Introduction to Predictive Analytics on SAP HANA
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Introduction to Predictive Analytics on SAP HANA

Learn how to use HANA Predictive Analytics Library to create powerful applications
Best Seller
4.6 (7 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
128 students enrolled
Last updated 4/2017
Current price: $12 Original price: $50 Discount: 75% off
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  • 1.5 hours on-demand video
  • 35 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Identify the various modelling tools available in SAP Predictive Analytics and apply the best tool for the use case.
  • Use SAP HANA Predictive Analytics Library models.
  • Create SAP HANA Predictive Analytics Library models.
  • Create and use HANA AP in SAP BW
  • Use Lumira and to visualize HANA data.
View Curriculum
  • The following software is used in the course. It would be helpful to have access to these tools to complete the course: SAP HANA, SAP BW on HANA, Eclipse Neon, Lumira,
  • Basic knowledge of development in SAP HANA recommended but not required.
  • Basic knowledge of development in SAP BW recommended.

******For a limited period, use this promo to get access to the course for only $15: IPASH2017******

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. In particular, from this course 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


  • This course assumes no knowledge of the HANA Predictive Analytics Library.
  • BW and HANA experience would be helpful.

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!

Who is the target audience?
  • SAP professionals looking to expand their skills into SAP HANA Predictive Analytics.
  • Non-SAP data mining experts who would like an overview of SAP HANA and its data mining and predictive analytics tools.
  • Anyone interested in Data Mining and Predictive Analytics.
Compare to Other SAP HANA Courses
Curriculum For This Course
10 Lectures
3 Lectures 27:12

Welcome to Data Mining with SAP HANA. In this lecture we will look at all the content the course will cover.

Preview 02:25

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
Preview 13:35

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:

  1. Create an ABC Analysis in Excel
  2. Use what we learnt above and create the ABC in HANA PAL
  3. Create the ABC using HANA Analysis Process

All the HANA resources and data used in this lecture is available in the Downloadable Materials section of this lecture.

Preview 11:12

The first quiz in for the HANA PAL will test some of the basic concepts.

Predictive Analytics Quiz 1
3 questions
HANA Predictive Analysis Library basics
2 Lectures 20:54

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

All the SQL for the lecture is contained in the downloadable material below.

Exponential Smoothing Part 1

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

This quiz will test some of the concepts of the Exponential Smoothing lecture.

Predictive Analytics Quiz 2
1 question
Case study: HR Analytics and Decision trees
5 Lectures 27:42

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

Generally, before we start the analysis of data, we have to do some data preparation.In this lecture we are going to do binning of the last performance rating into 5 categories. This will greatly reduce the complexity of the decision trees we are going to build in the next lecture.

Data Preparation

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 left as an exercise and the answers are in the downloadable material for this lecture.

Decision Trees (1) A bit of theory and math

Now that we have prepared our data, we can use the full dataset to construct the decision trees. We will use the CART tree in this lecture to see which of our high performing employees are at risk of leaving. As usual the sql can be found in the downloadable material. Not only is the script for the CART tree attached, but the script for the C4.5 tree is also attached and you can run it to contrast the results of the two procedures.

Decision Trees (2) Running Decision Trees in HANA

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
About the Instructor
Lambertus Oosthuizen
4.2 Average rating
21 Reviews
182 Students
2 Courses
Technical Director

I am a SAP certified CO, BW and BPC consultant. I started implementing SAP FI/CO in version 3.1h and completed more than 20 projects over the last 18 years, usually as the technical or functional lead. 

Designing planning applications in SAP has been a specialization of mine ever since my first introduction to BW (SEM BPS, BPC classic and Embedded, BI-IP), especially transferring the complex and onerous planning functions from ERP into a more user friendly, but more sophisticated system. 

I hope to use this experience to enable you to design sophisticated, integrated planning applications for your business.

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