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What we talk about when we talk about data science

Why context matters in data science

Nana Kwame Amagyei
4 min readFeb 5, 2025

Imagine you’re at a coffee shop, and the café attendant already knows your order before you say a word.

No, they don’t have supernatural abilities; they’re just using data!

If you’ve been ordering the same caramel latte every morning at 8 AM, the shop’s system might predict your next order based on past data. This, in a nutshell, is how data science works — it helps us understand patterns and make smart decisions.

But data science isn’t just about predicting coffee cravings. It’s a powerful field that’s transforming industries like healthcare, finance, and even environmental monitoring. In this blog post, we’ll explore what data science is, how it differs from data analytics, its real-world applications, and what it takes to become a data scientist.

Differences between Data Science and Data Analytics, and the importance of a Sociotechnical Perspective.

Many people use data science and data analytics interchangeably, but there are differences.

Data analytics often focus on examining past data to identify trends, generate insights, and support decision-making. It deals with business intelligence and decision-making.

Data science, on the other hand, often builds on data analytics by integrating machine learning, artificial

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Nana Kwame Amagyei
Nana Kwame Amagyei

Written by Nana Kwame Amagyei

A data scientist with a strong foundation in data governance practices, UI/UX design, statistical analysis, and data visualisation.

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