Business Analytics Business Analytics

Business Analytics

A Data-Driven Decision Making Approach for Business

    • $28.99
    • $28.99

Publisher Description

This second volume of Business Analytics discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics.

Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, importance of analytics, types of BA—descriptive, predictive, and prescriptive—along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed.

Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics—machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.

Ancillary data sets and more available for downloading on the publisher's website.

GENRE
Computing & Internet
RELEASED
2019
19 November
LANGUAGE
EN
English
LENGTH
404
Pages
PUBLISHER
Business Expert Press
SELLER
Ingram DV LLC
SIZE
16.7
MB
Predictive Data Mining Models Predictive Data Mining Models
2016
Essentials of Business Analytics Essentials of Business Analytics
2019
Statistics for Marketing and Consumer Research Statistics for Marketing and Consumer Research
2008
Introduction to Small Area Estimation Techniques Introduction to Small Area Estimation Techniques
2020
Improving Forecasts with Integrated Business Planning Improving Forecasts with Integrated Business Planning
2019
The SAGE Dictionary of Quantitative Management Research The SAGE Dictionary of Quantitative Management Research
2011
Data Visualization Data Visualization
2017
Data Visualization Data Visualization
2017