Blog article: Ask a Data Scientist: Ryan Garnett

Ask a Data Scientist: Ryan Garnett

Article text

We sat down with open data team manager Ryan Garnett to ask him a few questions about what traits make a good data scientist, what his favourite resources are, and words of wisdom for newbies.

Q1. What do you think makes a good data scientist?

Passion. Data science is becoming one of those terms that means something to everyone, like love. Because of that I thing you really need to love solving problems, looking at things differently, bring a diverse perspective, and most of all, be weird and own it.

Q2. What will you say the “best practices” in data science right now?

Delivery and communicating value. You can build a wicked machine learning algorithms, or have an AI predict with >95% confidence, but if you can communicate why it is valuable in a tangible outcome deliverable, then that work has minimal benefit.

Q3. What publications, websites, blogs, conferences and/or books do you read/attend that are helpful to your work?

This depends on which part of my data science journey. As a leader of a team my role is much less technical, however I do drink the R koolaid. I read a fair amount from R-Bloggers as it is a good mix of technical and project. Conferences aren’t really my thing, as I personally do not get a lot out of the experience. However I do watch a lot of YouTube, but not what you expect. I tend to watch thought provoking topics, such as the future of jobs and climate change. I find by watching these videos I am able to be creative and link how data and analysis can help to benefit society. As for books, story telling with data by Cole Nussbaumer Knaflic is a must read for everyone.
“I find by watching these videos I am able to be creative and link how data and analysis can help to benefit society.”

Q4. What are the biggest areas of opportunity / questions you would like to tackle?

Data literacy. Governments and organizations are focusing on teaching people to code. While good I feel educating society on data, what it is, why it’s important, what it can do, and how to work with it is fundamentally important. Improving data literacy will raise the global profile of data science, as well as make the daily activities for data scientist easier as both senior executives and the team at whole will understand and respect the power of data.

Q5. Any words of wisdom for Data Science students or practitioners starting out?

Humility will take you far in your career. In many organizations you will work with people who have a limited data and analytics background, don’t overlook the experience and knowledge they have achieved and how it can benefit your work. Your career will foster and explode if you position yourself as the bridge between the technical data science team and the business, communicating what’s possible, the value, while keeping everyone honest about the technology.