Apply Data Science: Introduction, Applications and Projects

Contents
Editors and Contributors
Part I Introduction
1 Data Science: From Concept to Application
	Abstract
	1.1	What is Data Science?
	1.2	What is and What Does a Data Scientist?
	1.3	Introduction to Data Science
	1.4	Systems, Tools and Methods
	1.5	Applications
	References
Part II Introduction to Data Science
2 Visualization and Deep Learning in Data Science
	Abstract
	2.1	Introduction
	2.2	Methods for the Visual Preparation of Data
		2.2.1	Representing Simple Data and Text
		2.2.2	Simplifying and Representing Complex Data
			2.2.2.1 Matrixplot
			2.2.2.2 Principal Component Analysis and Multidimensional Scaling
			2.2.2.3 t-SNE
	2.3	Extract Image Information
		2.3.1	Recognizing Visual Structures with Deep Learning
		2.3.2	Architectures for Practice
	2.4	Bringing Together Image and Data
		2.4.1	Generation of Enriching Detail Information
		2.4.2	Transformation of Visual Representations
		2.4.3	Applications
	2.5	Summary
	References
3 Digital Ethics in Data-Driven Organizations and AI Ethics as Application Example
	Abstract
	3.1	Introduction
	3.2	Data-driven Organizations
		3.2.1	The concept of Data-driven Organization
		3.2.2	Technology Use of Data-driven Organizations
		3.2.3	Data-driven Corporate Culture
	3.3	Digital Ethics
		3.3.1	Terminology and Moral Theories
		3.3.2	Overview of Digital Ethical Principles
	3.4	Digital Ethics and Data-driven Organizations
		3.4.1	Digital Ethical Principles and Data Value Creation
		3.4.2	Consequences for the Design of Data-driven Organizations
	3.5	Case Study Deutsche Telekom AG: Operationalization of AI Ethics
		3.5.1	The Company’s Motivation for Developing Digital Ethics
		3.5.2	AI Ethics at the DTAG
	3.6	Summary and Outlook
	References
4 Multiple Perspectives for the Implementation of Innovative Technological Solutions in the Context of Data-Driven Decision-Making
	Abstract
	4.1	Why the Implementation of Innovative Technologies Requires a Comprehensive Approach
	4.2	Models from the Literature and their Weaknesses
	4.3	The Technological and Organizational Coherence Implementation Model (TOCI Model)
	4.4	Benefits and Features of the TOCI Model
	4.5	Possible Useful Extensions to the TOCI Model
	4.6	Outlook
	References
5 Don’t Be Afraid of Failure—Insights from a Survey on the Failure of Data Science Projects
	Abstract
	5.1	Introduction
	5.2	Characteristics of and Hypotheses about Data Science Projects
	5.3	Design and Conduct of the Survey
	5.4	Evaluation of the Survey
	5.5	Conclusion and Outlook
	References
Part III Systems, Tools and Methods
6 Recommendation Systems and the Use of Machine Learning Methods
	Abstract
	6.1	Introduction
	6.2	Collaborative Recommendation Systems
		6.2.1	Approaches
		6.2.2	Methods
	6.3	Content-based Recommendation Systems
		6.3.1	Approach
		6.3.2	Methods
	6.4	Other Concepts
		6.4.1	Demographic Recommendation Systems
		6.4.2	Knowledge-based Recommendation Systems
		6.4.3	Hybrid Recommendation Systems
	6.5	Current Developments
	6.6	Summary
	References
7 Comparison of Machine Learning Functionalities of Business Intelligence and Analytics Tools
	Abstract
	7.1	Introduction
	7.2	Evaluation Framework for Business Intelligence Tools
		7.2.1	Selection of BI Tools
		7.2.2	Personas
			7.2.2.1 Persona 1: Expert
			7.2.2.2 Persona 2: Layperson
		7.2.3	Comparison Criteria
		7.2.4	Test Datasets
	7.3	Comparison of ML Methods
		7.3.1	SAP Analytics Cloud
		7.3.2	Tableau Online/Tableau Desktop
		7.3.3	Qlik Sense Business/Qlik Sense Desktop
		7.3.4	TIBCO Cloud Spotfire
		7.3.5	RapidMiner
	7.4	Recommendations
	References
8 Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles
	Abstract
	8.1	Introduction
	8.2	The Project Flow when Using DASC-PM v1.1
		8.2.1	DASC-PM v1.1 at a Glance
		8.2.2	Project Order
		8.2.3	Data Provision
		8.2.4	Analysis
		8.2.5	Deployment
		8.2.6	Application
	8.3	Overarching Key Areas
	8.4	Competence-driven Team Management Using Roles
	8.5	Concluding Remarks
	References
Part IV Applications
9 Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovoltaic Systems
	Abstract
	9.1	Introduction and Motivation: Integration of Renewable Energies
	9.2	Data Preparation
		9.2.1	Data Collection
		9.2.2	Data Exploration
		9.2.3	Data Cleansing
		9.2.4	Data Transformation
	9.3	AI-based Prediction Methods
		9.3.1	Approaches Based on Artificial Neural Networks
		9.3.2	Approaches Based on Ensemble Machine Learning
	9.4	Fusion of Results
	9.5	Application Examples and Outlook
	References
10 Machine Learning for Energy Management Optimization
	Abstract
	10.1	Digital Twin for an Air Conditioning System with Passive and Active Heat Recovery
	10.2	Conception and Architecture
	10.3	Analysis and Evaluation of the Data Processing Steps
		10.3.1	Step 1: Data Collection
		10.3.2	Step 2: Data Cleansing
		10.3.3	Step 3: Classify Data
		10.3.4	Step 4: Filter Data
		10.3.5	Step 5: Calculate Prediction
	10.4	Proof-of-Concept
		10.4.1	Methods and Technologies Stack
		10.4.2	Visualization of results
	10.5	Conclusion
	10.6	Outlook
		10.6.1	Further Analysis Approaches
		10.6.2	Applications
	References
11 Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content
	Abstract
	11.1	Introduction
	11.2	Explainable Artificial Intelligence
	11.3	Extraction of Keywords
	11.4	Extraction of keywords for a literature review on “Explainable AI”
	11.5	Conclusion
	References
12 Identification of Relevant Relationships in Data Using Machine Learning
	Abstract
	12.1	Introduction
	12.2	Expertise Problem
	12.3	Approaches to Reducing the Number of Rules
		12.3.1	Association Rule Discovery
		12.3.2	Subgroup Discovery
	12.4	Determining the Quality of Reduced Rule Sets
	12.5	Combination Schema
	12.6	Results
	12.7	Summary
	References
13 Framework for the Management and Analysis of Vehicle Data for Model-Based Driver Assistance System Development in Teaching and Research
	Abstract
	13.1	Motivation
	13.2	Wildauer Maschinen Werke at TH Wildau
	13.3	Presentation of the Vehicle Fleet
		13.3.1	Trikes
		13.3.2	Trucks
	13.4	Introduction to Infrastructure
		13.4.1	ROS
		13.4.2	Node-RED
		13.4.3	MQTT-Bridge
		13.4.4	ROS Car2X
		13.4.5	Traffic Light Systems
		13.4.6	VDI
	13.5	Development Framework
		13.5.1	Implementation of Vehicle Communication
		13.5.2	Model-based Development and Code Generation for Vehicles
		13.5.3	Agile Project Management, Knowledge Management and Source Code Management
	13.6	Scenario-based Teaching and Research
		13.6.1	ROS Car2X as Data Aggregation and Function Behavior Across Vehicles
		13.6.2	NodeRED for Data Analysis
		13.6.3	Interdisciplinary Scenario using the Example of Material Management
	13.7	Summary and Outlook
	References
Index