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
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.
Count the number of patients,
Output should be four datasets
Project 2: Build Revenue Projections Reports
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.
Project 4:K-Means Cluster Analysis on Iris Dataset
Objective: K-Means cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions
No, this course is not officially accredited by SAS.
This SAS course is designed for clearing the SAS Certified Base Programmer exam. The entire course content is in line with the certification program and helps you clear the certification exam with ease and get the best jobs in top MNCs.
As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this training program, there will be a quiz that perfectly reflects the type of questions asked in the certification exam and helps you score better marks.
Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after the expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.