Advanced SAS

Course Duration : 50 hrs + 12 hrs (Case Study)
18,000

About Advanced SAS

This is where SAS programming gets serious. People with Advanced SAS knowledge can look at your code and optimize it. They would know nifty hacks to use the system more efficiently. It your needs are reporting / dashboarding / automation on SAS, you will find these people good with their work.


Course Overview

This intermediate course focuses on using SQL as a data query and manipulation tool. You learn to use the SQL procedure as a data retrieval tool within SAS programs. Specifically, you learn

    • How to perform queries on data
    • Retrieve data from multiple tables
    • Create views, indexes, and tables; and update or delete values in existing tables and views
    • Using features of the SQL procedure to debug, test and optimize the performance of SQL queries

 

    It also focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with and without macro code are processed.

What we offer

Hands on experience with SAS software
Real time industry problems and solutions (Sample Projects)
Interview preparation and placement assistance
Sample questions for Global certification examination
SAS Certified Faculties
Exclusive Study Material
State-of-Art Labs with latest Infrastructure


Certification

NIVT certifies the students on successful completion of the training. NIVT being a brand in the Analytics industry, it’s certificate carries a good value to the high profile employers.
Apart from the that the above course prepares you for Advanced SAS Certification and we do evaluations and provide guidance for the certification exam.

Chapter-1: Introduction to the SQL Procedure

1.1 What is SQL?
1.2 What is the SQL Procedure?
1.3 Terminology
1.4 Comparing PROC SQL with the SAS DATA step
1.5 Notes about the Example Table

Chapter-2: Retrieving Data From a Single Table

2.1 Overview of the select Statement
2.2 Selecting Columns in a Table
2.3 Creating New Columns
2.4 Sorting Data
2.5 Retrieving rows that satisfy a Condition
2.6 Summarizing Data
2.7 Grouping Data
2.8 Filtering Grouped Data

Chapter-3: Retrieving Data from Multiple Tables

3.1 Introduction
3.2 Selecting Data from More Than One Table by
3.3 Using joins
3.4 Using Sub queries to Select Data
3.5 When to Use Joins and Sub queries
3.6 Combining Queries with Set Operators

Chapter-4: Creating and Updating Tables and Views

4.1 Introduction
4.2 Creating Tables
4.3 Inserting Rows into Tables
4.4 Updating Data Values in a Table
4.5 Deleting Rows
4.6 Altering Columns
4.7 Creating an Index
4.8 Deleting a Table
4.9 Using SQL Procedure Tables in SAS Software
4.10 Creating and Using Integrity Constraints in a Table

Chapter-5: Programming with the SQL Procedure

5.1 Introduction
5.2 Using Proc SQL Options to Create and Debug Quires
5.3 Improving Query Performance
5.4 Accessing SAS System Information Using DICTIONRY Tables
5.5 Using Proc SQL with the SAS Macro Facility
5.6 Formatting PROC SQL output using the Report Procedure
5.7 Accessing a DBMS with SAS/ACCESS Software

Chapter-6: Practical Problem Solving with Proc SQL

6.1 Overview
6.2 Computing a Weighted Average
6.3 Comparing Tables
6.4 Overlaying Missing Data Values
6.5 Computing Percentages within Subtotals
6.6 Counting Duplicate Rows in a Table
6.7 Expanding Hierarchical Data in a Table
6.8 Summarizing Data in Multiple Columns
6.9 Creating a Summary Report
6.10 Creating a Customized Sort Order
6.11 Conditionally Updating a Table
6.12 Updating a Table with Values from another Table
6.13 Creating and Using Macro Variables

Chapter-7: SAS Macros

7.1 SAS Macro Overview
7.2 SAS Macro Variables
7.3 Scope of Macro variables
7.4 Defining SAS Macros
7.5 Inserting Comments in Macros
7.6 Macros with Arguments
7.7 Conditional Macros
7.8 Macros Repeating PROC Execution
7.9 Macro Language
7.10 SAS Macro Processor

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.