For example, data science and machine learning (ML) have a lot to do with each other, so it shouldn't be surprising that many people with only a general understanding of these terms would have trouble figuring out how they differentiate from each other.
Here’s the best way to identify the differences between data science and ML, with both principle and technological approaches.
What's the Difference Between Data Science and Machine Learning?
First of all, data science is really a broad, overarching category of technology that encompasses many different types of projects and creations. (For more on what's involved in a data science job, see Job Role: Data Scientist.)
Data science is essentially the practice of working with big data. It emerged as Moore’s law and the proliferation of more efficient storage devices led to enormous amounts of data being collected by companies and other parties. Then, big data platforms and tools like Hadoop began to redefine computing by changing how data management works.
In its simplest form, data science is the way we manage that data, from cleaning it and refining it to putting it to use in the form of insights.
The definition of machine learning is much narrower. In machine learning, technologies take in data and put it through algorithms, in order to simulate human cognitive processes described as “learning.”
In other words, having taken in the data and trained on it, the computer is able to provide its own results, where the technology seems to have learned from the processes that programmers put in place. (Read Straight From the Programming Experts: What Functional Programming Language is Best to Learn Now?)
How Do Data Science and Machine Learning Skill Sets Differentiate?
Another way to contrast data science and ML is to look at the different skills that are most valuable for professionals in either of these fields. (Learn about this job role: Machine Learning Engineer.)
There’s a general consensus that data scientists benefit from deep analytical and mathematics skills, hands-on experience with database technologies, and knowledge of programming languages like Python or other packages that are used for parsing big data.
“Anyone who’s interested in building a strong career in (data science) should gain key skills in three departments: analytics, programming and domain knowledge,” writes Srihari Sasikumar at Simplilearn. “Going one level deeper, the following skills will help you carve out a niche as a data scientist: Strong knowledge of Python, SAS, R (and) Scala, hands-on experience in SQL database coding, ability to work with unstructured data from various sources like video and social media, understand multiple analytical functions (and) knowledge of machine learning.”
On the ML side, experts often cite data modeling skills, probability and statistics knowledge, and broader programming skills as helpful tools in the ML engineer’s toolkit.
Machine Learning: How to Spot ML
The key here is that all sorts of things comprise data science work, but it’s not ML unless you have a very strict regimen set up to help the computer learn from its inputs.
When that is in place, it makes for some surprisingly capable systems that can have broad-ranging effects on our lives.
“Much of what we do with machine learning happens beneath the surface,” Amazon founder Jeff Bezos has reportedly said, pointing out some of the applications of these types of systems.
“Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.”
One of the most helpful examples here is the emergence of the neural network — it’s a common and popular method of setting up machine learning processes. (Read Neurotechnology Vs. Neural Networks: What’s the Difference?)
In its most basic form, the neural network is composed of layers of artificial neurons. Each individual artificial neuron has functionality equivalent to a biological neuron – but instead of synapses and dendrites, it has inputs, an activation function and eventual outputs.
The neural network is made to act like a human brain, and machine learning professionals often utilize this model to create machine learning results.
However, that’s not the only way to do machine learning. Some more rudimentary machine learning projects simply include showing a computer a wide range of photographs (or supplying it with other raw data), inputting ideas through the process of using supervised machine learning and label data, and having the computer eventually be able to discriminate between various shapes or items in a visual field. (For the basics on machine learning, check out Machine Learning 101.)
ML Vs. Data Science: Two Cutting-Edge Disciplines
ML is a valuable part of data science. But data science represents the vaster frontier and the context in which machine learning takes place.
In a way, you could say that ML never would happen without big data. Big data itself didn’t create machine learning, though — instead, after we had collectively aggregated so much data that we almost didn’t know what to do with it, the top minds came up with these bio-simulating processes as a supercharged way of providing insights.
Another good thing to keep in mind here is that data science can be applied in two major ways — we can embrace ML and AI, letting computers think for us, or we can bring data science back to a more human-centered approach where the computer simply presents results and we as humans make the decisions.
That’s leading some experts, including some of today’s top innovators, to call for a more vibrant accounting of the ways in which we use these technologies.
"(AI) is capable of vastly more than almost anyone knows and the rate of improvement is exponential,” Elon Musk has been quoted as saying, while warning that machine learning and AI programs require oversight.
In any case, both data science and ML are core parts of the progress that we as societies are making in technology today.