
You might think journalism and data science don’t really go together, but on that, I differ. Below are some thoughts on the topic and lessons we can draw from data science on how to make journalism better and more effective in these times.
You might think journalism and data science don’t really go together, but on that, I differ. Below are some thoughts on the topic and lessons we can draw from data science on how to make journalism better and more effective in these times.
With the increasing speed of information coming at us, how do we know what’s true and what’s not, or even worse – what’s fake?
Figuring out what’s true and false is tough, and then understanding what to do about it can be even tougher. But we should recognize one aspect between lies and the truth.
Lies spread faster. Here’s why.
FAKE NEWS!
It’s amazing that we’ve now had our collective awareness heightened to the problem of fake news. I get frustrated at times with the sheer nonsense that seems to swim in the public consciousness, but in search of what I can do about it, I figured I’d share something that happened to me recently.
Data Science has become an exploding field in recent years, and depending on whether you are focusing on machine learning, artificial intelligence, or citizen data science, the discipline of data science is creating very high expectations.
There is indeed much promise for data science, where predictive models and decision engines can target skin cancer in patient imagery, presciently recommend a new product that piques your interest, or power your self-driving car to evade a potential accident.
However, promise requires much effort for it to be realized. It takes a lot of work and brand new engineering disciplines that are not yet mature or even employed on a wide scale. As there is greater recognition of the value of data science, and the generation of data is increasing at exponential rates, this engineering effort is starting and will grow beyond its adolescence soon.
This is why we are at the advent of a new engineering discipline that can truly realize the promise of data science – a discipline that I call “analytics engineering”.
Two of the biggest buzzwords in our industry are “big data” and “data science”. Big Data seems to have a lot of interest right now, but Data Science is fast becoming a very hot topic.
I think there’s room to really define the science of data science – what are those fundamentals that are needed to make data science truly a science we can build upon?
Below are my thoughts for an outline for such a set of fundamentals:
Richard Feynman is one of the greatest scientific minds, and what I love about him, aside from his brilliance, is his perspective on why we perform science. I’ve been reading the compilation of short works of Feynman titled The Pleasure of Finding Things Out, and I recently came across a section that really hit home with me.
I read a couple of items in this month’s Fortune magazine that I thought it was worth passing along.
The first was a small article by Brian Dumaine about the work being done at Applied Proteomics to identify cancer before it develops. At Applied Proteomics, they use mass spectroscopy to capture and catalog 360,000 different pieces of protein found in blood plasma, and then let supercomputers crunch on the data to identify anomalies associated with cancer. The company has raised $57 million in venture capital and is backed by Microsoft co-founder Paul Allen. You can read the first bit of the article here.
The second is from the Word Check callout, showing how access to information is making the word a better place:
wasa: Pronounced [wah-SUH]
(noun) Arabic slang: A display of partiality toward a favored person or group without regard for their qualifications. A system that drives much of life in the Middle East — from getting into a good school to landing a good job.
But on the Internet, there is no wasa.
– Adapted from Startup Rising: The Entrepreneurial Revolution Remaking the Middle East by Christopher M. Schroeder
I found this set of business wisdoms in the August 2013 issue of Entrepreneur magazine. While not perfect mantras by which to guide a business, I thought there were pretty fun.
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Chris Hardwick didn’t rely on just his nerdy instincts in founding his company; he also took inspiration from his heroes. Super-power your business with these lessons from some epic nerd properties.