SAS Base & Predictive modelling

Currency:

Why this course ?

  • 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.

Program Duration
and Fees

Duration

N/A

Price

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Features

  • Led by an expert instructor
    Instructors are best at the field of Statistics and also having industry exposure.
    Advantages of attending this course
    No programming knowledge is required for this course.
    Delivered online and classroom
    All coursework is available 24/7 online or offline.

Description

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

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Course Name: SAS Base & Predictive modelling