Insights

Here's to the hardworking ones

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.

Power Query: Excel's gateway to reproducible analysis

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.

Imposterism in Data Science: Addressing the credentials problem

Cross-posted: Medium 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.

The 4 Stages of Data Analytics Maturity: Challenging the Gartner’s Model

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.

On Importance of Minimalism in Retrospective Analytics

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?