In 2013, I came across this famous blog post by Jeff Leek. Little did I know that I would revisit this post 9 years down the lane. As it turns out, it’s not a bad idea to revisit old work with fresh eyes and new perspectives, which is what I have attempted to do here based on personal experience.
Without further ado, let’s talk about a typical data science project and let’s break it down into a few possible scenarios and their consequences (hint: the reality is always in between).
Every year a set of memes is circulated widely on social media, the 2022 version of which goes along the lines of:
My goal for 2022 is to accomplish the goals of 2021 which I should have done in 2020 because I made a promise in 2019 and planned in 2018.
Over the years, this remained my most favorite ‘new year’ meme for no real reason other than that it was hilarious, funny, and relatable, but in 2021, the actuality of these lines hit me hard as it tested my mettle out and out by bestowing a series of unfinished businesses and frequent hiccups throughout the year!
The last day of 2020 is finally here. To bring this decade to a close, I thought I would take a moment to share some of the highlights from this year in the form of pictures and anecdotes.
As much as I would love to remember this year as a year of setbacks and bottlenecks, in retrospect, it has been packed with some very surprising yet noteworthy moments.
On a personal level, 2020 has been a year of many firsts, much as has been the case for the society in general where we have together achieved incredible things as a community despite challenging circumstances.
As tough as this year has been, it goes without saying that 2020 is a particularly good year to be thankful for science, which happens to be one of the many things that are often taken for granted. While similar thankfulness posts have appeared elsewhere, I want to share a different take as a computational scientist.
As the title suggests, I am shamelessly borrowing idea from the so-called most influential series that are widespread in the popular culture.
This post is motivated by the growing list of awesome public repositories that curate a list of resources dedicated to a specific topic (such as this, this, and this, among others).
As a graduate student, I often struggled to find such a centralized resource, especially when it comes to student paper awards and travel grants, geared towards data science graduate students and postdocs (broadly defined). On the surface, these awards help you save a few hundred dollars of registration and other out-of-pocket travel expenses but they are also worth the effort to go the extra mile in getting a nice gold star for a CV.
This 15th August marked 7 years since I submitted my first paper (first-authored research article). Coincidentally, it also completed five calendar years last December, which gave me a chance to measure it’s 5-year impact (erring on the conservative side of course).
I am very happy to see that it has been well-received as shown in the figure above (the R code to generate the plot is publicly available). To interpret it more accurately in plain English, it has performed significantly better than an average paper in the same journal or same discipline (Statistics).
This is a repost backdated to match the original post that appeared in Nature Research Microbiology Community in July 2019. It represents the backstory behind the following publication:
I hope you enjoy the read. Comments and feedback are always welcome :)
This is a repost backdated to match the original post that appeared in Nature Research Microbiology Community in January 2018. In case you are wondering, this post is a prelude to the following publication:
I hope you enjoy reading my first ever technical blog post as much as I enjoyed writing it :)