Description

PAII10 – SAP Predictive Analytics freshly updated course will provide you with the skills to take advantage of the significant improvements and new capabilities of SAP Predictive Analytics.

 

Radiant Techlearning offers “PAII10 – SAP Predictive Analytics” training program in Classroom & Virtual Instructor Led / Online mode.

 

Learning Objectives

This course will prepare professionals to:

  • Understand predictive analytics concepts and approaches, as well as how they are implemented within the context of the SAP Predictive Analytics tool.
  • Develop the ability to use Predictive Analytics within a Data Science project context.
  • Be able to use automated analytics capabilities to build, score and implement classification, regression and time-series models.
  • Use Data Manager to prepare and manipulate data to support modelling.
  • Understand and implement Predictive Factory to import, build and schedule models.
  • Build Social and Recommendation models
  • Introduction to Expert Analytics and the Predictive Analytics Library (PAL)

Prerequisites

  • Basic statistical skills and a background in Business Analytics and Data Modelling

Audience Profile

  • Application Consultant
  • Business Analyst
  • Program / Project Manager
  • System Administrator
  • Technology Consultant

Course Details

Module 1: Introduction to Predictive Analytics

  • Welcome introduction and agenda

Module 2: Introduction to Predictive Analytics

  • Describing Predictive Analytics
  • Describing SAP Predictive Analytics
  • Outlining Predictive Analytics Project Frameworks
  • Detailing SAP Analytics Use Case examples

Module 3: Foundations of Automated Analytics (1)

  • Explaining SAP Predictive Analytics Data Types and Storage
  • Defining Automated Data Encoding Fundamentals
  • Describing Model Building in SAP Predictive Analytics

Module 4: Predictive Factory

  • Describing Predictive Factory
  • Listing the SAP Predictive Factory Roles
  • Describing the SAP Predictive Factory Server Setup
  • Checking Data Descriptions
  • Building a Time Series Model in SAP Predictive Factory
  • Explaining Segmented Time Series Modeling
  • Describing Classification Models
  • Building a Classification Model in SAP Predictive Factory
  • Explaining Classification Model Output
  • Interpreting the Error Matrix
  • Applying a Classification Model
  • Creating and Scheduling Tasks
  • Explaining Regression Modeling in SAP Predictive Factory
  • Building a Regression Model
  • Describing Deviation Analysis
  • Importing Models from SAP Predictive Analytics into Predictive Factory

Module 5: Data Manager

  • Explaining Data Preparation
  • Outlining Data Manipulation in SAP Predictive Anaytics
  • Outlining Data Manager
  • Using Data Manager and Toolkit

Module 6: Classification Modeling with Modeler

  • Building a Classification Model with Modeler
  • Explaining the Confusion Matrix
  • Applying a Model
  • Performing Deviation Analysis in Modeler
  • Outlining Advanced Functionality
  • Advanced Data Description Functionality

Module 7: Regression Modeling with Modeler

Module 8: Training a Regression Model

Module 9: Explaining Regression Model Output

Module 10: Improving Regression Model Performance

Module 11: Applying a Regression Model

Module 12: Clustering with Automated Analytics

Module 13: Explaining Clustering and Segmentation

Module 14: Describing Supervised and Unsupervised Clustering

Module 15: Explaining Cluster Range

Module 16: Explaining Cluster Model Outputs

Module 17: Applying the Cluster Model

Module 18: Building a Cluster Model in SAP Predictive Analytics

Module 19: Time Series with Modeler

Module 20: Building aTime Series Model with Modeler

Module 21: Social and Recommendation

  • Building a Social Network (link) Model using Telco CDR Data
  • Building a Product Recommendation Model using Link Analysis

Module 22: Foundations of Automated Analytics (2)

  • Outlining Data Partition Strategies in SAP Predictive Analytics
  • Explaining the Foundations of Automated Modeling
  • Describing the Data Encoding Process
  • Advanced Model Curves

Module 23: Final Exercise

  • SAP Creating a Retail Analysis

FAQs

Q: What do you mean by Predictive Analytics?

 

A: Predictive analytics is the part of the advanced analytics which is used to produce predictions about unknown later events.

it uses many different techniques like data mining, statistics for machine learning artificial intelligence and modeling to analyze current data to make predictions for future.

 

Q: What are the types of Predictive Analytics?

 

A: The different types of predictive analytics are:

  • Predictive models
  • Descriptive models
  • Decision models

 

Q: What is the process of Predictive Analytics?

 

A: The process if as follows:

  • Define project
  • Data collection
  • Data analysis
  • Statistics
  • Modeling
  • Deployment
  • Model monitoring

 

Q: Where is Radiant Techlearning Located?

 

A: Radiant Techlearning is headquartered in Electronic city & technology hub of Northern India, Noida, which is surrounded by several large multinational, medium & small Software companies.

We have our offices located all across the country and partners across the globe.

 

Q:  What is the schedule of training ?

 

A: Radiant Techlearning offers training program on the weekday, weekend and a combination of weekdays and weekends.  You can always choose the schedule that best suits to your need.

 

Q: What is the importance of predictive Analytics?

 

A: The importance of predictive analytics are:

  • Detecting fraud. 
  • Optimizing marketing campaigns.
  • Improving operations. 
  • Reducing risk.

 

Q: What are the applications of Predictive Analytics?

 

A: Some of the applications are:

  • Business
  • Child protection
  • Clinical decision support systems
  • Predicting outcomes of legal decisions
  • Portfolio, product or economy-level prediction
  • Underwriting

 

Q: What if I miss a class on a particular day?

 

A: We would always recommend you to attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if due to some contingency if you have to skip the class Radiant Techlearning would help you with the recorded session of that particular day.  However, those recorded sessions are not meant only for personal consumption and not for distribution or any commercial

 

Q: What are the various techniques of Predictive Analytics?

 

A: The different techniques are:

  • Regression techniques
  • Linear regression model
  • Discrete choice models
  • Logistic regression
  • Probit regression
  • Multinomial logistic regression
  • Logic versus probit
  • Time series models
  • Survival or duration analysis
  • Classification and regression trees (CART)
  • Multivariate adaptive regression splines
  • Machine learning techniques

 

Q: What is Predictive Analytics Data Mining?

 

A: Data Mining is a crucial step in the process of predictive analytics. It is used to extract most useful details or information from the current data to predict trends. It aids the analytics team by finding the relevant data to analyze and be used in the predictive models to find what will happen later on in the business.

 

Q: Does this training program include any project?

 

A: Yes, Radiant will provide you the most updated, high valued and relevant real time projects and case studies in each training program.

We included projects in each training program from fundamental level to advance level so that you don’t have to face any difficulty in future. You will work on highly exciting projects and that will upgrade your skill, knowledge and industry experience.

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