There’s an origin story for every superhero; even those without superpowers (like Batman – that’s right) got started somewhere. What we sometimes forget is that there is also an origin story for every regular person, every profession, every hobby.
Source: Wikimedia Commons
If you’re a radiologist looking to learn a few things in radiology data science, a simple web search will reveal a seemingly overwhelming amount of material you might have to know.
Fortunately, only a very small subset is necessary to start being productive. Here are a few resources I used to get started.
A Quora answer/article about data science.
Incidentally, the same 5 skills are also highly relevant to be a physician-informatician, particularly in radiology. Give it a read.
Source: William Chen’s answer to What are the top 5 skills needed to become a data scientist? – Quora
There is something strangely satisfying about being able to take things apart and putting it back together. Inspired by the popularity of Lego sets in our childhoods, Minecraft brought this sense of wonder to video games.
For those of us who are life-long tinkerers who happen to be radiologists, I published in Radiology Data Quest a DIY on how one take DICOM apart and manipulate it. All in Python, no less.
DICOM is a pain in the neck. It also happens to be very helpful. As clinical radiologists, we expect post-processing, even taking them for granted. However, the magic that occurs behind the scene…
Source: DICOM Processing and Segmentation in Python – Radiology Data Quest
The Radiology Society of North America (RSNA) Annual Meeting is a place to expand your knowledge base, both by taking a deeper dive into your core interest and by getting your feet wet a few new skills.
If informatics is something you’ve been interested in but need a good way to get started, then the RSNA offers some solid opportunities for beginners. Continue reading
After the recent presidential election, you are probably either particularly alarmed or especially excited about the outcome. Regardless of your particular political predilection, it is fair to say that this election puts data science on its head when so many got so wrong.
On an earlier issue of Harvard Business Review, the venerable magazine shared a piece of research from the University of Southern California on forecasting. When forecasting sales, the best estimators use a combination of intuition and logic – with both the logic-heavy and intuition-heavy forecasters performing less accurately.
In the age of artificial intelligence and big data, it can be sobering to realize that despite the staggering volume of data we are now collecting, ignoring your gut instincts can take a heavy toll on your decision-making abilities.
Source: “What type of forecaster are you?” Harvard Business Review (March): 26.
Big Data has become a radiology buzzword (the others: machine learning, AI, and disruptive innovation are also up there).
However, there is a real problem with using the term Big Data – it isn’t just one set of data problems. Big Data is a conglomerate of different data challenges: volume of data, heterogeneity of data, or the velocity of data are all important dimensions. Machine learning and internet of things are others layers superimposed on the big data problem.
Sometimes it is helpful to step back and approach data problems with a common framework, a way to think about how and which facets of data science fit in a real-life workflow in the face of an actual problem.
Below is a 6-element framework that helps me think about data-driven informatics problems. They are generally in chronological order, but they are not “steps” because you frequently will find yourself going back and redefining many things. However, the framework helps you maintain a big-picture outlook. The reason any sufficiently complex data problem requires a team approach. Continue reading
At the turn of the century, Joel Spolsky came up with the idea of a “Joel Test” – a highly irresponsible, sloppy test to rate the quality of a software team.
Then, this group thought to come up with their own criteria to rate the quality of a data science team. How do your analysts in the radiology department fare?
- Can new hires get set up in the environment to run analyses on their first day?
- Can data scientists utilize the latest tools/packages without help from IT?
- Can data scientists use on-demand and scalable compute resources without help from IT/dev ops?
- Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions?
- Does collaboration happen through a system other than email?
- Can predictive models be deployed to production without custom engineering or infrastructure work?
- Is there a single place to search for past research and reusable data sets, code, etc?
- Do your data scientists use the best tools money can buy?