Language in SAS

2

Advantages of using Proc SQL over Traditional SAS Codes

3

Creating new variables using Proc SQL

4

Use of select statement to display Column headings from a table

5

Creating outputs and new tables using Proc SQL statement

6

Selecting Duplicate/unique values

7

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

8

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

9

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

10

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

11

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

12

Use of Case expression on Select statement

13

Application of Where clause, Having Clause

14

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

15

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

16

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

17

Creating and updating tables using Proc SQL

18

Editing observations and Data table management

19

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

Section 13
##### SAS Macros

1

Getting started with Macro facility

2

Introduction to SAS programs and Macro Processing

3

Generating SAS Codes with Macro Language

4

Defining and Calling Macros

5

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

6

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

7

Defining Arithmetic and Logical expressions in SAS Macro

8

Evaluation of Arithmetic and Logical expressions in SAS Macro

9

Macro functions

10

Introduction to Storing and Reusing Macros

11

Creating Macro in Data Step

12

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.