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The primary objective of this book is to help economists,statisticians, developers, engineers, and data analysts who are well versed in writing codes; have a basic understanding of data and statistics; and are planning to transition to a data scientist profile. The most challenging part is practical and hands-on knowledge of building predictive models and machine learning algorithms and deploying them in industries to address industrial business problems.This book will benefit the reader in solving the business problems in various industrial domains by sharpening their analytical skills in getting practical exposure to various predictive model and machine learning algorithmsThis book focuses on industrial business problems and practical analytical approaches to solve those problems by implementing predictive models and machine learning techniques using MATLAB, and R analytical languages.Data analytics has become part and parcel of any business in today’s world. In fact, it has evolved into an industry in itself. Vast numbers of software platforms are available for data extraction, scrubbing, analysis, and visualization. Some of these platforms are specialized for carrying out one of the above-listed aspects of data analytics, while others offer a generalist tool to carry out almost all tasks ranging from data scrubbing to visualization.Who may read yhis book:1. Economists, finance and valuation professionals, marketresearchers, public policy analysts, and other practitioners whose occupation demands the ability to model market behavior under a variety of real-world conditions2. Teachers or students in graduate-level classes in appliedeconomics, particularly those who cover topics such as fiscal and economic impact analysis, fuzzy logic applications, retail sales analysis, and the integration of geographic information systems, as this is one of the very few books to rigorously cover these subjects. |
