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.
This advanced-level 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
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
Unlimited access to exclusive Study Materials on Cloud
Chapter-1: Introduction to the SQL Procedure
1.1 What is SQL?
1.2 What is the SQL Procedure?
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
3.2 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
4.2 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
5.2 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
6.2 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
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.
In-house Faculty (R & SAS)
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.
In-house Faculty/Consultant (SAS, Python)
In-house Faculty (R & SAS)
In-house Faculty/Consultant (SAS, Python,R,SPSS)