Don’t Be the Baker Who Can’t Smell the Cake

TL;DR: Whether you’re developing a product or selling it, understanding the fundamentals is essential.

Who wants a future where bakers can’t tell good flour from bad, or gardeners can’t differentiate between organic and pesticide-laden veggies?

There’s a concerning trend in the tech world that’s worth discussing. Bear with me; I might get a bit technical.

Just like we wouldn’t tolerate bland baguettes or contaminated food, we shouldn’t settle for mediocrity in tech. Imagine a DevOps engineer confused about stateful versus stateless pods, a business intelligence consultant mixing up tables and views, or a programmer who doesn’t know the difference between concurrency and parallelism.

While specialization and speed are valuable, the rise of “click programming” is baffling. How can someone call themselves a Data Scientist and neglect basic OOP principles? It’s equally confusing to see Machine Learning engineers unaware of first and second-order optimization, or Node.js developers who don’t understand threading.

Fundamentals matter. While salespeople don’t need to know the difference between discrete convolutions and fully connected neural networks, they should understand the products they’re selling, especially if they’re AI-powered. “Selling ice to Eskimos” doesn’t apply when you’re selling cars, healthcare, or virtual assistants.

Basic understanding of AI and Machine Learning concepts is important for a sales person without any necessity to be an expert.

Taking pride in mastering the basics and striving for continuous improvement, a la Kaizen, offers more than just a paycheck. It’s about the satisfaction of a job well done.

Or maybe I’m missing something.

Now, before closing the subject here are a few aspects that either a Data Scientist should consider as fundamentals or a sales person (say “client-facing” team member) should interiorize.

Top fundamentals for Aspiring Data Scientists

• Understanding and mastering as many as possible core algorithms, data structures. This of course compounds with having good programming skills including solid OOP principles.
• A solid foundation in statistics is essential both for making data-driven decisions as well as for “low level” tasks such as understanding data distributions, and validating models.
• Good grasp of key machine learning concepts ranging (but not limited to) from supervised and unsupervised learning, model evaluation, and first vs. second-order optimization techniques.
• The ability “get down and dirty” and clean, manipulate, and visualize data is crucial. Good understanding of tools and libraries such as pandas, numpy, matplotlib a.s.o.

Fundamentals for sales team:

• Know your product core features and primary functionalities and understand how they solve customer pain points
• Basic understanding of AI and Machine Learning concepts is important without any necessity to be an expert.
• Use cases, success stories, corner cases — all these build credibility with focus on understanding product limitations and the upcoming features and product roadmap.