DSC200. Delivering an SAP Data Science Project COURSE OUTLINE. Course Version: 2 Course Duration: 2 Day(s)
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1 DSC200 Delivering an SAP Data Science Project. COURSE OUTLINE Course Version: 2 Course Duration: 2 Day(s)
2
3 SAP Copyrights and Trademarks 2018 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE s or its affiliated companies strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. Copyright. All rights reserved. iii
4 Typographic Conventions American English is the standard used in this handbook. The following typographic conventions are also used. This information is displayed in the instructor s presentation Demonstration Procedure Warning or Caution Hint Related or Additional Information Facilitated Discussion User interface control Example text Window title Example text iv Copyright. All rights reserved.
5 Contents vii Course Overview 1 Unit 1: Introduction 1 Lesson: Introduction 3 Unit 2: CRISP-DM Introduction 3 Lesson: CRISP-DM Introduction 5 Unit 3: Business Understanding 5 Lesson: Business Understanding Phase 5 Lesson: Design Thinking 7 Unit 4: Data Understanding 7 Lesson: Data Understanding Phase 7 Lesson: Initial Data Analysis and Exploratory Data Analysis 7 Lesson: SAP Lumira 9 Unit 5: Data Preparation 9 Lesson: Data Preparation Phase 9 Lesson: Data Preparation Expert Analytics 9 Lesson: Automating Data Preparation 9 Lesson: Creating Dynamic Data Sets 11 Unit 6: Modeling 11 Lesson: Modeling Phase 11 Lesson: SAP Predictive Analytics Autmoated 11 Lesson: Automated Data Encoding 11 Lesson: Building Models Automatically 11 Lesson: Social and Recommendation 11 Lesson: SAP Predictive Analytics Expert 12 Lesson: PAL/APL 12 Lesson: Model Quality 13 Unit 7: Evaluation 13 Lesson: Introduction to Evaluation 15 Unit 8: Deployment 15 Lesson: Deployment Phase 15 Lesson: SAP Predictive Factory Copyright. All rights reserved. v
6 17 Unit 9: Bringing it All Together 17 Lesson: Complete Process and Challenges vi Copyright. All rights reserved.
7 Course Overview TARGET AUDIENCE This course is intended for the following audiences: Application Consultant Business Process Owner/Team Lead/Power User Program/Project Manager Copyright. All rights reserved. vii
8 viii Copyright. All rights reserved.
9 UNIT 1 Introduction Lesson 1: Introduction Explain the course in details Copyright. All rights reserved. 1
10 Unit 1: Introduction 2 Copyright. All rights reserved.
11 UNIT 2 CRISP-DM Introduction Lesson 1: CRISP-DM Introduction Provide an overview of CRISP and each of the phases Copyright. All rights reserved. 3
12 Unit 2: CRISP-DM Introduction 4 Copyright. All rights reserved.
13 UNIT 3 Business Understanding Lesson 1: Business Understanding Phase Explain the business understadning phase Lesson 2: Design Thinking Explain how design thinking can support business understanding phase Copyright. All rights reserved. 5
14 Unit 3: Business Understanding 6 Copyright. All rights reserved.
15 UNIT 4 Data Understanding Lesson 1: Data Understanding Phase Explain data understanding phase Lesson 2: Initial Data Analysis and Exploratory Data Analysis Explain Initial Data Analysis and Exploratory Data Analysis Lesson 3: SAP Lumira Describe how to use SAP Predictive Analytics automated functionality and SAP Lumira Discovery to visualize the data Copyright. All rights reserved. 7
16 Unit 4: Data Understanding 8 Copyright. All rights reserved.
17 UNIT 5 Data Preparation Lesson 1: Data Preparation Phase Explain the data preparation phase Lesson 2: Data Preparation Expert Analytics Prepare data in SAP Predictive Analytics expert mode Lesson 3: Automating Data Preparation Explain SAP Data Manager and data manipulation Lesson 4: Creating Dynamic Data Sets Create dynamic data sets in SAP Data Manager for predictive modeling Copyright. All rights reserved. 9
18 Unit 5: Data Preparation 10 Copyright. All rights reserved.
19 UNIT 6 Modeling Lesson 1: Modeling Phase Describe the modeling phase, algorithm selection and SAP data science applications Lesson 2: SAP Predictive Analytics Autmoated Describe SAP Predictive Analytics automated system Lesson 3: Automated Data Encoding Encode data in SAP Predictive Analytics automated system Lesson 4: Building Models Automatically Build models in SAP Predictive Analytics automated system Lesson 5: Social and Recommendation Explain social and recommendation Lesson 6: SAP Predictive Analytics Expert Copyright. All rights reserved. 11
20 Unit 6: Modeling Explain modeling in SAP Predictive Analytics expert system Lesson 7: PAL/APL Explain modeling in SAP HANA using PAL and APL Lesson 8: Model Quality Assess model quality 12 Copyright. All rights reserved.
21 UNIT 7 Evaluation Lesson 1: Introduction to Evaluation Explain each evaluation task Copyright. All rights reserved. 13
22 Unit 7: Evaluation 14 Copyright. All rights reserved.
23 UNIT 8 Deployment Lesson 1: Deployment Phase Explain deployment in details Lesson 2: SAP Predictive Factory Explain SAP Predictive Factory Copyright. All rights reserved. 15
24 Unit 8: Deployment 16 Copyright. All rights reserved.
25 UNIT 9 Bringing it All Together Lesson 1: Complete Process and Challenges Explain the complete process Copyright. All rights reserved. 17
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