SAS

SAS is an order driven factual programming suite broadly utilized for measurable information examination and perception. Its full structure is ‘Statistical Analysis Software’.

The SAS training course in Delhi is incorporated with the utilization of subjective strategies and cycles which assist you with upgrading worker profitability and business benefits.

SAS is additionally utilized for cutting edge examination like business insight, wrongdoing examination, and prescient investigation. SAS is articulated as “SaaS.”

In SAS, information is extricated and classified which encourages you to distinguish and break down information designs and SAS institute in Delhi experts students in everything related to SAS in detail.

It is a product suite which permits you to perform progressed investigation, Business Intelligence, Predictive Analysis, information the board to work adequately in the serious and changing business conditions.

Additionally, SAS is stage free which implies you can run SAS on any working framework either Linux or Windows and above mentioned terms are taught by SAS institute in Delhi briefly.

Contrasted with other BI instruments, SAS offers broad help to automatically change and investigate information, aside from utilizing the simplified interface. SAS training course in Delhi gives extremely granular command over information control and examines which is its USP.

What are the alternatives of SAS tools?

• R: It is open-source programming. It is anything but difficult to learn R as it is very much archived. It offers solid measurable capacities.
• Python: It is another mainstream open-source scripting language. It is upholds libraries, for example, Numpy, Scipy, and MatPlotLib. You can play out any factual activity, or you can construct any model utilizing these libraries.
• SAS: It is the broadly utilized logical apparatus in the business investigation market and with the help of plenty measurable capacities and great GUI.

SAS Training Syllabus:

• CONCEPTS OF SAS DATA WAREHOUSING
• What is a Data Warehouse?
• What is a Data Mart?
• What is the difference between Relational Databases and the Data in Data Warehouse (OLTP versus OLAP)
• Why do we need Data Warehouses when the Relational Source Database exists
• Multi-Dimensional Analysis and Decision Support Reporting from Data Warehouse
• Data Warehouse Architecture (ETL Design)
• Normalized Relational Database Design (Entity Relationship Model)
• Dimensional Data Modeling
• Star Schema Design
• Snowflake Schema Design
• Slowly Changing Dimensions Why SAS BI ?Capabilities of SAS B12 Advantages of SAS BI Over Base & Advance SAS
• B1 Architecture
• SAS BI Tools
• INTRODUCTION OF BASE SAS
• An Overview of the SAS System
• SAS Tasks
• Output produced by the SAS System
• SAS Tools
• A sample SAS program
• Exploring SAS Windowing Environment Navigation
• DATA ACCESS & DATA MANAGEMENT
• SAS Data Libraries
• Rules for Writing SAS Programs / Statements, Dataset Variable Name Getting familiar with SAS Dataset
• Data portion of the SAS Dataset
• Rules for writing Dataset names / Variable names
• Attributes of a Variable (Numeric / Character)
• Options
• System Options
• Dataset Options
• How SAS works
• Input Buffer
• Program data vector (PDV)
• DATALINES OR CARDS DATA TRANSFORMATIONS
• SAS Date Values
• Length Statement
• Creating multiple output SAS datasets from singe input SAS dataset
• Conditionally writing observations to one or more SAS datasets
• Outputting Multiple Observations (Implicit Output)
• Selecting Variables and observations (DROP or KEEP statement and DROP= or KEEP = dataset options)
• Controlling which Observations are read (OBS= FIRSTOBS = Options)
• The Data Statement_Null_
• The_N_Automatic Variable
• Creating Subset of observations
• READING RAW DATA FROM EXTERNAL FILE (INFILE & INPUT STATEMENT )
• Introduction to Raw Data
• Factors considered to examine the raw data
• Reading Unaligned Data (List Input)
• Reading Data Aligned to Columns (Column Input)
• Reading Data that requires Special Instructions (Formatted Input)
• Controlling the position of the Pointer in Formatted Input
• Absolute – Column pointer control (@)
• Relative- Column pointer control (+)
• Mixed Style Input (Mixing List, Input. Formatted Input styles in one INPUT Statement)
• Using colon (:) modifier to specify an informat in the INPUT Statement )
• Recognize delimiter in the raw data file (Using DLM= option in INFILE Statement
• Missing data at the end of row (Using MISSOVER option in INFILE statement )
• Missing values without placeholders (DSD option in INFILE statement)
• Reading a raw data file with multiple records per observation(Column pointer controls)
• Method1: Using Multiple INPUT statement
• Method2: Using Line Pointer Control (/)
• Reading Variables from multiple records in any order (#n)
• Line Hold Specifies in INPUT statement
• The Single Trailing @
• FUNCTIONS OF SAS
• Manipulating Character Values (SUBSTRING / RIGHT / LEFT / SCAN/ CONCATENATION TRIM / FIND / INDEX / UPCASE / LOWCASE / COMPRESS / LENGTH )
• Manipulation Numeric Values ( ROUND / CEIL / FLOOR / INT / SUM / MEAN /MIN/MAX)
• Manipulating Numeric Values based on DATES ( MDY / TODAY / INTCK / YRDIF)
• Converting Variable Type
• INPUT ( character-to-numeric)
• PUT (numeric-to-character)
• Debugging SAS program (DEBUG Option)
• SAS VARIABLE Lists
• SAS Arrays
• Enhancing Report Output
• Defining Titles & Footnotes