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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

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

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-L1 - Course Introduction
3
1.1-L1 - How to Get the Most out of the Course
1.2-L2 - SAS OnDemand Registration
1.3-L3 - Why use SAS (you may skip this introductory lecture)
2-Understanding the Workflow
11
2.1-L4 - Overview
2.2-L4 - The Workflow
2.3-L4 - Ask Questions Wisely to Assist Project Planning
2.4-L4 - SAS Basics
2.5-L4 - SAS Basics
2.6-L4 - Data Importing - Instream data and Proc Import
2.7-L4 - Import Wizard for SAS 9.x
2.8-L4 - Data Importing in SAS Studio
2.9-L4 - Bring in Data from Pre-existing SAS Dataset and Create Permanent Dataset
2.10-Project Part 1: Data importing - excel data (a must to the other assignments)
2.11-Section Summary in Text
3-Data Manipulation - Naming Convention and IF THEN/ELSE Statement
8
3.1-L5 - Overview
3.2-L5 - Naming Convention and Variable Types
3.3-L5 - Naming Convention and Variable Types
3.4-L5 - IF THEN/ELSE Statement
3.5-L5 - IF THEN/ELSE Statement
3.6-L5 - Keep and Drop Variables
3.7-L5 - Keep and Drop Variables
3.8-Section Summary in Text
4-Data Manipulation - SAS Functions and DO Loop
6
4.1-L6 - Overview
4.2-L6 - SAS Functions
4.3-L6 - SAS Functions
4.4-L6 - DO Loop
4.5-L6 - DO Loop
4.6-Section Summary in Text
5-Dataset Manipulation - Subset and Append
6
5.1-L7 - Overview
5.2-L7 - Subset
5.3-Use WHERE statement to subset data
5.4-L7 - Concatenation (Append)
5.5-Project Part 2: Concatenate 3 of the 4 datasets into one.
5.6-Section Summary in Text
6-Dataset Manipulation - Merge and Transposition
9
6.1-L8 - Overview
6.2-L8 - Merge
6.3-L8 - Merge
6.4-Merge two datasets into a single dataset
6.5-Project part 3: Merge two datasets
6.6-L8 - Transpose
6.7-Transpose the following data
6.8-A comprehensive task using several techniques to subset, transpose data
6.9-Section Summary in Text
7-Descriptive Statistics - Frequency and Univariate Analysis
9
7.1-L9 - Overview
7.2-L9 - Explore the Data Using PROC PRINT and CONTENTS Procedures
7.3-L9 - Explore the Data Using PROC PRINT and CONTENTS Procedures
7.4-L9 - Descriptive Statistics
7.5-L9 - Descriptive Statistics
7.6-Calculate the mean of the sample
7.7-PROC FREQ
7.8-Project Part 4: perform descriptive statistical analysis
7.9-Section Summary in Text
8-One, Two Sample T-Test and ANOVA
10
8.1-L10 - Overview and Introduction to Hypothesis Testing
8.2-L10 - One Sample T-Test
8.3-L10 - One Sample T-Test
8.4-L10 - Two Sample T-Test
8.5-L10 - Two Sample T-Test and paired T-Test
8.6-L10 - 3+ Sample ANOVA
8.7-L10 - 3+ Sample ANOVA
8.8-L10 - Non-parametric Analysis
8.9-A comprehensive assignment for hypothesis test
8.10-Section Summary in Text
9-Linear Regression - Predicting the Outcome
8
9.1-L11 - Overview
9.2-L11 - Linear Regression
9.3-Project Part 5: Use Linear Regression model to predict the MSRP
9.4-L11 - Dummy Variable
9.5-Project Part 6: Try to include some categorical variables into the model
9.6-Thank You and Please Leave Your Comments for Future Improvement of the Course!
9.7-Project Part 7: Some code examples for you to digest
9.8-Section Summary in Text
10-Bonus section: Data Visualization using SAS
1
10.1-Bonus Lecture: How to Draw Statistical Graphs using SAS
11-Source Codes
1
11.1-Download source code of the course