Image by Editor | ChatGPT
# Introduction
“Data science”, “data scientist”, “data-driven systems and processes”, and so on…
Data is everywhere and has become a key element in every industry and business, as well as in our very lives. But with so many data-related terms and buzzwords, it is easy to get lost and lose track of what exactly each one means, especially one of the broadest concepts: data science. This article is intended to explain in simple terms what data science is (and what it isn’t), the knowledge areas it involves, common data science processes in the real world, and their impact.
# What is Data Science?
Data science is best described as a blended discipline that combines multiple knowledge areas (explained shortly). Its primary focus is on using and leveraging data to reveal patterns, answer questions, and support decisions — three critical aspects needed in virtually every business and organization today.
Take a retail firm, for instance: data science can help them find out best-selling products at certain seasons (patterns), explain why certain customers are leaving for competitors (questions), and how much inventory to stock for next winter (decisions). Since data is the core asset in any data science process, it is important to identify the relevant data sources. In this retail example, these sources could include purchase histories, customer behaviors and purchases, and sales numbers over time.
Data science example applied to the retail sector | Image generated by OpenAI and partly modified by the Author
So, what are the three key areas that, when blended together, form the scope of data science?
- Math and statistics, to analyze, measure, and understand the main properties of the data
- Computer science, to manage and process large datasets efficiently and effectively through software implementations of mathematical and statistical methods
- Domain knowledge, to ease the “real-world translation” of processes applied, understand requirements, and apply insights gained to the specific application domain: business, health, sports, etc.
Data science is a blended discipline that combines multiple knowledge areas.
# Real World Scope, Processes, and Impact
With so many related areas, like data analysis, data visualization, analytics, and even artificial intelligence (AI), it is important to demystify what data science isn’t. Data science is not limited to collecting, storing, and managing data in databases or performing shallow analyses, nor is it a magic wand that provides answers without domain knowledge and context. It is neither the same as artificial intelligence nor its most data-related subdomain: machine learning.
While AI and machine learning focus on building systems that mimic intelligence by learning from data, data science encompasses the comprehensive process of gathering, cleaning, exploring, and interpreting data to draw insights and guide decision-making. Thus, in simple terms, the essence of data science processes is to deeply analyze and understand data to connect it to the real-world problem at hand.
These activities are often framed as part of a data science lifecycle: a structured, cyclical workflow that typically moves from understanding the business problem to collecting and preparing data, analyzing and modeling it, and finally deploying and monitoring solutions. This ensures that data-driven projects remain practical, aligned with real needs, and continuously improved.
Data science impacts real-world processes in businesses and organizations in several ways:
- Revealing patterns in complex datasets, for instance, customer behavior and preferences over products
- Improving operational and strategic decision-making with insights driven from data, to optimize processes, reduce costs, etc.
- Predicting trends or events, e.g., future demand (the use of machine learning techniques as part of data science processes is common for this purpose)
- Personalizing user experience through products, content, and services, and adapting them to their preferences or needs
To broaden the picture, here are a couple of other domain examples:
- Healthcare: Predicting patient readmission rates, identifying disease outbreaks from public health data, or aiding drug discovery through the analysis of genetic sequences
- Finance: Detecting fraudulent credit card transactions in real time or building models to assess loan risk and creditworthiness
# Clarifying Related Roles
Beginners often find it confusing to distinguish between the many roles in the data space. While data science is broad, here’s a simple breakdown of some of the most common roles you’ll encounter:
- Data Analyst: Focuses on describing the past and present, often through reports, dashboards, and descriptive statistics to answer business questions
- Data Scientist: Works on prediction and inference, often building models and running experiments to forecast future outcomes and uncover hidden insights
- Machine Learning Engineer: Specializes in taking the models created by data scientists and deploying them into production, ensuring they run reliably and at scale
| Role | Focus | Key Activities |
|---|---|---|
| Data Analyst | Describing the past and present | Creates reports and dashboards, uses descriptive statistics, and answers business questions with visualizations. |
| Data Scientist | Prediction and inference | Builds machine learning models, experiments with data, forecasts future outcomes, and uncovers hidden insights. |
| Machine Learning Engineer | Deploying and scaling models | Turns models into production-ready systems, ensures scalability and reliability, and monitors model performance over time. |
Understanding these distinctions helps cut through the buzzwords and makes it easier to see how the pieces fit together.
# Tools of the Trade
So, how do data scientists actually do their work? A key part of the story is the toolkit they rely on to accomplish their tasks.
Data scientists commonly use programming languages like Python and R. Popular libraries for Python (for example) include:
- Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- Scikit-learn or PyTorch for building machine learning models
These tools lower the barrier to entry and make it possible to quickly move from raw data to actionable insights, without having to focus on building your own tools from scratch.
# Conclusion
Data science is a blended, multidisciplinary field that combines math, computer science, and domain expertise to reveal patterns, answer questions, and guide decisions. It isn’t the same as AI or machine learning, though those often play a part. Instead, it’s the structured, practical application of data to solve real-world problems and drive impact.
From retail to healthcare to finance, its applications are everywhere. Whether you’re just getting started or clarifying the buzzwords, understanding the scope, processes, and roles in data science provides a clear first step into this exciting field.
I hope you’ve enjoyed this concise, gentle introduction!
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.
