Business Data Science: Combining Machine Learning and Economics to Optimize,Automate,and Accelerate Business Decisions
Authors: Matt Taddy
ISBN-10: 1260452778
ISBN-13: 9781260452778
Edition 版次: 1
Publication Date 出版日期: 2019-08-21
Print Length 页数: 352 pages
Book Description
By finelybook
Use machine learning to understand your customers,frame decisions,and drive value
Spreadsheet models and pivot tables were once the cutting-edge tools of business analysis. But the Big Data revolution has changed everything. Tasks that once required armies of business analysts are being automated and scaled with software,allowing decision makers to go deep into the data to understand how their business is running and what their customers want. This has led to a new superstar job class: the Business Data Scientist who is able to combine science and engineering tools with business and economic context to build data analyses that drive better decisions.
Matt Taddy,developer of the Big Data curriculum at the University of Chicago Booth School of Business,has made a career of training students to use economic principles to connect business decisions to massive data. Business Data Science is an essential primer for those who want to use cutting-edge machine learning to have a real impact on the direction of their business.
With Business Data Science,readers will learn:
The key ingredients that make ML work,without getting lost in the hype,and a playbook for how ML and AI can be used to solve business problems
A wealth of real-world examples,including applications of text analysis,pricing and demand estimation,A/B experiments,and customer behavior analysis
How to move from correlation to causation and to use ML tools to make business decisions
An example-driven education in scripting in R,including a wealth of R-code examples,giving you a launch pad for your own work
Scalable frameworks ideal for modern cloud computing environments
With Business Data Science you have everything you need to connect business problems to data and drive decisions with data analysis. You’ll understand your customers better,make more informed business decisions,achieve maximum value–and thrive in today’s data-driven economy.
Contents
Preface
Introduction
1Uncertainty
2 Regression
3Regularization
4 Classification
5 Experiments
6 Controls
7 Factorization
8 Text as Data
9 Nonparametrics
10 Artificial Intelligence
Bibliography
Index
