If you are using data in any capacity I recommend listening to Data Framed. I always manage to learn a little something and also be sort of a data voyeur. The podcast reliably features working data scientists willing to share their process and data culture.
I think it's imperative that as many people know about how to handle their own personal data, as well as how to handle data within their own domain. I think every domain uses data in some aspect. Either you're collecting it, either you're manipulating it, or you're using it. It doesn't matter what discipline you're in. You need to know how to manage it.
I especially like the consideration of data science as five lanes. Data collection and cleaning is the first lane followed by storage and management, analysis, visualization, and storytelling. Brandeis reminds us that even if you manage the first 4 lanes--if the story doesn't make sense--it all falls flat.
Depending on your data origin story you may have different experiences along the way but I began as a medical writer. I think of medical writing and data integration as having an explanatory role. A client would have a complex clinical study or complicated research question requiring an explanation. Here is the data--please tell the "so what" story that will make an audience care.
Now as I lean closer to applied data science in my educational and professional experience, I am more often than not, assuming an exploratory role. Ground zero is now formulating a question and discovering if we have the data to answer.
Simple statistics can tell you the shape of your data, variance, correlations, clusters--and provide a visual of where to go next.
Inspiration is cheap, but rigor is expensive. And if you are not willing to pay up, don't expect that there is some magical formula that's going to give it to you.--Cassie Kozyrkov
Where do you get the skills? Workshops are expensive and time consuming. I was fortunate and able to step away to take refresher courses in data analytics but that isn't for everyone.
The conversation from Data Framed actually estimated 10s of thousands of dollars and two-week bootcamps running from 9 to 5 every day. Not exactly what I would consider true "access" for those of us wanting to skill up.
We are developing a series of data literacy workshops. Simply a few hours of instruction on a variety of topics--beginning with needs assessments. It may sound specific but what is a needs assessment if not a process for identifying a question.
How do you formulate questions, identify proper data sets, access, collect, manipulate, use and operationalize?
What are the outcomes you hope to measure? How should they be measured? Can they be measured accurately? What are the best types of survey questions? How do I analyze the data?
Register at the link Continuing Medical Education: change isn't necessary, survival is not mandatory.
Folks that have already registered have received instruction on downloading open source software, a free coupon to background resources and a few introductory sample sets to manipulate before the April 11th workshop date.
Stay tuned for archived workshops in data governance, Python for healthcare, R programming for journalists, and a wide variety of topics requested by you...