Data Science with SAS

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Why this course ?

This course helps you prepare you for the following certification exam(s): (I) SAS Certified Base Programmer for SAS 9 (II) SAS Certified Advanced Programmer for SAS 9.
This course is for SAS programmers who prepare data for analysis.
Advanced SAS focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. It also covers how to process SAS data using Structured Query Language (SQL).
Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program

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Weekend/Weekday|Live Classes 30% OFF

Program Duration
and Fees

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Duration

125 Hours

Price

39990

Features

Training Features

Led by an expert instructor

Instructors are best in the field of Statistics and having industry experience.

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.

E. BASE SAS PROGRAMMING

Description

Introduction to SAS Programming 1: Essentials

• An Overview of the SAS System

Getting Started With the SAS® System

• Introduction to SAS Programs
• Running SAS Programs
• Mastering Fundamental Concepts
• Diagnosing and Correcting Syntax Errors
• Exploring Your SAS Environment

Getting familiar with SAS Data Sets

• SAS Data Libraries

Producing List Report

• Getting Started With the PRINT Procedure
• Sequencing and Grouping Observations
• Identifying Observations
• Special WHERE Statement Operators

Enhancing Output

• Customizing Report Appearance
• Formatting Data Values
• Creating HTML Reports

Creating SAS® Data Sets

• Reading Raw Data Files: Column Input
• Reading Raw Data Files: Formatted Input
• Examining Data Errors

• Assigning Variable Attributes
• Changing Variable Attributes
• Reading Excel Spreadsheets

DATA Step Programming

• Reading SAS Data Sets and Creating Variables
• Conditional Processing
• Dropping and Keeping Variables
• Reading Excel Spreadsheets Containing Date Fields

Combining SAS® Data Sets

• Match-merging Two or more SAS Data Sets
• Simple Joins Using the SQL Procedure

Producing Summary Reports

• Introduction of Summary Reports.
• Basic Summary Reports
• The Report Procedure
• The Tabulate Procedure

Introduction to Graphics

• Producing Bar and pie Chart
• Enhancing output
• Producing Plots

Introduction to SAS Programing II

• Overview
• Review of SAS basics
• Review of DATA Step Processing
• Review of Displaying SAS Data Sets
• Working with Existing SAS Data Sets

Controlling Input and Output

• Outputting Multiple Observations
• Writing to Multiple SAS Data Sets
• Selecting Variables and Observations
• Writing to an External File

Summarizing Data

• Creating an Accumulating Total variable
• Accumulating Totals for a Group of Data

Reading & Writing Different Types of Data

• Reading Delimited Raw Data Files
• Controlling When a Record Loads
• Reading Hierarchical Raw data Files

Data Transformations

• Introduction
• Manipulating Character values
• Manipulating Numeric values
• Manipulating Numeric values based on Dates
• Converting variable Type

Debugging Techniques

• Using the PUT Statement
• Using the DEBUG Option

Processing Data Iteratively

• Do Loop Processing
• SAS Array Processing
• Using SAS Arrays

Combining SAS Data Sets

• Concatenating SAS Data Sets
• Merging SAS Data Sets
• Combining SAS Data Sets : Additional Features

F. ADVANCE SAS

Description

Advance SAS Programming

• overview of SAS Foundation
• course logistics
• course data files
• Introducing the Structured Query Language

Basic Queries

• overview of the SQL procedure
• specifying columns
• specifying rows

SQL Joins

• introduction to SQL joins
• inner joins
• outer joins
• complex SQL joins

Subqueries

• noncorrelated subqueries
• in-line views

Set Operators

• Introduction to set operators
• the UNION operator
• the OUTER UNION operator
• the EXCEPT operator
• the INTERSECT operator

Creating Tables and Views

• creating tables with the SQL procedure
• creating views with the SQL procedure

Advanced PROC SQL Features

• dictionary tables and views
• using SQL procedure options
• interfacing PROC SQL with the macro language

Macro Variables

• Introduction to macro variables
• Automatic macro variables
• macro variable references
• user-defined macro variables
• delimiting macro variable references
• macro functions

Macro Definitions

• defining and calling a macro
• macro parameters

DATA Step and SQL Interfaces

• creating macro variables in the DATA step
• Indirect references to macro variables
• creating macro variables in SQL

Macro Programs

• conditional processing
• parameter validation
• iterative processing
• global and local symbol tables

G. SAS PREDICTIVE MODELING

Description

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

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
• Multiple linear Regression with their Assumptions
• Concept of Multiocollinearity
• 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

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

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

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

Description

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Testimonials

Bhushan Kumar

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Sumit Sen

Excellent live online teaching. Trainer is very friendly and kind to us.Best training institute for SAS Training.

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I was worried about online training, but after attending the Hands-On live online Certificate course in R programming, my concept changed. It is fully practical and interactive. Really helpful instructor.

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Good Trainer. Have good teaching skill set. I personally recommend people to join Hands-On System.

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Great to do if, you want to dive into data science or machine learning. It is a must do, if you have no idea of statistics and data science you can do this course

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Trainers are very calm and knowledgeable, thank you very much to Hands-On System.Really enjoyed and look forward to learning more in the future. Well structured data science course with all the popular software Python,SAS and R and well organised.I found the course very good and learnt a lot that has benefitted my role in work.

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I have attended the Live online Certificate in Digital Marketing course.My Instructor demonstrated the entire course thoroughly with practical case studies. I really enjoyed the course.

David Smith

Highly recommended if you want to learn or improve your Data Science skill. This course is fantastic -- the concepts & methods that are directly applicable to my field. The numerous examples and case studies are really useful for understanding all the concepts. The instructor is very good and shares her knowledge at a good pace.For the value, the course was worth every penny and more.

Manisha

Data Science course explained in detail and with examples.great case studies which helped me to understand more easily.

Sumitava Maji

The course structure is very good and useful for the student.

Soumen Lahiri

I have completed Data Science course from Hands-On System. It is really a great experience.

Data Science with SAS

Why this course ?

Duration

Price

Features

Led by an expert instructor

Advantages of attending this course

Delivered online and classroom

E. BASE SAS PROGRAMMING

Description

F. ADVANCE SAS

Description

G. SAS PREDICTIVE MODELING

Description

Description

Testimonials

Bhushan Kumar

Vishal Jaiswal

Priti Basu

Harmandeep Kaur

Partha Roy

Ajanta Konar

Prakash

Vijoy Agarwal

Sumit Sen

Bidisha Paul

Sayan Basu

Priyanka Roy

Kopil Strivastava

Paramita Banerjee

Bhaskar Gupta

Kallol Sengupta

David Smith

Manisha

Sumitava Maji

Soumen Lahiri

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