Binding: Paperback
Description: Successfully navigating the data - driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data. Building on familiar content from applied econometrics and business analytics this book introduces the reader to the basic concepts of Big Data Analytics. The focus of the book is on how to productively apply econometric and machine learning techniques with large complex data sets as well as on all the steps involved before analysing the data (data storage data import data preparation). The book combines conceptual and theoretical material with the practical application of the concepts using R and SQL. The reader will thus acquire the skills to analyse large data sets both locally and in the cloud. Various code examples and tutorials focused on empirical economic and business research illustrate practical techniques to handle and analyse Big Data. Key Features: - Includes many code examples in R and SQL with R/ SQL scripts freely provided online. - Extensive use of real datasets from empirical economic research and business analytics with data files freely provided online. - Leads students and practitioners to think critically about where the bottlenecks are in practical data analysis tasks with large data sets and how to address them. The book is a valuable resource for data science practitioners graduate students and researchers who aim to gain insights from big data in the context of research questions in business economics and the social sciences.
Title: Big Data Analytics A Guide To Data Science Practitioners Making The Transition To Big Data
Author(s): Matter Ulrich (Assistant Professor Of Economics At Uni Of St. Gallens Switzerland.)
Publisher: Taylor & Francis Ltd
Barcode: 9781032458144
Pages: 310 Pages, 26 Line Drawings, Color; 22 Line Drawings, Black And White; 14 Halftones, Color; 40 Illus
Publication Date: 9/4/2023
Series: Chapman & Hall/CRC Data Science Series
Category: Data Capture & Analysis