End-to-End Data Science with SAS®: A Hands-On Programming Guide

Gearheart, James

ISBN 10: 1642958042 / ISBN 13: 9781642958041

Edité par SAS Institute, 2020

A propos de cet article

Some cover and edge wear, little highlighting/writing on text.

N° de réf. du vendeur 241226001

Détails bibliographiques

• Titre: End-to-End Data Science with SAS®: A Hands-On Programming Guide
• Éditeur: SAS Institute
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 1642958042
• ISBN à 13 chiffres: 9781642958041
• Reliure: paperback
• État de l'article: Good

A propos de ce titre

Synopsis

Learn data science concepts with real-world examples in SAS^®!

End-to-End Data Science with SAS^®: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process step-by-step.

Although a significant amount of background information and supporting mathematics are presented, the book is not structured as a textbook, but rather it is a user’s guide for the application of data science and machine learning in a business environment. Readers will learn how to think like a data scientist, wrangle messy data, choose a model, and evaluate the model’s effectiveness. New data scientists or professionals who want more experience with SAS will find this book to be an invaluable reference. Take your data science career to the next level by mastering SAS programming for machine learning models.

À propos de l?auteur

James Gearheart is an experienced Senior Data Scientist and Machine Learning Engineer who has developed transformative machine learning and artificial intelligence models. He has over 20 years of experience in developing and analyzing statistical models and machine learning products that optimize business performance across several industries including digital marketing, financial services, public health care, environmental services, social security and worker's compensation. James has been both a data science manager and an individual contributor on projects ranging from logistics, anti-money laundering, CRM, cash demand forecasting, customer segmentation, digital marketing, A/B testing, attribution and attrition. His research interests include applied data science, machine learning, and artificial intelligence.