TY  - GEN
AB  - This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github. This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
AU  - Paczkowski, Walter R.
CN  - HD30.23
CY  - Cham :
DA  - 2023.
DO  - 10.1007/978-3-031-31887-0
DO  - doi
ID  - 1471924
KW  - Decision making
KW  - Industrial management
KW  - Industrial management
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31887-0
N1  - 5 Information Extraction: Non-Time Series Methods
N2  - This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github. This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
PB  - Springer,
PP  - Cham :
PY  - 2023.
SN  - 9783031318870
SN  - 3031318870
T1  - Predictive and simulation analytics :deeper insights for better business decisions /
TI  - Predictive and simulation analytics :deeper insights for better business decisions /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-31887-0
ER  -