Hacker Bits, Issue 12

Introduction
Data science continues to generate excitement and yet real-world results can often disappoint business
stakeholders. How can we mitigate risk and ensure results match expectations? Working as a technical
data scientist at the interface between R&D and commercial operations has given me an insight into the
traps that lie in our path. I present a personal view on the most common failure modes of data science
projects.
The long version with slides and explanatory text below. Slides in one pdf here.
There is some discussion at Hacker News
First, about me:
This talk is based on conversations I’ve had with many senior data scientists over the last few years.
Many companies seems to go through a pattern of hiring a data science team only for the entire team
to quit or be fired around 12 months later. Why is the failure rate so high?
Let’s begin:
A very wise data science consultant told me he always asks if the data has been used before in a project.
If not, he adds 6-12 months onto the schedule for data cleansing.
Do a data audit before you begin. Check for missing data, or dirty data. For example, you might find
hacker bits
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