Before I was a data scientist, I was a statistician.

For a long time, I thought of that as a liability.

Machine learning felt like the way of the future and my statistical training felt like it belonged in the past.

But over time, I noticed something. While most data scientists thought in terms of point estimates, I instinctively thought in terms of uncertainty ranges.

It turned out that instinct was more valuable than I’d given it credit for.

According to Adam DeJans Jr, co-author of The Decision Factory, probability is one of the most overlooked layers in most decision systems and its absence leaves organisations making decisions as if all future uncertainty could be collapsed down to a single number, when that couldn’t be further from the truth.

Probability is just one of eight disciplines Adam believes are necessary to build decision systems that actually work. And most data scientists are only fluent in one or two of them.

In the latest Value Boost episode of Value Driven Data Science, Adam joins me to walk through the eight disciplines that make up the modern decision stack and where data scientists should focus to close the gaps in their own skill set.

In just 12 minutes, you'll discover:

1. The eight disciplines that make up the modern decision stack [01:24]
2. The most overlooked layer in most decision systems [04:47]
3. Why optimisation textbooks are teaching data scientists the wrong things [06:17]
4. What to learn next if you want to build better decision systems [09:45]

Machine learning is what most data scientists know best. The other seven disciplines are where the opportunities lie.

Listen now on Apple Podcasts or Spotify, or click the link below:

​Episode 124: 8 Disciplines Every Decision System Needs​

Talk again soon,

Dr Genevieve Hayes