Predictive Modeling using SAS programming

Programme highlights

Placement assistance

All the students would get placement assistance and help in cracking interviews

Mock interview will be conducted those interested 


Duration : 2 MONTHS

Weekdays : 8 am to 10 am

Week ends: 

Saturday: 10 am to 12 Sunday: 1 pm to  3 pm

 FLEXIBLE TIMING : 60 hrs

Training Material

Training Material will be provided to student module wise

Industry Experts as faculty

Instructors are having industry experience and would relate all the training to industry use case

Course Fees

 Course Fee: Rs. 35,000 (Classroom)

                   Rs. 25,000 (Online live)

The fees can be paid in 3 installments

Certifications (Specialization)

Student can choose the following specialization for Industry knowledge and relevant projects.

Specialization : Banking |Retail | Fintech

COURSE CONTENT

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Lesson 1: Analytics Overview

  • Definition of Analytics 
  • Types of Analytics  
  • Analytics Problem Types 
  • Widely used tools and analytical techniques 

Lesson 2: Set up the environment

  • Introduction of GUI 
  • Library statement, understanding of PDV 
  • Import / Export of Data 
  • Variable Attributes 
  • Basic Procedures 

Lesson 3: Combining/Modifying Datasets

  • Combining Data Sets Methods 
  • Concatenation 
  • Interleaving 
  •  One to One Reading 
  • One to One Merging 
  • Data Manipulation steps and tools 

Lesson 4: PROC SQL & advance sas

  • Introduction and Advantages 
  • Options and Syntax - Understanding of Select Statement 
  • Joins in SQL 
  • Merge v/s Join 
  •   Need for SAS Macros 
  • Macro Variables 
  • Automatic Macro Variables 
  • User-defined Macro Variables 
  • Macro Functions 
  • SYMBOLGEN System Option 
  • SQL Clauses for Macros 
  • The %Macro Statement 

Lesson 6: Basic Statistics & Procedures

  • Descriptive Statistics 
  • Inferential Statistics 
  • Hypothesis testing 
  • Non parametric tests
  •  PROC UNIVARIATE 
  • PROC MEANS 
  • PROC FREQ 
  • PROC CORR 
  • PROC REG 
  • PROC ANOVA  

Model Development & Projects

  • Data Preparation 
  • Data Type Conversion 
  • Missing Value Treatment
  • Summarizing Data 
  •  Clustering  Introduction  Clustering Methodology  Data Preparation  K Means Clustering  Cluster Profiling 
  •  Decision Trees  Introduction  Creating Decision Trees 
  •  Linear Regression  Introduction  Linear Regression in SAS  Diagnostics 
  •  Logistic Regression  Introduction  Logistic Regression in SAS  Diagnostics 
  • Time Series Analysis
  • 5 Projects
  • 10 Assignments
  • 2 Exam

Call us at +91 9740006768 to know more