Why a Business Analytics Problem Demands all of your Data Science Skills

In this article, we show, through a case study, how the modern practice of data-driven analytics, when applied to a real-life business problem, is always a complicated mixture of multiple techniques and frameworks— data wrangling, machine learning, business logic, and even ethical choices.

Disclaimer: This article grew out of a homework assignment in my class for the Georgia Tech’s Online Masters in Analytics (OMSA) program. I am proud to pursue this excellent Online MS program. You can also check the details here.

Introduction

Data-driven analytics (featuring massive datasets and fully-automated methodologies without much human intervention) is becoming one of the most important business processes for every organization around the world. The rise (or re-emergence) of powerful machine learning (ML) and artificial intelligence (AI) techniques is helping this transformation to a large extent.

However, setting aside all the hype around these amazing new developments, we should recognize the basic fact — a data-driven analytics process is much like a complex and intricate dance performance, where a high degree of harmony between the participants — data extraction, wrangling, statistical modeling, business logic, etc.—…