Base SAS Programming

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

Learning More

o SAS resources

o next steps

SAS Programming 2: Data Manipulation Techniques

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 match-merging

Creating and Maintaining Permanent Formats

o creating permanent formats

SAS Analytics

• 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

• 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

• Sampling Theory And Estimation

• Concept of population and sample

• Techniques of Sampling

• Sampling Distributions

• Theory of Estimation

• Concept of estimation

• Different types of Estimation

• 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.

• Analysis of variance

• One Way Anova

• Two Way Anova

• 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

• 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

• 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

• 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.

• 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

1. INTRODUCTION ON R

i. What is R

ii. What is S

iii. History of R

iv. Features of R

v. SAS versus R

2. OBTAINING AND MANAGING R

i. Installing R

ii. Packages

iii. Input/output

iv. R interfaces

v. R Library

3. BASIC OPERATIONS IN R

4. DIFFERENT DATA TYPES AND DATA

STRUCTURES IN R

5. SUBSETTING IN R

6. ADDITIONAL TOPICS ON DATA

STRUCTURES

7. IMPORTING DATA SETS IN R

8. R LOOPS AND SPECIAL FUNCTIONS

9. CALCULATION OF COMMISSION

AND SIMPLE INTEREST

10. PLOTS AND CHARTS IN R

11. MERGING AND SORTING FUNCTIONS IN R

12. SUMMARISING DATA

13. CALCULATIONS OF THE MEASURES OF

CENTRAL TENDENCY AND MEASURES OF

VARIABILITY

R ANALYTICS

1. TESTING OF HYPOTHESIS

2. ANOVA

3. MANOVA

4. LINEAR REGRESSION

5. LOGISTIC REGRESSION

6. CLUSTER ANALYSIS

7. EXPLORATORY FACTOR ANALYSIS

8. DECISSION TREES

9. TIME SERIES FORECASTING

10. TEXT MINING ANALYSIS

11. MARKET BASKET ANALYSIS