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Business Analytics for Professionals de Alp Ustundag

Business Analytics for Professionals: Springer Series in Advanced Manufacturing

Editat de Alp Ustundag, Emre Cevikcan, Omer Faruk Beyca
en Limba Engleză Paperback – 11 mai 2023
This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python.
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Specificații

ISBN-13: 9783030938253
ISBN-10: 3030938255
Ilustrații: XIV, 481 p. 245 illus., 181 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.69 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Springer Series in Advanced Manufacturing

Locul publicării:Cham, Switzerland

Cuprins

PART I: TBA.- Chapter 1. Business Analytics for Managers.- Chapter 2. Big Data Management and Technologies.- Chapter 3. Descriptive Analytics: Feature Engineering & Data Visualization.- Chapter 4. Predictive Analytics with Machine Learning.- Chapter 5. Neural Networks and Deep Learning.- Chapter 6. Handling Unstructured Data: Text Analytics and Image Analysis.- Chapter 7. Prescriptive Analytics: Optimization and Modelling.- ​PART II: TBA.- Chapter 8. Supply Chain Analytics.- Chapter 9. CRM & Marketing Analytics.- Chapter 10. Financial Analytics.- Chapter 11. Human Resources Analytics.- Chapter 12. Manufacturing Analytics.

Notă biografică

​Prof. Alp Ustundag is a lecturer at Industrial Engineering Department in Istanbul Technical University (ITU). He is also the founder and coordinator of MSc in Big Data & Business Analytics program in ITU. He has conducted many research projects regarding data analytics in supply chain management and finance since 2006 and also he has published several research papers in distinguished journals. He teaches courses such as “Business Analytics for Managers” and “Industry 4.0 and Digital Transformation”. His research areas include intelligent automation, data science, supply chain management, financial analytics, digital transformation and innovation management.

Emre Cevikcan (PhD) is a Professor in Industrial Engineering from Istanbul Technical University. His research has so far focused on digital transformation of manufacturing and service systems, the design of production systems (assembly lines, production cells, etc.), lean production and scheduling. He hasseveral research papers in scientific journals such as International Journal of Production Research, Computers and Industrial Engineering, Expert Systems with Applications, Maritime Policy and Management, Assembly Automation, and International Journal of Information Technology & Decision Making. Cevikcan is currently reviewer in International Journal of Production Research, Assembly Automation, Applied Soft Computing.

Omer F. Beyca is currently an Assistant Professor with the Industrial Engineering Department at Istanbul Technical University, Istanbul, Turkey. His current research interests include data science, machine learning, deep learning and sensor-based modeling and he has several research papers in distinguished journals. He teaches courses such as “Machine Learning in Industrial Systems”, “Neural Network Models in Industrial Systems” and “Data Mining for Business”.

Textul de pe ultima copertă

This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python.

Caracteristici

Explain concepts and techniques for business analytics Demonstrates real applications in business and industry Supplies models and solutions in Python