Video description
SAS programming continues to be the language of choice for most enterprises/corporations. In 2018, 92% of Fortune 100 companies used SAS. It is the go-to for many industries, including banking/finance, insurance, healthcare, pharmaceutical, and automotive.
The first part of the course utilizes the Data Step; the second part looks at SAS SQL, and the third part looks at Macro Programming/Programs. The author has added a section on SAS predictive modeling using logistic regression. Here, we will build a SAS Model. In other words, we will build a predictive model (also known as predictive analytics) while following critical principles to ensure we get things right.
This course is also developed to help you become SAS Certified Specialist: Base Programming certified.
By the end of this course, you will learn how to code in the SAS programming language, to help you start a career/gain employment, or move up at your current company. If you are studying SAS at a post-secondary institution, this course can not only help you with school projects but prepare you for a career after you complete your education.
Disclaimer: This course uses a commercial license from WPS.
Learning SAS programming means that you will be able to accomplish the same goal on any software that supports the SAS language. The author personally uses WPS.
What You Will Learn
- Discover the data step, the primary way to program in SAS
- Learn about the proc step that is utilized for more particular tasks
- Apply SAS SQL, the SAS interpretation of SQL
- Look at the practical application of SAS SQL
- Learn the fundamentals of Macro facility
- Look at SAS predictive modeling and evaluation metrics
Audience
This course is designed for the users of the SAS Enterprise Guide, as being able to code inside of EG is the next natural step and skill to obtain. Individuals that are considering a career or want to gain employment with the biggest corporations/enterprises out there along with the ones looking to become SAS Certified Specialist: Base Programming will gain a lot from this course. The students new to SAS syntax and or looking for a refresher will learn and enjoy this course thoroughly.
No prior programming knowledge is required to take this course but only a stable internet connection.
About The Author
Ermin Dedic: Ermin Dedic started his studies by studying psychology for six years. He received his bachelor’s degree from the University of Ottawa, Canada, and his master’s degree from the University of Calgary, Canada. Ermin also spent two years in a master’s program (school/child psychology) at the University of Calgary before voluntarily withdrawing, in part to focus more on his teaching. It was through academia that he was introduced to and fell in love with statistics and statistical programming with SAS.
He is passionate about making education accessible and fun for students. Ermin believes that students learn better when they feel the passion that the instructor has for the content.
Table of contents
- Chapter 1 : Introduction to the Course
- Chapter 2 : Importing
- Chapter 3 : SAS Syntax, Data Step Versus Proc Step, SAS Compared to R/Python
-
Chapter 4 : Working with Data
- Data Set Options
- What If Your Data Is Separated by a Dot or Something Else? (Delimiters)
- Reading Data Instream in Data Step (Typing Data Right into Coding Area)
- Reading DATES in Data
- Creating Variables/Calculations
- More on Creating New Variables
- Automatic Variables
- Filtering Observations (So Only Some Data Shows Up)
- Intuition for If-Then/Else and Do, Do-While, Do-Until
- If-Then Conditional Logic
- DO Iterative Loop and Variations (DO WHILE, DO Until)
- More on DO Group Processing (Without Index/Counter Variable)
- More on the WHERE Expression/Statement
- Sorting Observations (PROC SORT and BY Statements)
- Merging Two Datasets
- Using SET Statement to Merge
- Data Reduction and Cleaning Your Data
- LENGTH Statement
- Creating a Counting (Enumeration) Variable
- Chapter 5 : Back to Importing
- Chapter 6 : Input Types and Informats + User-Defined Formats
- Chapter 7 : Arrays
-
Chapter 8 : SAS Functions
- Understanding SAS Functions
- RAND Function (Producing a Sample with Distribution of Your Choice)
- LENGTH, LENGTHN, LENGTHC Functions (Are You Working with a Large Dataset?)
- TRIM Function (Want to Get Rid of Trailing Blanks?)
- COMPRESS Function (Remove Characters from String, and All Types of Blanks)
- Input and Put Functions
- CATX Function
- SCAN Function
- Coalesce Function
- Verify Function
- Substr Function
- Chapter 9 : Advanced Techniques – Flexibilities and Efficiency
- Chapter 10 : Visual Representation of Data
- Chapter 11 : Statistical Analysis
- Chapter 12 : Statistical Analysis – Part 2 (Linear and Multiple Regression)
- Chapter 13 : Case Studies
- Chapter 14 : SQL Fundamentals
- Chapter 15 : SAS SQL and Joining
- Chapter 16 : Working with Tables Using SAS SQL
- Chapter 17 : Practical Application of SAS SQL
-
Chapter 18 : Fundamentals of Utilizing SAS Indexes
- Intro to Indexes/Indices
- Should You Use an Index?
- Types of Indices
- Index Options
- Testing with Large Datasets
- Selecting Variable(s) for Your Index
- PROC Datasets and WHERE Expression
- BY Statement (Sorting Variables, While Exploiting Your Index)
- Handling Common Tasks with an Indexed Dataset
- Updating the Master Dataset with New Variables or Observations
-
Chapter 19 : Macro Facility Fundamentals
- Types of Macro Variables
- Don't Lose Track of Your Macro Variables
- Macro Variable Assignment Rules
- Masking Special Characters
- Macro Functions (%Index and %Upcase)
- Macro Functions 2 (%Scan)
- Creating a Macro Variable (Helps You Modify Data Easier)
- Macro Programs Introduction
- Creating a Macro Example 1 (Greater Flexibility and Useful for Repetitive Coding)
- Creating a Macro Example 2 (Unique Sales Reports for Different Days)
- Creating a Macro Example 3 (Calculating Average Sales for Multiple Years)
- Debugging Options
- Storing Macros (External)
- Brainstorming for Logistic Macro Case Study
- Logistic Macro, Case Study – Part 1
- Logistic Macro, Case Study – Part 2
- Chapter 20 : Introduction to SAS Predictive Modeling Using Logistic Regression
- Chapter 21 : SAS Model – Predictive Modeling, Understanding the Problem and the Data
-
Chapter 22 : SAS Predictive Modeling, Prepare the Input Variables
- Sources, Patterns, and Mechanisms of Missing Data
- Evaluating Missing Data Patterns with SAS
- 3 Phase Multiple Imputation Process Using SAS
- Considering the Output from PROC MI
- Oversampling and Adjusting for Oversampling
- Categorical Inputs
- Variable Clustering
- Multicollinearity
- Subset Selection
- Parameter Estimates
- Chapter 23 : SAS Predictive Modeling, Evaluation Metrics
- Chapter 24 : Extra Content
Product information
- Title: Complete SAS Programming Guide - Learn SAS and Become a Data Ninja
- Author(s):
- Release date: December 2022
- Publisher(s): Packt Publishing
- ISBN: 9781837633531
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