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Apply data science : introduction, applications and projects / Thomas Barton, Christian Müller, editors.

Contributor(s): Material type: TextTextLanguage: English Publication details: Springer Vieweg, Copyright© 2023.Description: xiii, 232 pages : illustrations (some color)ISBN:
  • 9783658387983
  • 365838798X
Subject(s): Additional physical formats: Print version:: Apply Data ScienceDDC classification:
  • 005.7 23/eng/20230119 BAR
Online resources:
Contents:
Cover Front Matter Part I. Introduction 1. Data Science: From Concept to Application Part II. Introduction to Data Science 2. Visualization and Deep Learning in Data Science 3. Digital Ethics in Data-Driven Organizations and AI Ethics as Application Example 4. Multiple Perspectives for the Implementation of Innovative Technological Solutions in the Context of Data-Driven Decision-Making 5. Don’t Be Afraid of Failure—Insights from a Survey on the Failure of Data Science Projects Part III. Systems, Tools and Methods 6. Recommendation Systems and the Use of Machine Learning Methods 7. Comparison of Machine Learning Functionalities of Business Intelligence and Analytics Tools 8. Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles Part IV. Applications 9. Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovoltaic Systems 10. Machine Learning for Energy Management Optimization 11. Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content 12. Identification of Relevant Relationships in Data Using Machine Learning 13. Framework for the Management and Analysis of Vehicle Data for Model-Based Driver Assistance System Development in Teaching and Research Correction to: Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles Back Matter
Summary: Apply Data Science Introduction, Applications and Projects This book offers an introduction to the topic of data science based on the visual processing of data. It deals with ethical considerations in the digital transformation and presents a process framework for the evaluation of technologies. It also explains special features and findings on the failure of data science projects and presents recommendation systems in consideration of current developments. Machine learning functionality in business analytics tools is compared and the use of a process model for data science is shown. The integration of renewable energies using the example of photovoltaic systems, more efficient use of thermal energy, scientific literature evaluation, customer satisfaction in the automotive industry and a framework for the analysis of vehicle data serve as application examples for the concrete use of data science. The book offers important information that is just as relevant for practitioners as for students and teachers.
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Holdings
Item type Current library Collection Call number Status Barcode
General Books General Books CUTN Central Library Generalia Non-fiction 005.7 BAR (Browse shelf(Opens below)) Available 54558

Includes bibliographical references and index.

Cover
Front Matter
Part I. Introduction
1. Data Science: From Concept to Application
Part II. Introduction to Data Science
2. Visualization and Deep Learning in Data Science
3. Digital Ethics in Data-Driven Organizations and AI Ethics as Application Example
4. Multiple Perspectives for the Implementation of Innovative Technological Solutions in the Context of Data-Driven Decision-Making
5. Don’t Be Afraid of Failure—Insights from a Survey on the Failure of Data Science Projects
Part III. Systems, Tools and Methods
6. Recommendation Systems and the Use of Machine Learning Methods
7. Comparison of Machine Learning Functionalities of Business Intelligence and Analytics Tools
8. Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles
Part IV. Applications
9. Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovoltaic Systems
10. Machine Learning for Energy Management Optimization
11. Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content
12. Identification of Relevant Relationships in Data Using Machine Learning
13. Framework for the Management and Analysis of Vehicle Data for Model-Based Driver Assistance System Development in Teaching and Research
Correction to: Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles
Back Matter

Apply Data Science
Introduction, Applications and Projects
This book offers an introduction to the topic of data science based on the visual processing of data. It deals with ethical considerations in the digital transformation and presents a process framework for the evaluation of technologies. It also explains special features and findings on the failure of data science projects and presents recommendation systems in consideration of current developments. Machine learning functionality in business analytics tools is compared and the use of a process model for data science is shown. The integration of renewable energies using the example of photovoltaic systems, more efficient use of thermal energy, scientific literature evaluation, customer satisfaction in the automotive industry and a framework for the analysis of vehicle data serve as application examples for the concrete use of data science. The book offers important information that is just as relevant for practitioners as for students and teachers.

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