SAS is a flexible, extensible 4GL and web-based interface for data access, transformation and reporting.
This course is for users who want to learn how to write SAS programs. It is the entry point to learning SAS programming and is a prerequisite for many other SAS courses.
This course would make use of SAS to teach you all the basics of statistics, hypothesis testing, t-test, ANOVA, Linear and Logistic Regression
SAS Analytics skills are the most valuable skills to have in today's job market.
SAS Programming 1: Essentials
Learn how to
navigate the SAS windowing environment
navigate the SAS Enterprise Guide programming
environment
read various types of data into SAS data sets
create SAS variables and subset data
combine SAS data sets
create and enhance listing and summary reports
validate SAS data sets.
Course Contents
Introduction
an overview of SAS foundation
course logistics
course data files
SAS Programs
o introduction to SAS programs
o submitting a SAS program
o working with SAS program syntax
Accessing Data
Producing Detail Reports
o submitting report data
o sorting and grouping report data
o enhancing reports
Formatting Data Values
o using SAS formats
o creating user-defined formats
Reading SAS Data Sets
o reading a SAS data set
o customizing a SAS data set
Reading Spreadsheet and Database Data
o reading spreadsheet data
o reading database data
Reading Raw Data Files
o introduction to reading raw data files
o reading standard delimited data
o reading nonstandard delimited data
o handling missing data
Manipulating Data
o using SAS functions
o conditional processing
Combining SAS Data Sets
o concatenating data sets
o merging data sets one-to-one
o merging data sets one-to-many
o merging data sets with nonmatches
Creating Summary Reports
o using the FREQ procedure
o using the MEANS and UNIVARIATE
procedures
o using the Output Delivery System
Learn how to
o control SAS data set input and output
o combine SAS data sets
o summarize, read, and write different types
of data perform DO loop and SAS array
processing
o transform character, numeric, and date
variables
Course Contents
Introduction
o an overview of SAS foundation
o course logistics
o course data files
Controlling Input and Output
o writing observations explicitly
o writing to multiple SAS data sets
o selecting variables and observations
Summarizing Data
o creating an accumulating total variable
o accumulating totals for a group of data
Reading Raw Data Files
o reading raw data files with formatted input
o controlling when a record loads
Data Transformations
o manipulating character values
o manipulating numeric values
o converting variable type
Debugging Techniques
o using the PUTLOG statement
Processing Data Iteratively
o DO loop processing
o conditional DO loop processing
o SAS array processing
o using SAS arrays
Data Transformations
o manipulating character values
o manipulating numeric values
o converting variable type
Debugging Techniques
o using the PUTLOG statement
Processing Data Iteratively
o DO loop processing
o conditional DO loop processing
o SAS array processing
o using SAS arrays
Restructuring a Data Set
o rotating with the DATA step
Combining SAS Data Sets
o using data manipulation techniques with matchmerging
Creating and Maintaining Permanent Formats
o creating permanent formats
1. Introduction To Analytics and Basic Statistics
Types of Analytics
Properties of Measurements
Scales of Measurement
Types of Data
Measures of Central Tendency
Measures of Dispersion
Measures of Location
Presentation of Data
Skewness and Kurtosis
2. Introduction to Probability Theory
Three Approaches towards Probability
Concept of a Random Variable
Probability Mass Function
Probability Density Function
Expectation of A Random Variable
Probability Distributions
3. Sampling Theory And Estimation
Concept of population and sample
Techniques of Sampling
Sampling Distributions
4. Theory of Estimation
Concept of estimation
Different types of Estimation
5. Testing of hypothesis
Concept of hypothesis
Null hypothesis
Alternative hypothesis
Type-I error
Type-II error
Level of Significance
Confidence Interval
Parametric Tests and Non Parametric Tests
One Sample T test
Two independent sample T test
Paired Sample T test
Chi square Test for Independence of Attributes.
6. Analysis of variance
One Way Anova
Two Way Anova
7. Exploratory Factor Analysis
Principal Component Analysis
Estimating the Initial Communalities
Eigen Values and Eigen Vectors
Correlation Matrix check and KMO-MSA check
Factor loading Matrix
Diagrammatic Representation of Factors
Problems of Factor Loadings and Solutions
8. Cluster Analysis
Types of Clusters
Metric and linkage
Ward’s Minimum Variance Criteria
Semi-Partial R-Square and R-Square
Diagrammatic Representation of clusters
Problems of Cluster Analysis
9. Linear Regression and Multiple Linear Regression
Concept of Regression and features of Linear line.
Assumptions of Classical Linear Model
Method of Least Squares
Understanding the Goodness of Fit
Test of Significance of The Estimated Parameters
Multiple linear Regression with their Assumptions
Concept of Multocollinearity
Signs of Multicollinearity
The Idea Of Autocorrelation
10. Logistic Regression
Concept and Applications of Logistic Regression
Principles Behind Logistic Regression
Comparison between Linear probability Model and Logistic Regression
Mathematical Concepts related to Logistic Regression
Concordant Pairs, Discordant Pairs and Tied Pairs
Classification Table
Graphical Representation Related to logistic Regression.
11. Time Series Analysis
Concept of Time Series and its Applications
Assumptions of Time Series Analysis
Components of Time Series
Smoothening techniques
Stationarity
Random Walk
ARIMA Forecasting
Box Jenkins Technology
Merits and Demerits of BJ Technology
Module I : Introduction to Analytics
Module II :SAS Programming
Base SAS Programming
SAS Programming 1: Essentials
SAS Programming 2: Data Manipulation Techniques
Module III :SAS Analytics