Data Science for Supply Chain Forecasting - Nicolas Vandeput

Descriere YEO:

Data Science for Supply Chain - Disponibil la libris.ro

Pe YEO găsești Data Science for Supply Chain de la Nicolas Vandeput, în categoria Business & Economics.

Indiferent de nevoile tale, Data Science for Supply Chain Forecasting - Nicolas Vandeput din categoria Business & Economics îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 272.9 Lei

Caracteristicile produsului Data Science for Supply Chain

• Brand: Nicolas Vandeput
• Categoria: Business & Economics
• Magazin: libris.ro
• Ultima actualizare: 15-12-2024 01:42:32

Comandă Data Science for Supply Chain Online, Simplu și Rapid

Prin intermediul platformei YEO, poți comanda Data Science for Supply Chain de la libris.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.

Descriere magazin:
Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical traditional models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting--from the basics all the way to leading-edge models--will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical traditional models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting--from the basics all the way to leading-edge models--will benefit supply chain practitioners, forecasters, and analysts looking