SAS Training and Certification

• Weekdays – 5 to 6 weeks
• Weekend – 7 to 8 weeks
• FastTrack – 3 to 4 weeks

• Weekdays – 5 to 6 weeks
• Weekend – 7 to 8 weeks
• FastTrack – 3 to 4 weeks

• Installation and introduction to SAS
• Different SAS Windows
• Working with different types of Data Sets
• Introduction to various SAS windows i.e. Output, Search, Editor, Log, Explorer
• Working with different types of Data Sets
• Library types and programming files
• SAS functions introduction

• Import and Export raw Data files
• Read and subset the data set
• Statements like SET, MERGE and Where
• Hands on: - Import Excel file, read the data and export the workspace for saving

• Different SAS operators like logical, comparison and arithmetic
• Deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input / Output
• Conditional statements like If/Else, Do While, Do Until and so on
• Hands-on: - Performing operations using the SAS functions and logical and arithmetic operations

• Defining and using KEEP and DROP statements
• Formats and labels in SAS using the defined statements
• Hands-on: - Using KEEP and DROP statements

• Understanding about input buffer and PDV (backend)
• What is Missover?
• Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution
• List input for standard data
• Hands-on: - Use delimiter rules on raw data files

• Various popular built-in SAS standard procedures for programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.
• Hands-on: - Use SORT, FREQ, SUMMARY, EXPORT and other procedure

• Reading standard and non-standard numeric inputs with formatted inputs
• Column pointer controls
• Controlling while a record loads
• Line pointer: - control/absolute line pointer, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing
• Hands-on: - 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 and deploying it on PROC data sets
• Comparing ATTRIB and Format statements
• Hands-on: - 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: - 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

• Introduction to ODS
• Data optimization and how to generate files (rtf, pdf, html and doc) using SAS
• Hands-on: - Optimize data and generate rtf, pdf, html and doc files

• Macro Syntax and macro variables
• Positional parameters in a macro and macro step
• Hands-on: - Write a macro and use positional parameters

• SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data
• Hands-on: - 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 and global statements
• User-defined formats
• PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means
• PROC UNIVARIATE, PROC REPORT and PROC PRINT
• Hands-on: - Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT

Industry: - Healthcare

Problem Statement: - A healthcare organisation wants to generate descriptive statistics and create subsets for clinical data problems and insights.

Description: - This real time project will work in two steps.

• Step 1: - Patient count
a. No. of Patients on any one of given 4 drugs ever b. No. of Patient on all given 4 drugs ever c. No. of Patient never been on any of given 4 drugs
• Step 2: - Data set output
a. List of Patient under category 1 b. List of Patient under category 2 c. List of Patient under category 3 d. Summary – Summary of whole data set

Industry: - Finance

Problem Statement: - This project aims at finding out most powerful factor while creating a prepaid plan to increase investor preferences.

Description: - This project will help to figure out most reliable pre-paid model among the investors. It will identify the reasons for pre-paid model preferences and non-preferences among the investors to understand and identify various advantages and disadvantages of schemes and figuring out brand wise market share. It will figure out different insights for a brand to penetrate deeper into the market shares.

Industry: - Sales

Problem Statement: - You need to collectively use the concept of data analytics, predictive modelling and data mining to help an enterprise to generate a revenue prediction model

Description: - With this project more advanced concepts of SAS analytics and business intelligence will be understood and deployed over data entered in business enterprise setup. The process comprises of data aggregation, retrieving data and managing the data 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.

Industry: - Healthcare

Problem Statement: - An healthcare institute wants to have a research over different types of tumours. You need to make a analysis to generate clusters of highly relatable tumours.

Description: - This project is highly focused for medical industry and research. Different features of tumours such as dimensions and sizes will be used for analytical processes which will help out in figuring the type of tumour and predict the possible infection or diseases associated.