Maths in the real world...
Seth Godin doesn't care about SEO savvy headlines and you shouldn't either. Many of my journalist colleagues disagree and I get it. We are only as clickable as our cleverest headline written to bring more and more eyeballs to the table. I am no Seth Godin but I also prefer headlines that mean something to me not the Google algorithms that bring short term interest. A stray piece of conversation or a snippet from a podcast that catches my attention will also do just nicely.
Look down the list at the last few posts--Go Fund Yourself, A Tabula Rasa in Data Literacy, and Generic but Ambitious--what the hell am I talking about? I don't know. Maybe grab a coffee or tea and take a look. Seth teaches us not to be the thing for everyone--just some people.
If you prefer provocative headlines, I didn't make this for you.
Even if you don't think of yourself as a marketer--you are a marketer. Maybe you are the product or maybe your currency is knowledge or expertise but make no mistake we are all selling something.
Seth teaches us how to be effective. If you are indeed bringing value to the table you need to find your tribe, stand in front and say--here. I made this for you. This particular blog captured my attention. Not because I need something shiny to look at like a buzz worthy headline but because I trust I will learn something.
And when does it get boring? is well worth the quick read. I think Seth and I share short attention spans. In fact, I agree with his method--read along until you get the joke. So why not keep it succinct and to the point?
Almost no one who takes an intro to economics course becomes an economist. One reason might be that within a few days of starting the class, it becomes abstract, formula-based and dull.--Seth Godin
I think the problem might be that we no longer remember how to be curious. If you are a writer or do anything where your stories matter, this is a muscle you need to keep flexing.
I read, listen to podcasts, travel, and follow the threads on anything that interests me.
During a hike up in the mountains I saw the most amazing tree.
I immediately texted my childhood best friend to ask her if she too notices trees that would have been perfect hosts for our childhood selves to explore. I was happy to receive an enthusiastic yes within minutes if not seconds.
I think I surprise many when they find out I am a data analyst. I have always had a wide scope of general interests and this serves me well. Believe it or not, there aren't that many of us that can work the entire end to end strategic data process. We need to know the language, the challenges, and the opportunities. Not everyone needs to be niche driven or highly specific. If you are reading this as one of my data friends or colleagues, here is the perfect podcast for you, Should I Become More Technical or Business Focused in Data Science Career.
I began this post in response to a question I field pretty regularly. How much statistics do I need to become a data scientist/analyst/professional? I can't answer that question. Textbooks cram information into your head for an exam. She/he/they who passes the most exams wins and can be perhaps a statistician, mathematician, computer scientist. You need to ask yourself what the long road looks like for you. Where do you want to work? What do you want to be doing?
I have completed numerous classes in statistics but I am not a statistician. Weirdly I am also a member of the American Statistical Association. I need to be. I need to keep learning and applying rigor and comprehension to technical topics.
Once you get out in the real world and discover your unique flair and contribution, the trick is to remain curious. To use the tools that give "us a chance to understand and to figure things out."
Because a testing regime is in place, particularly now when so many other tropes in the education-industrial complex are disrupted, the textbook authors and administrators work together to skip the ‘fluff’ and go straight to the stuff that’s easy to test.--Seth Godin
For example, when I think about data modeling and trying figure out the shape of my data, I think about linear, sinusoidal, or quadratic equations. These questions jump from the pages of math books into practical applications when you have variables to consider and relationships to determine.
Am I the only one considering positive first derivatives when looking at the COVID-19 curves? All the curves were getting bigger but we needed to consider the rates. They can stay the same, increase slowly, or represent what actually was occurring--the rate was increasing quite rapidly. All applications of the math concepts we were forced to memorize to a test in the absence of how they are applied in the real world.
When my boys were small occasionally one or both of them would complain about being bored. My response rarely wavered. I would tell them, "That doesn't sound like being bored, that sounds like a lack of imagination."
And off we would go to have an adventure.
Speaking of adventures...
May 15th we are having a free lunch and learn about demographic data. Register at link Getting comfortable with demographic data.
