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Course: Introduction to Predictive Analytics on SAP HANA

Introduction to Predictive Analytics on SAP HANA

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
About this Course

This Entry Level to Intermediate SAP HANA Predictive Analytics course will help you master many important techniques to start creating sophisticated, predictive analytics applications that utilize the power of SAP HANA and Business Intelligence.

The course is designed so that you can master all the techniques gradually, starting from basic and relatively simple techniques before moving on to the more demanding techniques that Business Intelligence Professionals use to create predictive analytics applications for their customers.

The course will take you step by step through the process of creating the required HANA objects, such as tables, views and predictive analytics SQL scripts.

What this course is not:

This course does not cover every single Predictive Analytics algorithm. It covers enough of the algorithms for you to get comfortable with using them and apply the techniques to any other functions. Covering all algorithms will result in a high level of repetition without any real value.

What sets this course apart from anything available on other platforms is the fact that it covers the integration and application of the Predictive Analytics Library with the various other SAP BW and visualization platforms.

This course will always expend so check back regularly for updates and more content, for example, integration of PAL into SAP BPC Embedded, more case studies for Regression Algorithms, Text Analytics and more!

Basic knowledge
  • This course assumes no knowledge of the HANA Predictive Analytics Library
  • BW and HANA experience would be helpful
What you will learn
  • Fundamentals of the Predictive Analytics Library
  • The structures involved, such as HANA Tables, Views, PAL SQL procedures and more
  • A comparison of the raw PAL SQL code with the HANA Analytical Processes available in SAP BW by creating the comparable HANA AP in BW
  • Integrating Predictive Analytics into SAP BW and SAP Lumira
Curriculum
Number of Lectures: 20
Total Duration: 01:15:54
Welcome to Predictive Analytics and Data Mining on SAP HANA
  • Welcome to Data Mining with SAP HANA  

    In this lecture we will look at all the content the course will cover.

  • Welcome to Data Mining with SAP HANA - Resource Files  
  • Introduction to HANA Predictive Analysis Library  

    In this lecture we will have a first look at the PAL library, We will cover the following topics:

    • What is the Predictive Analytics Library
    • An overview of the various PAL algorithms
    • Requirements for getting Started with PAL
    • Generation and calling of PAL functions
    • HANA Basics.
  • Introduction to HANA Predictive Analysis Library - Resource Files  
  • ABC Analysis  

    In this lecture we will look at ABC Analysis as simple implementation of a Grouping Algorithm. We will go through the creation of the ABC Analysis step by step utilizing the various tools available:

    • Create an ABC Analysis in Excel
    • Use what we learnt above and create the ABC in HANA PAL
    • Create the ABC using HANA Analysis Process


  • ABC Analysis - Resource Files  
  • Exponential Smoothing Part 1  

    In this lecture we will look at Single, Double and Triple Exponential Smoothing. We will use Smoothing to make predictions using real-world data on Sea Surface temperatures. This lecture will cover:

    • Loading of raw data into SAP HANA
    • Transforming the data into a usable format
    • Create the SQL for exponential smoothing procedure,
    • Run the Seasonality Test procedure to get two of the required parameters for triple Exponential Smoothing
    • View the results against actual observations for all three algorithms


  • Exponential Smoothing Part 1 - Resource Files  
  • Exponential Smoothing Part 2  

    In the second part of Exponential Smoothing we will look at the HANA Analysis Process (AP) in SAP BW. Not only are we going to create the HANA Analysis Process in BW, but we will create a data flow to use the table we created in HANA. In more detail, we will:

    • Set up HANA Smart Data Access
    • Create a view in the Schema of the BW system
    • Create an Open ODS view on top of the view
    • Create a Composite Provider to act as a infoProvider to the HANA AP
    • Create the HANA AP for Triple Exponential Smoothing and 
    • Display the results in an Analytical Index


  • Exponential Smoothing Part 2 - Resource File  
  • Scenario: HR Analytics  

    Why are our best and most experienced employees leaving prematurely? We will try to predict which valuable employees will leave next. 

    In this lecture we will do some basic data exploration to get a feel for the dataset and also visualize the data in Lumira.


  • Scenario: HR Analytics - Resource Files  
  • Data Preparation  

    In the second part of Exponential Smoothing we will look at the HANA Analysis Process (AP) in SAP BW. Not only are we going to create the HANA Analysis Process in BW, but we will create a data flow to use the table we created in HANA. In more detail, we will:

    • Set up HANA Smart Data Access
    • Create a view in the Schema of the BW system
    • Create an Open ODS view on top of the view
    • Create a Composite Provider to act as a infoProvider to the HANA AP
    • Create the HANA AP for Triple Exponential Smoothing and 
    • Display the results in an Analytical Index


  • Data Preparation - Resource File  
  • Decision Trees; A bit of theory and math  


    This lecture discusses how decision tress are constructed. This lecture is optional and can be skipped if you are familiar with the math of decision tree construction.

    Now that we have prepared our data, we will look at the various decision trees available to us. In particular we will look at:

    • CART decision trees and
    • C4.5 Decision Trees

    Once we have completed the theory, we will run a small sql file to verify the first level split of both types of decision trees.

    Note: Only the construction of the first level of the tree is covered in this lecture. The rest of the levels are in the answers are in the downloadable material for this lecture.


  • Decision Trees; A bit of theory and math - Resource File  
  • Decision Trees; Running Decision Trees in HANA  

    In the second part of Exponential Smoothing we will look at the HANA Analysis Process (AP) in SAP BW. Not only are we going to create the HANA Analysis Process in BW, but we will create a data flow to use the table we created in HANA. In more detail, we will:

    • Set up HANA Smart Data Access
    • Create a view in the Schema of the BW system
    • Create an Open ODS view on top of the view
    • Create a Composite Provider to act as a infoProvider to the HANA AP
    • Create the HANA AP for Triple Exponential Smoothing and 
    • Display the results in an Analytical Index


  • Decision Trees; Running Decision Trees in HANA - Resource Files  
  • Decision Trees (3) Effect of Categorical vs Continuous Data  

    This lecture will contrast the results of the decision tree when defining data as either continuous or discrete intervals. In the data preparation lecture we binned the last evaluation column into 5 discrete bins. In contrast, what would the effect be if we incorrectly classify Hours Worked as categorical?


  • Decision Trees (3) Effect of Categorical vs Continuous Data - Resource File  
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