Base SAS 9.x

Course Duration : 50 hrs+12 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

Base SAS Certification – We guide the students to appear for Global Certification program.
Case Studies/Projects based training.
Flexible class timing, continuous support.
Exclusive study materials.
Placement assistance after training.
State-of-Art Labs with latest Infrastructure

Introduction to Analytics and Role of SAS

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


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


Understanding Datasets

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


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


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


Importing and Exporting Data (Fixed Format / Delimited)

  • Import Wizard
  • Proc Import / .txt,.csv,xlsx… on
  • Proc Export / Exporting Data from SAS
  • Datalines
  • Atypical importing cases (mixing different style of inputs)
    • Reading Multiple Records per Observation
    • Reading “Mixed Record Types”
    • Sub-setting from a Raw Data File
    • Multiple Observations per Record
    • Reading Hierarchical Files
  • Importing Tips


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


Combining Datasets (Appending and Merging)

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


Various SAS Functions for Data Manipulation

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


Data Analysis and Reporting by Using SAS Procedures

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


Chart and Graphical Reports

  • ProcGchart for Producing Bar and pie Chart(3D)
  • PROC GPLOT for Producing Plots

We have various case studies based on different industries. You can choose the case study as applicable for you.

Case Study 1: Regression Analysis

How to assess if you are paying correct price or not while buying a property?
Price is very important function for any business. Correct price can create a real gap between profit and loss. In this case study we will take an example of property pricing to gain a deeper understanding of regression analysis.

Step – 1: Data Preparation
A. Checking the outlier
B. Checking Missing Values and how to treat them.
C. Basic bivariate and univariate analysis i.e. checking correlations, how the variables are distributed.
Step – 2: Principle Component Analysis
Step – 3: Traditional Regression Analysis with variable selection


Case Study 2: Marketing Analytics

Being a key decision and strategy maker on an online retail store that specializes in apparel and clothing, how by establishing analytics practice opportunity to improve PnL could be figured out. Background of behavioural analytics – How human brains follow involuntary pattern (behave like other similar people around them) and the detection of the pattern is preciously the idea behind marketing analytics.

Step – 1: EDA – Exploratory Data Analysis
A. Exploring different patterns i.e. distribution of the customers across the number of product categories purchased by each customer.
B. Why the customers buying different product categories
C. Categorization of customers based on the # of product category they purchased.
D. Which category is contributing highest sales?
Step – 2: Association Analysis
E. Support/Confidence/Lift – Apriori concept
F. Market Basket Analysis
Step – 3: Customer Segmentation
A. Classification/Clustering


Case Study 3: Score Card ModelLing

Given the on-going turmoil on credit markets, a critical re-assessment of credit risk modelling approaches is more than ever needed. This modelling approach generates some probability of default score for each customer on basis of some collection of independent variables (it may differ as per business requirements). After that it is usable for predictive modelling, MIS reporting etc.

Step – 1: EDA – Exploratory Data Analysis
A. Data import and basic data sanity check.
B. Exploring different patterns i.e. distribution of data
C. Variables (categorical & numerical) selection approaches.
D. Training and validation data creation.
Step – 2: Model Preparation
E. Creating indicator variables
F. Apply step wise regression
Step – 3: validation of model
G. Check for multi Collinearity (using correlation matrix, VIF)
H. Generate Score using logistic regression.
I. KS calculation
J. Coefficient validation, coefficient stability and score stability.


Case Study 4: Web Scrapping & Text Analysis

The rapid growth of the World Wide Web over the past two decades tremendously changed the way we share, collect, and publish data. Firms, public institutions, and private users provide every imaginable type of information and new channels of communication generate vast amounts of data on human behavior. Web scrapping is a process to extract data from websites and applying some text analysis algorithms to analyze these data. Twitter analysis, google data analysis etc.

Step – 1: Setup connection
A. Create a key against developer account.
B. Run API request to fetch data.
Step – 2: Data Extraction
C. Save API requested data into excel/csv.
D. Data analysis and sanity check (dealing with missing data)
Step – 3: Text mining
E. Apply diff-2 algorithms like: sentiment analysis.

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.

Arnab Majumdar

In-house Faculty/Consultant (SAS, Python)

Arnab Majumdar, Data Scientist Consultant, is a physicist, researcher and educator. He completed his Integrated M.Sc. in Physics from the Indian Institute of Technology, Kanpur before moving to Boston, USA, where he did his Ph.D. and post-doctoral work at Boston University. During his seventeen years in academic research, primarily in the domain of statistical physics, Econophysics and Biomedical engineering, he has published over forty research papers in international peer-reviewed journals including Nature, Physical Review Letters and Proceedings of the National Academy of Sciences USA.

Debajyoti Chakraborty

In-house Faculty (R & SAS)

Debajyoti, a Statistical Analyst, Member of Actuarial Society of India, Analytics Trainer on Statistical Softwares - SAS,R,Ms Excel with basic query language knowledge on SQL. Graduate in Statistics with Maths and Computer Science as other subjects. Having over 3yrs of work experience as Data Analyst. In Statistical Analyst role he has worked on multiple industry projects including dashboarding and analytics implementation for Retail and Healthcare projects. Also, as an Actuarial Analyst he assisted in Claim Analytics. As an Analytics Trainer, he is providing Analytics training to Industry Professionals and Academic Students on Statistical Software packages - SAS, R, MS Excel (Beginner to Advance) and SPSS, and overseeing Data Analysis projects undertaken by students and knowledge sharing for successful completion of projects on time.

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.