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SAP Predictive Analytics Training
Various SAP applications are implanted with built-in predictive and machine learning models for a precise set of scenarios and use cases with a single component called Predictive Analytics Integrator. Predictive Analytics Integrator behaves as a connection between the model authoring environment to insert the models and visions directly into the application.
SAP Predictive analytics work with preceding data, based on which the data visualization reports are formed. With the help of these reports, the company or an enterprise is able to recognize the unrevealed stuff and can arrive at a better conclusion. It comprises some of the functionalities from HANA. It is known as statistical analysis and data mining solution used to build the predictive models.
With SAP Predictive Analytics, users can:
· Create, modify, and extend predictive and machine models
· Manage entire end-to-end life cycle of predictive and machine learning models
· Automate the deployment model with a single click
· Access implanted insights (suitable to all types of users) for bright decision making
By making use of the predictive analysis, various data investigations such as time series forecasting, trend analysis, classification analysis, segmentation and affinity analysis can be achieved. The data can be evaluated using different visualization techniques such as decision trees, cluster charts, etc.
-----------------------------------------------------------------------------------------------------------------------------------Uplatz offers comprehensive training on SAP Predictive Analytics. The SAP Predictive Analytics training delivers in-depth explanation of SAP Predictive Analytics concepts and its application. The course also provides demonstration of real-time business scenarios and how you can use SAP Predictive Analytics to add value to your analytics portfolio.
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Objectives of SAP Predictive Analytics Training
The SAP Predictive analysis online certification course offered by Uplatz is intended for participants to provide the understanding of concepts and skills of the business intelligence to evaluate bulk amount of data sets and predict future forecast related to business.
• To understand SAP Predictive Analytics concepts and approaches
• To use Predictive Analytics within a Data Science project context
• To use automated analytics capabilities to build, score and implement classification, regression and time-series models
• To use SAP Data Manager to prepare and manipulate data to support modelling
• To understand and implement Predictive Factory to import, build and schedule models
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Target Audience
· Application Consultant
· Technology Consultant
· Power user
· System Administrator
· Data Scientist
· Newbies and anyone aspiring for a career in Analytics and/or SAP
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SAP Predictive Analytics Training
Lesson 1: Introduction to Data Science and Predictive Analytics
· Describing Predictive Analytics
· Differences between Analytics, Data Mining, Data Science and Predictive Analytics
· Differences between Descriptive, Predictive, Prescriptive Analytics
· Explain the size of data required
· Davenport’s 5 stages of Analytic Competition
· Benefits of predictive analytics
· Explain where predictive analytics is currently being used in enterprise and what the user types are
· Understanding the basics of SAP Predictive modeling concepts
· Data variables, Data exploration, and Data preparation
· Data Preparation: Transforming variables, sampling, outliers, binning, missing values
· Data Cutting strategies
· Model performance indicators and ROC Curve
· Predictive Power and Predictive Confidence, random model, perfect Model, lift, Confusion Matrix
· Basic Predictive Algorithms
a) Classification - Decision Tree, Neural Network
b) Regression
c) Clustering/Segmentation
d) Association Rule Analysis
e) Outlier Analysis
f) Time Series Analysis
g) Network Analysis
Lesson 2: Predictive Analytics tools present in market
· Explain market trend for Predictive Analytics
· Which organizations are the top providers of predictive analytics?
· Understand some of the predictive analytics packages provided by SAS, SPSS, Rapid Mine, R
· Explain where predictive Analytics is placed in market
Lesson 3: Introduction to SAP Predictive Analytics
· Introduction to SAP predictive analytics toolkit PA 2
· Predictive Analytics Strategy, Data Manager
· Expert Analytics
· R integration
· SAP HANA - Predictive Analytics Library (PAL)
· SAP Automated Predictive Analytics Library (APL)
· SAP Automated Analysis
· Traditional vs. SAP automated analysis approach
· Predictive Analytics Project Planning - CRSIP DM, SEMMA
· Use case and Examples
Lesson 4: Foundation of SAP Expert Analytics
· Introductions to Expert Analytics
· How SAP Predictive Analytics can be used with SAP BI
· How SAP Predictive Analytics can be used with Big Data
· A demo for Expert Analytics
· Exercise: Forecast and visualization for time series data
· Data exploration, Data Manipulation, Data visualization
· A demo for data management
· Retail Analysis, Store Analysis
· Predictive Modeling with Expert Analysis
· A demo for some of the Algorithms used in the Predictive Analysis tool
· Combining Algorithms
· Model Statistics and compare
· Model Accuracy and Generalization
· Analysis Bank Customer with segmentation
· Integration of Expert Analysis with custom R code
· Using SAP HANA as the source for Expert Analytics
Lesson 5: Foundation of SAP Automated Analytics
· Explain SAP Automated Analytics
· Understanding Foundations and data cutting strategy
· Understanding Data encoding and data preparation
· Describe model building methodology for Automated Analytics
Lesson 6: Classification Modeling with SAP Automated Analytics
· Understanding Classification Modeling with SAP Automated Analytics
· Understanding Classification Model Output
· Understanding the Confusion Matrix
· Applying a Data Model
· Improving Predictive Power and Prediction Confidence
· Reducing the number of variables
· Data Deviation Testing and perform data deviation on data with target and without target
· Advanced functionalities like gain chart etc.
· Understanding Advanced Data Description Functionality - Composite variables and Geolocation tiles
Lesson 7: Regression Modeling with SAP Automated Analytics
· Understanding Regression Modeling with SAP Automated Analytics
Lesson 8: Clustering with SAP Automated Analytics
· Introducing Cluster Analysis and Segmentation
· Understanding Options - Target or No Target
· Differentiate between supervised and unsupervised segmentation
· Understanding Cluster Range
· Understanding Model Debriefing - Cluster profiles
· Applying the Segmentation Model option
· Describing Segmented Models - Classification and Regression
Lesson 9: Time Series with SAP Automated Analytics
· Describing Time Series with SAP Automated Analytics
· Train a time series model
Lesson 10: SAP Data Manager
· Introducing SAP Data Manager for data preparation
· Understanding Data Manipulation functionality
· Understanding the Data Manager benefits and process
Lesson 11: SAP Predictive Factory
· Introducing SAP Predictive Factory
· Completing setup, understanding architecture and roles
· Importing Models
· Model scheduling functionality
· Understanding time series segmented model
Lesson 12: Social and Recommendations Functionality
· Understanding the Social Functionality
· Understanding the Recommendations Functionality and create a retail recommendation analysis
Lesson 13: SAP Predictive Analytics Expert
· Describe SAP Predictive Analytics Expert and Predictive Analysis Library (PAL)
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About SAP Predictive Analytics Certification
SAP Predictive Analytics Certification exam validates that the applicant possesses demonstrated skills and advanced knowledge in implementing and managing projects using SAP Predictive Analytics. With this exam, the candidate's knowledge of SAP Predictive Analytics will be measured. This certification exam ensures the candidates’ proficiency in both Automated Analytics and Expert Analytics as well as knowledge about SAP HANA (APL, PAL, and R) and HCP Services.
The SAP certified Development Associate – SAP Predictive analytics online certification exam ensures that the participants possess the basic knowledge in the area prediction analysis and proven skills needed for the professional profile. The SAP Predictive analysis online training certification course with the help of expert trainers make sure that the participants can learn how to utilise the analytics process effectively within an organization.
P_PAII10_35: SAP Certified Application Professional - SAP Predictive Analytics
Pre-requisites
· There are no such pre-requisites to take up this certification
Exam Details
· Exam Duration - 180 Minutes
· Exam Questions - 80 Questions
· Passing Score - 68 %
· Exam Level - Professional
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The SAP Predictive analysis online certification course by Uplatz expert professionals provides wide range of job opportunities owing its efficiency in prediction and analytics in the business intelligence software. The leading companies hire SAP Prediction analytic consultants to manage business efficiently.
The following are the primary job avenues in this area:
· Predictive Analytics Consultant
· SAP Data Manager
· Predictive Analytics Expert
· Data Analyst
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With the advent of data and analytics and its huge potential for effective decision-making in organizations, SAP Predictive Analytics consultants are in great demand in the industry. An SAP Predictive Analytics Consultant draws an average salary of $146,658 dollar per year based on seniority level.
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Interview Questions
1. List out the responsibilities of a data analyst?
· Perform data auditing.
· Solve business-oriented issues on behalf of clients.
· Analyze data results and transform data using statistical techniques.
· Perform business related needs and is closely associated with management and information sector.
· Fetch data from primary source and deal database systems.
2. Mention the steps needed in an analytics project?
The steps for an analytic project are:
· Problem identification
· Data exploration
· Data preparation
· Data validation
· Modeling
3. Explain Data cleansing process?
Data cleansing also known as data cleaning, handles with identification, error removal and data inconsistencies.
4. How to improve the data quality?
To maintain the data quality it is important to have data consistency.
5. Explain Logical regression?
Logical regression is a statistical methodology for analysing a dataset which contains more than one variables that describes an outcome.
6. List out the tools which is used for data-analysis?
· Tableau
· MicroStrategy
· SAS Analytics
· TIBCO Spotfire
· Rapidminer
· OpenRefine
· KNIME
· Google Data Studio
· Solver
· NodeXL
7. Explain data profiling?
Data profiling is meant to target on the immediate analysis of individual attributes.
8. What information does data profiling have?
The data profiling offers information in various attributes such as value range, discrete value and their frequency, null occurrence values, data type, length etc.
9. What are the common challenges faced by data analyst?
· Common spelling
· Recurrent entries
· Missing values
· Analyse data overlap
10. Mention the data validation methods commonly used by data analyst?
· Data Screening
· Data Validation
11. How to handle multi-source problems?
To handle multi-source issues:
· Restructure schema to form schema integration
· Recognize similar records and combine them into single record which contains relevant attributes with no redundancy.
12. Explain Outlier?
The outlier is an inhouse term used by analysts which refers for value that seems far away and deviates from an entire pattern in a sample.
13. Mention the types of outliers?
The types of outliers are:
· Univariate
· Multivariate
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