SAS Base Certification Training - Hyderabad, India

1. Fundamentals and environment of SAS
2. SAS Enterprise Guide
3. Creation, compilation and execution of SAS datasets
4. Creating and analyzing graphs and reports
5. SAS Base and SAS Advanced
6. Logical operators, statements and functions

• Business Intelligence Professionals
• Software Developers and Architects
• Project Managers
• ETL, SQL, Data Warehousing and Analytics Professionals
• Mainframe and Testing Professionals
• Graduates wishing to enter Big Data Analytics domain

Anybody can learn SAS and get trained. However, having a basic knowledge of SQL can help.

Hyderabad is the second most dynamic city in India with a booming IT sector. This city comprises all sorts of tech firms, small and big, thriving in this market. With the growing unpredictability of economy and business conditions, companies are increasingly relying on research and analytics. Due to this, SAS software stack has become a preferred tool for the Hyderabad-based companies generating lucrative career opportunity for the aspirants in this city.

With the emergence of Big Data, more and more companies are adopting tools and technologies to comprehend and decode this humongous amount of data. SAS is one of those technologies which have successfully served this purpose. Therefore, companies in Hyderabad are hiring SAS professionals with attractive salary packages.

• 70% of companies say that analytics is integral to how they make decisions – IBM Study
• Most companies estimate that they’re analyzing a mere 12% of the data they have – Forrester Research
• A Principal SAS Programmer in the United States can earn up to $102,000 – indeed.com

Intellipaat’s SAS training course helps you gain expertise in SAS through interactive classrooms, lab sessions and real-time projects. Also, this training program helps you qualify for the SAS Certified Base Programmer exam with good scores. Enroll in the Intellipaat SAS training course and give a head start to your career.

Bangalore, Melbourne, Chicago, San Francisco, London, Toronto, New York and India

Installation and introduction to SAS, how to get started with SAS, understanding different SAS windows, how to work with data sets, various SAS windows like output, search, editor, log and explorer and understanding the SAS functions, which are various library types and programming files

How to import and export raw data files, how to read and subset the data sets, different statements like SET, MERGE and WHERE

Hands-on Exercise: How to import the Excel file in the workspace and how to read data and export the workspace to save data

Different SAS operators like logical, comparison and arithmetic, deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on

Hands-on Exercise: Performing operations using the SAS functions and logical and arithmetic operations

Understanding about input buffer, PDV (backend) and learning what is Missover

Defining and using KEEP and DROP statements, apply these statements and formats and labels in SAS

Hands-on Exercise: Use KEEP and DROP statements

Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution and list input for standard data

Hands-on Exercise: Use delimiter rules on raw data files

Various SAS standard procedures built-in for popular programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.

Hands-on Exercise: Use SORT, FREQ, SUMMARY, EXPORT and other procedures

Reading standard and non-standard numeric inputs with formatted inputs, column pointer controls, controlling while a record loads, line pointer control/absolute line pointer control, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing

Hands-on Exercise:  Read standard and non-standard numeric inputs with formatted inputs, control while a record loads, control a line pointer and write multiple IN and OUT statements

SAS Format statements: standard and user-written, associating a format with a variable, working with SAS Format, deploying it on PROC data sets and comparing ATTRIB and Format statements

Hands-on Exercise: Format a variable, deploy format rule on PROC data set and use ATTRIB statement

Understanding PROC GCHART, various graphs, bar charts: pie, bar and 3D and plotting variables with PROC GPLOT

Hands-on Exercise: Plot graphs using PROC GPLOT and display charts using PROC GCHART

SAS advanced data discovery and visualization, point-and-click analytics capabilities and powerful reporting tools

Character functions, numeric functions and converting variable type

Hands-on Exercise: Use functions in data transformation

Introduction to ODS, data optimization and how to generate files (rtf, pdf, html and doc) using SAS

Hands-on Exercise: Optimize data and generate rtf, pdf, html and doc files

Macro Syntax, macro variables, positional parameters in a macro and macro step

Hands-on Exercise: Write a macro and use positional parameters

SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data

Hands-on Exercise: Create SQL query to select and add a condition and use a CASE in select query

Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval and descriptive statistics

Hands-on Exercise: Use web UI to do statistical operations

Report enhancement, global statements, user-defined formats, PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT and PROC PRINT

Hands-on Exercise: Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT

Project 1: Categorization of Patients Based on the Count of Drugs for Their Therapy

Domain: Healthcare

Objective: This project aims to find out descriptive statistics and subset for specific clinical data problems. It will give them brief insight about Base SAS procedures and data steps.

Problem Statement:

Count the number of patients,

1. Who were ever on at least one of the four drugs
2. Who were ever on each of the four drugs
3. Who had never been on any drug

Output should be four datasets

1. TYPA – Contains the list of patients from problem 1
2. TYPB – Contains the list of patients from problem 2
3. TYPC – Contains the list of patients from problem 3
4. SUMMARY – Contains the summary of counts for each of three problems

Project 2: Build Revenue Projections Reports

Domain: Sales

Objective: This project will give you hands-on experience in working with the SAS data analytics and business intelligence tool. You will be working on the data entered in a business enterprise setup and will aggregate, retrieve and manage that data. You will learn to create insightful reports and graphs and come up with statistical and mathematical analysis to scientifically predict the revenue projection for a particular future time frame. Upon the completion of the project, you will be well-versed in the practical aspects of data analytics, predictive modeling and data mining.

Project 3: Impact of Pre-paid Plans on the Preferences of Investors

Domain: Finance Market

Objective: The project aims to find the most impacting factors in preferences of pre-paid model; it also identifies which all are the variables highly correlated with impacting factors.

Problem Statement:

• The project aims to identify various reasons for pre-paid model preference and non-preference among the investors, to understand the penetration of the pre-paid model in the brokerage firms and, to identify the pre-paid scheme advantages and disadvantages and also to identify brand-wise market share. In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale.

Project 4:K-Means Cluster Analysis on Iris Dataset

Domain: Analytics

Objective: K-Means cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions

Requirements:

• Using the famous Iris dataset, predict the class of a flower
• Perform K-Means cluster analysis