Chapter 1: Meet The Buzzwords

Disclaimer: any sub caught was thrown without malicious intent. Like they say, no offense ????

The right name-dropping of AI is like sprinkling the right amount of magic into a conversation. Suddenly, boring topics like what to eat for lunch become cutting-edge.

But what do these buzzwords actually mean? And why does “big” mean different things to different people?

We’ll break down some of these buzzwords, so whether you’re trying to sound smarter at your next team meeting or just want to finally understand what your data friends are always talking about, I’ve got you covered.

Machine Learning

Machine Learning is essentially teaching computers to spot patterns and learn from data without being explicitly programmed. It’s how Netflix knows you’re secretly into rom-coms (don’t worry, your secret’s safe ??).

But, much like that one student who crams all night only to blank during the actual test, Machine Learning can fumble if not properly trained when it comes to real-world situations. It's not perfect.

The next time you hear someone say ‘machine learning,’ I hope you don’t picture robots cracking textbooks.

Big Data

Big Data refers to datasets so large and complex that traditional data processing methods can’t handle them. But here’s the catch: “big” is relative. For some, it’s terabytes of information; for others, it’s just a really stubborn Excel file.

AI

AI is the flashy sibling of ML—often used interchangeably, but with more swagger and far-reaching implications. Mention AI, and people immediately think of robots taking over the world—or, if you’re a teacher, the mystery behind why all your students’ essays sound suspiciously well-written.

So, when in doubt, call it AI. Even if it’s just glorified if-else statements with a marketing budget. Right?

Data Analytics

While "data analytics" is a legitimate and crucial field within data science, it's also a term that gets thrown around a lot in business settings. It’s often used as a catch-all phrase for any process involving the analysis of data—whether it's a simple Excel pivot table or a complex statistical model. Saying your company is "focused on data analytics" is a surefire way to sound modern and informed, even if the analytics are as basic as counting likes on Instagram.

Data-Driven

Being “data-driven” is the idea that decisions should be guided by data, rather than gut feelings or intuition—and definitely not cherry-picking data to support the choice you’ve already made (hello, confirmation bias!). It’s a versatile term that can make any strategy sound foolproof—just don’t ask too many questions about the data behind it.

Algorithm

An algorithm is essentially a recipe for solving a problem or performing a task. In data science, algorithms are the building blocks of machine learning models and data processing techniques.

Yet, in everyday conversation, the term is often mystified to the point where it sounds like the all-powerful force orchestrating social media when in reality, it’s just the code-crunching work of Meta’s engineers.

Data & Data Points

Ah Yes, Data, the new oil

We now live in a world where 'data' isn’t just the new oil; it's the new everything—currency, language, and possibly your next best friend.

In the data science world, "data" refers to the entire collection of information that’s being analyzed, while "data points" are the individual pieces of that collection. Despite their distinct meanings, these terms are often used as if they’re the same. When someone says, “We have a lot of data,” what that really means is they have a ton of data points.

But like in construction, it’s not about how many bricks you have, but what you can build with them that counts.

I hope you’ve enjoyed meeting the buzzwords. In the next chapter, we’ll hear the story of the real MVPs: the ground soldiers of the data world, The Tools.

Let me know which buzzword is your favourite—or the one that makes you roll your eyes??. Feel free to drop your thoughts and any other buzzwords you love or loathe!