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Understanding the differences: data science, data analytics, and machine learning
At the University of Virginia (UVA) School of Data Science, we take pride in being the first data science school in the country. Whether you are just hearing about us or are actively thinking of applying in the future, we are thrilled that you’re interested in such a fascinating field.
We speak with prospective applicants all the time. In these conversations, we get a wide variety of questions. That said, we often see questions surrounding the differences between three seemingly similar, yet very different concepts: data science, data analytics, and machine learning.
Consequently, we want this blog post to provide clarity. While these concepts may seem interchangeable on the surface, there are some key distinctions that we must keep in mind.
What is Data Science?
Let’s start with the big picture. At its core, data science is about leveraging multiple disciplines (like computer science and statistics) to extract knowledge from data. The data could be everything from sensor data on a fleet of aircraft to satellite data that tracks a forest’s wildfire risk.
Data scientists have plenty of tools at their disposal. Some of the more common tools, however, are R and Python. The pre-built frameworks within these languages make it easy for data scientists to get started. From cleaning raw data to testing models for reliability, data scientists wear many different hats.
Out of these three, data science is the broadest category. In other words, data analytics and machine learning are within the data science umbrella.
What is Data Analytics?
Now, let’s talk about data analytics. While data science is mostly used to understand and extract knowledge from the data, data analytics looks more at insights from already-existing datasets.
Yes, data analysts are evaluating large pieces of data in response to some type of question. That said, data analysts are more often looking at structured data to solve business problems. They then present their findings through things like data visualization.
Simply put, data analysts aren’t interpreting this data for the sake of interpreting it. Rather, data analysts are interpreting the data to help inform data-driven decision-making. Whether or not data “is the new oil,” data analysts make sense of a world that is being inundated with all types of data.
While their work is similar to that of data scientists, data analysts are often looking at the past. They are analyzing already-existing data to extrapolate patterns. After discovering those patterns, they can help decision-makers make better, more data-informed decisions.
What is Machine Learning?
Finally, let’s talk about machine learning. Machine learning (also called ML) is a type of artificial intelligence that helps systems “learn” from prior data. In effect, with ML, you are focused on building algorithms to make predictions about the future. Essentially, this is the world of predictive analytics.
Within ML, there are four broad types of algorithms: supervised, semisupervised, unsupervised, and reinforcement. While each algorithm has its own advantages, their goal is to automatically extract insights from the data and make new recommendations to decision-makers.
This word "automatically" is key here. Yes, humans need to have strong model development and deployment skills. That said, once the model is deployed, it does much of the heavy lifting in extracting key insights and conclusions from the underlying data. It is used in machine learning applications like computer vision, natural language processing, autonomous decision-making, and more.
Adapting to an Ever-Evolving World
Concepts like data science, data analytics, and machine learning aren’t brand new. That said, we are living in fascinating times. The lines that separated all three of these areas are increasingly blurred.
That being said, there are some key characteristics that exist among the three areas. The first is a passion for numbers. If you love digging into statistics or have already created apps with Python, data science, data analysis, or machine learning may be for you.
Along with this, these three disciplines require strong analytical skills. Data scientists and data analysts aren’t satisfied with the first answer. They are willing to dig deep, closely analyze the data, and understand what is really going on.
And then, there is genuine curiosity. ML engineers, data scientists, and data analysts want to understand how the world works. They are perplexed, yet fascinated by the world, and leverage their core competencies to bring more clarity to the puzzles in front of them.
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Preparing for a Data-Driven Future
Although distinct, data science, data analytics, and machine learning share some essential traits:
- A passion for problem-solving through numbers and computation
- A commitment to critical thinking and rigorous analysis
- An enduring curiosity about how complex systems work
These qualities drive our students, faculty, and alumni as they use data to address challenges in fields ranging from healthcare and the environment to finance and public policy. At the UVA School of Data Science, we prepare students to thrive in all of these areas through our interdisciplinary curriculum, experiential learning opportunities, and world-class faculty mentorship.
Whether you envision yourself as a data scientist building advanced models, a data analyst uncovering insights from structured datasets, or a machine learning engineer designing predictive systems, there has never been a more exciting time to join this field.
No matter what, we want to hear from you. You can reach us by emailing us at sdsadmissions@virginia.edu. or connect with us through a building tour or admissions appointment. You can learn more about our different data science degree programs by clicking here.
