Year In Review: Best Reads

As measured by reads, data science career articles not to miss!

Adam Ross Nelson
3 min readDec 28, 2023


As we get ready for 2024, there are a few reads worth keeping in mind from 2023. As usual, I continued writing about salary negotiation. This one, Salary Negotiation Advice: The 999th Reason Salary History Is Never A Good Starting Point, might go down as a favorite of all time. I thank you all for liking it too, with nearly 2,000 reads.

Any Mention Chat GPT

It seems like nearly any mention of Chat GPT resulted in more reads. This article, with a dose of Refactoring Data Science Python Code With Chat GPT earned just over 3,000 reads.

Job Searching

An analysis of data from the United States Department of Education produced an article with just over 2,500 reads. Called Where The Data Say Job Seekers Get Hired this article presented an analysis of data from national survey questions that asked, in gist: “how did you get your job?” “How did you find your job.”

The results are illuminating. Also helpful. Helpful for job seekers, I think. Helpful for companies to know too.

Image Credit: Author’s Illustration In Canva. This is an info-graphic with a blue background. It summarizes the results listed within this article.

Further Back All Time Leaders

Looking further back it is worth a mention to note that the all-time leading articles are related to Moving Pandas Columns Around with 1,100 reads but which was a re-write of an earlier article called Reordering Pandas DataFrame Columns: Thumbs Down On Standard Solutions which comes in at over 57,000 reads.

A particularly useful article on the topic of getting data from GDrive into Google Colab has also done well with nearly 4,900 reads. Perhaps also not surprising is the ever pragmatic topic of helping others find new data sets to study where an article called 93 Datasets That Load With A Single Line of Code has earned almost 16,000 reads.

I hope you’ll consider reading more.



Adam Ross Nelson

Ask about my free career course. I mentor new (💫) and aspiring data scientists enter (🚪) and level up (📈) in the field.