Quantitative Data Management & Analysis With SAS Course

Course Overview

Welcome to a comprehensive course on SAS, a powerful statistical software used for data management, analysis, and graphics. SAS offers advanced functionalities such as forecasting, survival analysis, and time series analysis, all accessible through a user-friendly graphical interface. With support from a vibrant user community, you'll find ample resources and guidance as you learn.

Course Objectives

By the end of this course, participants will be able to:

• Grasp the entire data analysis workflow from a high-level perspective.
• Build a solid foundation in SAS, from basics to advanced techniques.
• Utilize various SAS techniques to accomplish analytical tasks.
• Conduct step-by-step statistical analysis, including descriptive statistics, hypothesis testing, and linear regression.
• Import data using different techniques suitable for various data types.
• Apply important SAS functions for effective programming.
• Understand metadata concepts like formats, informats, labels, and lengths.
• Prepare and manipulate data to ensure it's analysis-ready.
• Perform dataset manipulations such as subsetting and transposition.
• Interpret results from statistical analyses accurately.

Duration

5 Days

Who Should Attend

This course is designed for statisticians, analysts, budding data scientists, and beginners eager to learn data analysis with SAS.

Course Content

Module 1: Research Design, Analysis, and Interpretation

• Introduction to research and the research process
• Problem definition and research design
• Methods for data preparation and analysis strategy
• Hypothesis testing, frequencies, and cross-tabulation

Module 2: Understanding the Workflow

• Overview of SAS basics and data importing techniques
• Create and manage datasets using SAS Studio
• Import data from Excel and pre-existing SAS datasets

Module 3: Data Manipulation Techniques

• Naming conventions and IF THEN/ELSE statements
• Utilize SAS functions and DO loops for data manipulation
• Subset and append datasets using various techniques

Module 4: Advanced Dataset Manipulation

• Merge datasets and perform comprehensive tasks
• Transpose data for in-depth analysis

Module 5: Descriptive Statistics and Analysis

• Use PROC PRINT and CONTENTS procedures
• Calculate descriptive statistics and explore data

Module 6: Performing Statistical Analysis

• Conduct T-tests and ANOVA for data comparison
• Explore non-parametric analysis methods

Module 7: Linear Regression and Predictive Analysis

• Implement linear regression models to predict outcomes
• Incorporate categorical variables with dummy variables