In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote:
When I ask myself the question “What is data science?” I tend to think of the following five components. Data science is (1) the application of design thinking to data problems; (2) the creation and management of workflows for transforming and processing data; (3) the negotiation of human relationships to identify context, allocate resources, and characterize audiences for data analysis products; (4) the application of statistical methods to quantify evidence; and (5) the transformation of data analytic information into coherent narratives and stories.
File this under long-term followup, but just about four years ago I wrote about Palantir, the previously secretive but now soon to be public data science company, and how its valuation was a commentary on the value of data science more generally. Well, just recently Palantir filed to go public and therefore submitted a registration statement (S-1) describing its business. It’s a fascinating read, if you’re into that kind of stuff.
Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and quite a contrast to just 10 years ago when there were comparatively few choices.
The question of toolset though is not a question worth focusing on too much because it’s the wrong question to ask.
Like a lot of people, I’ve been glued to various media channels trying to learn about the latest with what is going on with COVID-19. I have also been frustrated - like a lot of people - with misinformation and the deluge of preprints and peer reviewed material. Some of this information is critically important and some is hard to trust.
As a biostatistician at a very visible school of public health I have also had a number of media outreaches, but I’ve been hesitant to do any interviews or talk about COVID-19.
NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. The full summary for the workshop on which this post is based can be obtained here.
Although R is great for quickly turning data into plots, it is not widely used for making publication ready figures. But, with enough tinkering you can make almost any plot in R. For examples check out the flowingdata blog or the Fundamentals of Data Visualization book.
Here I show five charts from the lay press that I use as examples in my data science courses. In the past I would show the originals, but I decided to replicate them in R to make it possible to generate class notes with just R code (there was a lot of googling involved).
Podcasting has gotten quite a bit easier over the past 10 years, due in part to improvements to hardware and software. I wrote about both how I edit and record both of my podcasts about 2 years ago and, while not much has changed since then, I thought it might be helpful if I organized the information in a better way for people just starting out with a new podcast.
Today a couple of different things reminded me about something that I suppose many people are talking about but has been on my mind as well.
The idea is that many of our societies social norms are based on the reasonable expectation of privacy. But the reasonable expectation of privacy is increasingly a thing of the past. Three types of data I’ve been thinking about are:
Obviously identifying data: Data like cellphone GPS traces and public social media posts are obviously information that is indentifiable and reduce privacy.
Introduction We have expanded the dslabs package, which we previously introduced as a package containing realistic, interesting and approachable datasets that can be used in introductory data science courses.
This release adds 7 new datasets on climate change, astronomy, life expectancy, and breast cancer diagnosis. They are used in improved problem sets and new projects within the HarvardX Data Science Professional Certificate Program, which teaches beginning R programming, data visualization, data wrangling, statistics, and machine learning for students with no prior coding background.
When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product. The common factor underlying each of these tasks is that you want to use the data to answer a question that you haven’t answered before. The most effective process we have come up for getting those answers is the scientific research process.
