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I’m happy to check back in for a third year running and give some quick reviews of the professional development books I read over the past year.
I’m a huge fan of year-end lists. I keep my eyes peeled the entire month of December for my favorite writers and publications to publish annual lists of their favorite media of the year (with an extra-special shout to Stereogum’s genre-specific year-end coverage and NPR’s Book Concierge). Last year I decided to get back into the action with a rundown of the professional development books I read during the year, and I’m happy to be back with a fresh crop of books in 2020.
Every list so I’ve read so far has included an editorial lede that has made me whip out the COVID bingo card - “these trying times,” “finding escape,” “radically reshaped world,” etc. I’ll forego my own grand statement and just say that I’m thankful I was able to use reading throughout the year to establish a sense of normalcy.
In 2018, after having read Jake Knapp’s Sprint toward the end of the year, I decided I wanted to read at least one book every quarter that would contribute to my professional development or impact my career. I think this first year has been a really valuable exercise and I wanted to write up which books I chose, how I chose them, and what stuck with me about each of them.
The idea for this blog post has been germinating since I finished my previous post. I knew I wanted to compare and contrast my experiences with various plotting methodologies in R and Python, but I felt there just wasn’t enough meat on the bone. It all comes down to a single takeaway: It’s much easier to create presentable plots using R and ggplot2 than any method I’ve attempted in any other language or library.
As outlined in my previous posts, I went through the trouble of scraping over 15 years of one of the leading independent music websites. Pitchfork shaped my record collection over the past 10 years I have been reading the publication, but nowadays it feels like a totally different website. I wanted to take a look and see if there was any evidence that supported my intuition.
Two years ago I took a first stab at scraping Pitchfork reviews. Shortly afterward, I flung myself into a new position as pitchfork kept posting new reviews. I knew that at some point I wanted to revisit the data to attempt to try out some new ideas, but I also never got over the fragility of my initial implementaion.
I’ve finally taken some ground on my first serious personal project: a deep dive into Pitchfork album reviews. I’ve followed the site for nearly a decade now and thought it would be interesting to analyze and visualize their 15 years of reviews. Before I could do anything fun with the reviews, I had to scrape them.
Although I started out my career in a traditional web developer role, I’ve gotten progressively closer to all things data-related. Most recently, this has included a lot of development in business intelligence tools such as QlikView. Qlikview is a powerful ETL and number-crunching tool but to call the look and feel of the application “dated” is putting it politely. I have been able to develop QlikView applications that give the end users the impression of interacting with a website developed with modern HTML5/CSS3 by liberally using a simple QlikView object: the textbox.
As I work on getting the new Jekyll site up and running, one of my primary concerns is tagging.
So after a lot of tinkering, I finally have a blog.