Base SAS (Beginner)

Course Duration : 50 hrs + Case Study

About Base SAS

SAS is the leader in business analytics software and services.
Base SAS provides a scalable, integrated software environment specially designed for data access, transformation and reporting. It includes a fourth-generation programming language; ready-to-use programs for data manipulation, information storage and retrieval, descriptive statistics and report writing; and a powerful macro facility that reduces programming time and maintenance headaches.

Course Overview

This foundation course focuses on the following key areas:

  • Reading raw data files and SAS data sets
  • Investigating and summarizing data by generating frequency tables and descriptive statistics
  • Creating SAS variables and recoding data values, sub setting data
  • Combining multiple SAS files
  • Creating listing, summary, HTML, and graph reports


What we offer

Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Classroom training with flexible timing
Customized/On-demand training
Unlimited access to exclusive Study Materials on Cloud

Chapter-1: Introduction to Analytics and Role of SAS

  • Introduction to Analytics
  • SAS in Data Manipulation
  • SAS in Data Presentation
  • SAS in advanced analytics
  • SAS Certification


Chapter-2: Introduction to SAS

  • SAS software  latest version ,installation and system requirements
  • Introduction to SAS, GUI
  • Different components of SAS language like PROC SQL
  • SAS programming windowing Envirournment
  • Concept of SAS Data Libraries and Creating Libraries
  • Variable and  Attributes – (Name, Type, Length, Format, In format, Label)
  • Importing Data and Entering data manually


Chapter-3: Understanding Datasets

  • Descriptor Portion of a Dataset
  • Data Portion of a SAS Dataset
  • Variable Names and Values
  • SAS Data Libraries
  • SAS Terminologies


Chapter-4: Accessing Data by Using Data Step

Data Step processing

  • Data step execution
  • Compilation and execution phase
  • Looking behind the scene
  • Input buffer and concept of PDV


Chapter-5: Importing Raw Data files

  • Column Input ,Formatted input and List Input
  • Delimiters, Reading missing and non standard values
  • Reading one to many and many to one records
  • Reading Hierarchical files
  • Creating raw data files and put statement
  • Formats / Informat


Chapter-6: Importing and Exporting Data (Fixed Format / Delimited)

  • Import Wizard
  • Proc Import / .txt,.csv,xlsx… on
  • Proc Export / Exporting Data from SAS
  • Datalines


Chapter-7: Data Understanding, Managing & Manipulation

Understanding and Exporing Data

  • Introduction to basic Procedures – Proc Contents, Proc Print
  • Operators and Operands
  • Conditional Statements (Where, If, If then Else, If then Do and select when)
  • Difference between WHERE and IF statements and limitation of WHERE statements
  • SAS Labels, Commenting
  • SAS System Options (OBS, FSTOBS, NOOBS etc…)


Data Manipulation

  • Proc Sort – with options / De-Duping
  • Accumulator variable and By-Group processing
  • Explicit Output Statements
  • Nesting Do loops
  • Do While and Do Until Statement
  • Array elements and Range


Chapter-8: Combining Datasets (Appending and Merging)

  • Concatenation
  • Interleaving
  • One To One Merging
  • Match Merging
  • IN = Controlling SAS merge and Indicator


Chapter-9: Various SAS Functions for Data Manipulation

  • Functions for Arithmatic operations
  • Functions for Date and Time
  • Functions for Text Manipulation
  • Functions for Nested Functions


Chapter-10: Data Analysis and Reporting by Using SAS Procedures

Basic Statistics using SAS

  • Types of Data
  • Summarizing Data in Tables
  • Frequency Distribution
  • Grouped Frequency Distribution
  • Cumulative Frequency Distribution
  • Summarizing Data in diagrams using
    Barplot,Histogram,Boxplot,Stem and Leaf Diagram,Dotplot/Lineplot
  • Introduction to Descriptive Statistics and Measure of Dispersion


Statistical Proficiecncy using SAS

  • Correlation and Regression
  • Correlation
  • Types of linear Correlation
  • Covarience
  • Correlation Coefficient
  • Regression
  • Line of Best Fit
  • Regression Line


Summary and Statistical Reports

  • ProcFreq
  • Proc Format to create user defined formats
  • Proc Means
  • Proc Summary
  • Proc tabulate
  • Proc report
  • Concept of the Output Delivery System for generating HTML,PDF and RTF Files



Criminal Data Analysis

In the last few years most of the American cities have recorded a low crime rate. However, San Francisco Bay area saw an up surge in crimes reported in these years. The need for anticipating and take preventive measures have become the utmost priority for the local government authorities. The crime records of the area for the last few years need to be mined , analyzed and used to forecast the crime that can be committed in future. Our Objective is to find out

1.How many crimes have been reported in each category of crime?
2.Which day of the week sees the highest number of crimes recorded ?
3.Frequency of crimes resolved to that of pending.
4.How many crimes have been reported by each PdDistrict ?
5.Predict the Crime Category

Tania Chakraborty

In-house Faculty (R & SAS)

Tania, with a background in engineering, have 3+ years of hands on working experience on various Analytics tools, mainly SAS & R. She played a major role in the student data analysis of two entire countries, Dominica & St. Kitts, on a popular student management software “openSIS”.
Other than that she has worked on various other data analysis projects like, Data Analysis on US Economic Indices, Twitter Sentimental Analysis, GDP rates etc.
Simultaneously with project work, she provides training on Big Data analytics using Hadoop and R, Base SAS & Advanced SAS. She has already educated over hundred high profile MNC professionals on Data Analytics. She is the most junior but most appreciated faculty of our team.

Anindya Kundu

In-house Faculty/Consultant (SAS, Python,R,SPSS)

Anindya Kundu is into qualitative and quantitative analytics consultancy for more than half a decade. He has been involved in both analytics as a service and analytics product development projects. Anindya is a data obsessed person who loves generating insights from large quantities of data - clean, process, harness data to get hidden truth. He uses his SAS/R/SPSS/Python tool implementation capability to analyze data, and also perform automation. He is involved in analytics innovation, specializing in product development for population health management, health economics, insurance and mortgage, healthcare analytics and transportation – supply chain management.  He was also extensively involved in functional development of CRM (Customer retention module) application tool for a Fortune's Best 100 Companies. He has received his post graduate certification from IIM Ranchi.