Statistical Data Analysis with SAS

Name: Statistical Data Analysis with SAS
Rating: 4.6 (829 reviews)

Understanding the Data Flow in SAS: Learn SAS Programming from Data Importing, Management, Manipulation to Data Analysis

Created byJohn Zhong | JSTARTS.COM

Last updated 7/2024

English

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What you'll learn

Learn SAS and be confident on your data analysis skills
Learn to accomplish a task with various SAS techniques, with tons of examples and quizes
Learn step-by-step statistical analysis from descriptive statistics, hypothesis testing to linear regression
Learn data importing with different techniques for variuos type of data
Use many important functions to make SAS programming easy
Advanced concepts of meta data: formats and informats, labels, lengths, etc.
Learn the manipulation techniques to prepare the data and make the data analysis-ready
Perform dataset manipulations: subsetting, transposition, etc.
Be able to properly interpret the results from statistical analyses
I am giving out one course for free. Check out the last lecture after you enroll

Course content

11 sections • 46 lectures • 4h 25m total length

L1 - How to Get the Most out of the Course7:23
L2 - SAS OnDemand Registration12:23
SAS OnDemand was rolled out a while ago in replace of the outdated SAS University Edition. The cloud-based platform allows users to access anywhere from any device, and it's installation-free.
L3 - Why use SAS (you may skip this introductory lecture)6:20
In this lecture, you will get an idea of what SAS is and what SAS does. SAS is dominant in many industries and will continue to grow. So you will get some good reasons to learn SAS. This will be mostly interesting to those of you who want to start a career in data analysis with SAS.
You may skip this lecture to proceed into the technical part.

L4 - Overview1:18
L4 - The Workflow2:59
Learn the SAS workflow from planning and data preparation to analysis and reporting, using data steps and proc steps to prepare, analyze, interpret results, and generate reports.
L4 - Ask Questions Wisely to Assist Project Planning8:41
This lecture is updated to delivery more contents and examples. I am trying to extend the previous topic - the workflow, by giving a case study as an example to fully demonstrate the idea that project planning by asking questions wisely is a must to a successful data analysis task. I make this topic very important and would highly recommend that you go through it first before getting into the technical parts.
L4 - SAS Basics10:42
L4 - SAS Basics
L4 - Data Importing - Instream data and Proc Import6:26
L4 - Import Wizard for SAS 9.x4:08
Use the SAS 9.x import wizard to import an Excel sheet, store the dataset in a library, name it, save the SAS code to a standalone file, and finish.
L4 - Data Importing in SAS Studio2:57
Import excel data into SAS Studio from a shared folder by selecting worksheet, outputting to the WORK library, and enabling generate SAS variable names; CSV or TXT imports are supported.
L4 - Bring in Data from Pre-existing SAS Dataset and Create Permanent Dataset2:51
Project Part 1: Data importing - excel data (a must to the other assignments)
Section Summary in Text2:21
This is a summary text for this section. Each section will include a summary text for future reference.

L5 - Overview1:18
Master data manipulation in SAS by creating new variables with if-then-else, applying do loops, and performing subsetting, appending and concatenation, merging, and transposition to prepare clean datasets for analysis.
L5 - Naming Convention and Variable Types8:04
Master SAS naming conventions for library, dataset, and variable names, including length limits, allowed characters, and case-insensitivity, then distinguish numeric versus character variables, length requirements, and missing values.
L5 - Naming Convention and Variable Types
L5 - IF THEN/ELSE Statement4:20
L5 - IF THEN/ELSE Statement
L5 - Keep and Drop Variables2:30
L5 - Keep and Drop Variables
Section Summary in Text0:49

L8 - Overview1:04
L8 - Merge9:30
Learn how to merge SAS datasets using a by statement and proc sort to create a combined dataset. Explore match merge and one-to-one merge and their sorting requirements.
L8 - Merge
Merge two datasets into a single dataset
Project part 3: Merge two datasets
L8 - Transpose8:26
Transpose the following data
A comprehensive task using several techniques to subset, transpose data
Section Summary in Text0:19

L10 - Overview and Introduction to Hypothesis Testing4:15
Explore hypothesis testing concepts with one-sample and three-or-more-sample t tests and ANOVA, including null and alternative hypotheses, alpha levels, p-values, and the role of descriptive statistics.
L10 - One Sample T-Test10:33
L10 - One Sample T-Test
L10 - Two Sample T-Test6:05
L10 - Two Sample T-Test and paired T-Test
L10 - 3+ Sample ANOVA6:46
Explore analysis of variance for comparing three or more means with SAS proc Anova. Learn to use class and model statements, interpret p-values, and apply Duncan, Tukey, and Bonferroni methods.
L10 - 3+ Sample ANOVA
L10 - Non-parametric Analysis1:58
A comprehensive assignment for hypothesis test
Section Summary in Text1:06

L11 - Overview1:30
L11 - Linear Regression17:08
Project Part 5: Use Linear Regression model to predict the MSRP
L11 - Dummy Variable7:58
Project Part 6: Try to include some categorical variables into the model
Thank You and Please Leave Your Comments for Future Improvement of the Course!1:36
Thank you guys, I hope you've enjoyed this course! And don't forget to check out my personal website for discounted coupons for my other courses. Hope to see you soon!
Project Part 7: Some code examples for you to digest
Section Summary in Text0:47

Requirements

No SAS background required
Basic data intuition
Basic knowledge of statistics is preferred

Description

COURSE ABSTRACT

This course aims to provide a comprehensive introduction to the SAS analytic software for Windows. Through a mixture of lectures and in-class examples, quizzes, and take-home assignments, students will gain experience using the SAS system for data manipulation, management and analysis. You will also expect A LOT of extracurricular learning materials for self-pace learning, treat it as a BONUS! Emphasis will be placed on the skills and techniques necessary for efficient data manipulation, management and analysis. It is designed for students with little to no background with SAS, and an understanding of the basic statistical concepts. This will be an excellent choice for your first SAS introduction course for your data analysis career.

Plus, you will get a FREE course - SAS Data Issue Handling and Good Programming Practice (check out in the bonus lecture)!!!

WHAT DO I EXPECT?

A comprehensive course design from SAS basics to statistical analysis
Many in-class examples, exercises and take-home assignment
Master various techniques for data importing
Solid understanding of variable attributes, and learn various character/numeric functions
IF-THEN/ELSE statements
Do loop and counter variables
Master DATA step with Concatenation, Merge, etc.
Exposed to several useful PROC step (PRINT, SORT, TRANSPOSE, etc.).
Descriptive statistics procedures (MEANS, UNIVARIATE, FREQ)
Hypothesis testing (UNIVARIATE, TTEST, ANOVA)
Correlations (CORR)
Regression (REG)

PREREQUISITE COURSES AND KNOWLEDGE:

No SAS background required;
Basic knowledge of statistics is preferred.

Who this course is for:

Anyone from other programming fields that are interested in SAS
Anyone with no SAS background
Anyone who wants to understand the workflow in SAS

Statistical Data Analysis with SAS

What you'll learn

Explore related topics

Course content

L1 - Course Introduction3 lectures • 26min

Understanding the Workflow9 lectures • 42min

Data Manipulation - Naming Convention and IF THEN/ELSE Statement5 lectures • 17min

Data Manipulation - SAS Functions and DO Loop4 lectures • 32min

Dataset Manipulation - Subset and Append4 lectures • 15min

Dataset Manipulation - Merge and Transposition4 lectures • 19min

Descriptive Statistics - Frequency and Univariate Analysis4 lectures • 29min

One, Two Sample T-Test and ANOVA6 lectures • 31min

Linear Regression - Predicting the Outcome5 lectures • 29min

Bonus section: Data Visualization using SAS1 lecture • 26min

Requirements

Description

Who this course is for: