Backstory: I wrote this blog post a couple of months ago, and I wasn’t at my best. My resumes and cover letters were getting no response, I was (and still am) suffering from a bad case of imposterism fueled by doing data analytics and data science without a degree, I was looking around only saw people 10 times smarter than I’d ever be.
Since that time, in a matter of a few months, I had a number of very good interviews with prospective employers, and I gave two well-received talks at a local SQL Saturday event.
Intro In this blog post, I’ll try to highlight some of Excel’s functionality which have been around for a while, but remains largely unknown to the broad public.
Now, I’ll be the first one to throw rocks at the Excel camp. I’ve got receipts:
Please don't... pic.twitter.com/r3j3KQtcCT
— Taras Kaduk (@taraskaduk) February 10, 2018 My analysis is in Excel. #Loseyourjobin5words
— Taras Kaduk (@taraskaduk) March 5, 2018 Reproducible analysis, case in point.
We happen to talk a lot about the impostor syndrome these days. No wonder — it seems to be an important subject. But what is it? That feeling of faking it while others clearly know what’s they’re doing.
Many attempts have been made to clarify the issue. Explaining that it is OK, that we all feel that way going through life. Et cetera et cetera. Some advice has gone as far as making the impostor syndrome a badge of honor.
Cross-posted: LinkedIn, Medium
If you happen to work in analytics, data science or business intelligence, you’ve probably seen one of the iterations of this Gartner’s graph on stages of data analysis in a company:
The figure above shows various stages of analytics maturity, from “descriptive” to “prescriptive”. I’ve seen it so many times, it became an eyesore to me.
There is nothing wrong with it. This look nicely breaks down the evolution of analytics into understandable parts and pairs each stage with a question to be answered: what happened, why did it happen, what will happen, how can we make it happen.
Cross-posted: Medium, LinkedIn
Hey, analyst, how is life? Talk to me. Do you love what you do for life, do you like all things data?
Yet, do you sometimes feel like you’re a Sisyphus rolling a giant rock of data up the hill every day, only to see it go down with a racket in the evening (and you know what you’re going to do tomorrow)? Or, do you imagine yourself being a plate spinner at a circus, only instead of plates and poles you’ve got five dozen reports to spin, and instead of an entertained crowd you’ve got your co-workers, managers and senior executives watching your “performance” and asking to add more plates, and God forbid any one plate falls?
About a month ago, I wrote a little article about the MPAA rating system. I set up to find out if their lettering system does any justice to the actual content seen on the screen. Briefly speaking, it does, but with caveats.
One of such caveats was the effect of profanity. What my quick and dirty data analysis showed was that profanity was the sure thing that could send a movie into an R category:
Today, I’m going to show you how to make pixel maps in R. Why pixel maps? Because they look awesome!
I was searching on the web for a while, but couldn’t find a good tutorial. Being stubborn as I am, I eventually figured out a way to get what I want. You know, if you torture your code enough, it might give you what you need.
Setup First, of course, loading required packages.
Intro Being a parent in modern days is lots of fun. Not only all of us are pretty much winging it, not having any idea what we’re doing (seriously, you need a license to do braids and nails, yet raising a human being a future member of society is a no-brainer, right?) — we are also constantly being watched and judged by other parents.
When it comes to watching movies with our six-year-old son, we don’t have a strict set of rules.