Buy Apply Data Science : Introduction, Applications and Projects | FOREIGN BOOKS , A Social Legal Perspective

ABOUT THE BOOK

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.

TABLE OF CONTENTS

1. Introduction

   1. Front Matter

      Pages 1-1

      
      

   2. Data Science: From Concept to Application

      • Thomas Barton, Christian Müller

      Pages 3-9

2. Introduction to Data Science

   1. Front Matter

      Pages 11-11

      
      

   2. Visualization and Deep Learning in Data Science

      • Jens Kaufmann, Daniel Retkowitz

      Pages 13-29

   3. Digital Ethics in Data-Driven Organizations and AI Ethics as Application Example

      • Claudia Lemke, Dagmar Monett, Manuel Mikoleit

      Pages 31-48

   4. Multiple Perspectives for the Implementation of Innovative Technological Solutions in the Context of Data-Driven Decision-Making

      • Anna-Maria Nitsche, Christian-Andreas Schumann, Christoph Laroque, Olga Matthias

      Pages 49-63

   5. Don’t Be Afraid of Failure—Insights from a Survey on the Failure of Data Science Projects

      • Jule Aßmann, Joachim Sauer, Michael Schulz

      Pages 65-76

3. Systems, Tools and Methods

   1. Front Matter

      Pages 77-77

      
      

   2. Recommendation Systems and the Use of Machine Learning Methods

      • Andreas Peuker, Thomas Barton

      Pages 79-93

   3. Comparison of Machine Learning Functionalities of Business Intelligence and Analytics Tools

      • Gabriele Roth-Dietrich, Michael Gröschel, Benedikt Reiner

      Pages 95-118

   4. Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles

      • Stephan Kuehnel, Uwe Neuhaus, Jens Kaufmann, Michael Schulz, Emal M. Alekozai

      Pages 119-134

4. Applications

   1. Front Matter

      Pages 135-135 

   2. Integration of Renewable Energies—AI-Based Prediction Methods for Electricity Generation from Photovoltaic Systems

      • Boris Brandherm, Matthieu Deru, Alassane Ndiaye, Gian-Luca Kiefer, Jörg Baus, Ralf Gampfer

      Pages 137-158

   3. Machine Learning for Energy Management Optimization

      • Gabriele Roth-Dietrich, Rainer Gerten

      Pages 159-179

   4. Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content

      • Thomas Barton, Arthur Kokoev

      Pages 181-187

   5. Identification of Relevant Relationships in Data Using Machine Learning

      • Joshua Hammesfahr, Martin Spott

      Pages 189-205

   6. Framework for the Management and Analysis of Vehicle Data for Model-Based Driver Assistance System Development in Teaching and Research

      • Tobias Peuschke-Bischof, Stefan Kubica

      Pages 207-229

5. Correction to: Using the Data Science Process Model Version 1.1 (DASC-PM v1.1) for Executing Data Science Projects: Procedures, Competencies, and Roles

   • Stephan Kuehnel, Uwe Neuhaus, Jens Kaufmann, Michael Schulz, Emal M. Alekozai

   Pages C1-C1