Data Science vs Machine Learning: What’s the Difference?

The words data science and machine learning are often used interchangeably among those with only a little knowledge of the fields. However, if you are planning to build a career in one of these, it is important to know the differences between machine learning and data science. For that, we need to understand a few important terms that are related but fundamentally different. 

We dive into what exactly Artificial Intelligence and Machine Learning are first, before going into both in a little more detail. Here’s a quick data science vs machine learning summary if you’d just like to know the differences briefly.

Data Science vs Machine Learning: Head-to-Head Comparison

Here is a side-by-side comparison for easy reference.

What is Artificial Intelligence and Data Science?

Artificial intelligence or machine intelligence refers to the intelligent decisions made by machines at par with their human counterparts - at least in certain tasks. It is a study where we enable machines to learn through experience and make them intelligent enough to perform human-like tasks. We have previously discussed the differences between AI and ML, but for the purpose of this article, let’s look at a simple definition of machine learning.

Think of ML as a subset of AI. In the same way, humans learn with experience, machines can learn with data (experience) rather than just following simple instructions. This is called machine learning. Machine learning uses 3types of algorithms:supervised, unsupervised, and reinforced.

Then there’s deep learning, which is a subset of machine learning based on artificial neural networks (think of neural networks similar to our own human brain). Unlike machine learning, deep learning uses multiple layers and structures algorithms such that an artificial neural network is created that learns and makes decisions on its own!

Big Data is another term you might have come across. These refer to humongous sets of data that can be computationally analyzed to understand and process trends, patterns, and human behavior. Big Data plays a role in data science. 

The machine learns on its own through machine learning algorithms – but how? Who gives the necessary inputs to a machine for creating algorithms and models? That’s where data science comes in. Data Science uses different methods, algorithms, processes, and systems to extract, analyze and get insights from data. We have our data science tutorialshere if you want to learn about this in detail.

If we were to see the relationship between all the above in a simple diagram, this is how it would look like this:

Artificial Intelligence includes both machine learning and data science which are correlated. Thus, data science is also a part (the most popular and most important one) of AI.

As we see above, data science and machine learning are closely related and provide useful insights and generate the necessary trends or ‘experience’. In both, we use supervised methods of learning i.e. learning from huge data sets.

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How are both correlated?

Data Science is a broader field of study that uses algorithms and models of machine learning to analyze and process data. Apart from learning, data science also involves data integration, visualization, data engineering, deployment, and business decisions. You may also be wondering about data analytics - but we’ll refer you to our guides on data science vs data analytics and a comparison of the data science and data analyst roles for that.

Difference Between Data Science and Machine Learning

On one hand, data science focuses on data visualization and a better presentation, whereas machine learning focuses more on learning algorithms and learning from real-time data and experience. Always remember – data is the main focus for data science and learning is the main focus for machine learning and that is where the difference lies.

To appreciate this difference more, let us take a use case and see how both data science and machine learning can be used to achieve the results we want.

Let us say you want to purchase a phone on xyz.com. This is the first time you are visiting xyz.com and you are browsing through phones of all ranges. You use various filters to narrow down your preferences and out of the results you get, you choose 4-5 of the phones and compare those. Once you select a phone model, you will see a recommendation below the product – for a similar product at a lower price or with more features, related accessories for the phone you have chosen, and so on. How does the website recommend these when it has little history about you?

That’s through the data from millions of other people who may have tried to purchase the same phone, and searched/bought other accessories along. This makes the system automatically recommend the same to you.

The entire process of collecting data from the users, cleaning and filtering out the required data for evaluation, evaluation of the filtered data for building patterns, finding similar trends and building a model for a recommendation of the same thing to other users, and finally the optimization, is data science.

Where is machine learning in all this? We build models through machine learning algorithms. Based on the data collected and trends generated, the machine understands that these are the accessories that are usually bought by other users with a particular phone. Hence, it suggests the same thing based on what it has ‘experienced’ before.

The modeling step is the most critical step because that is what improves the overall business and makes the machine understand human behavior. If the right machine learning model is applied, it could mean more progressive learning for the machine as well as success for the business model.

This step is called the data modeling step, which is essentially the machine learning phase of the data science lifecycle.

This might seem like a lot, but data science professional courses will explain everything clearly. These specializations go a long way in explaining the fundamentals and the more complicated concepts.

How Does Data Modeling Work?

There are different types of machine learning algorithms, the most common being clustering, matrix factorization, content-based, recommendations, collaborative filtering, and so on. Machine learning involves 5 basic steps.

The huge set of data that we receive in the first step is split into the training set and testing set and the model is built and tested using the training set. A significant portion of data is used for training purposes so that different conditions of input and output can be achieved and the model built is closest to the required result (recommendation, human behavior, trends, etc.). Once built, the model is tested for efficiency and accuracy using the test data so that it can be cross-validated.

As we can see, machine learning comes into the picture only during the data modeling phase of the data science lifecycle. It thus contains machine learning.

With machine learning, the machine can generate complex mathematical algorithms that need not be programmed by a human, and further can improvise and improve the programs by itself. When compared to traditional statistical analysis techniques, machine learning evolves as a better way of extracting and processing the most complex sets of big data, thereby making data science easier and less chaotic.

Furthermore, machines tend to be more accurate and have a better memory than humans, they can learn and produce accurate results based on experiences. We get fast algorithms and data-driven models without the errors that are possible by humans.

Careers Opportunities with Machine Learning and Data Science

As we’ve mentioned, much of what you will learn is applicable to both machine learning and data science. However, there are more specific roles in both fields.

With machine learning, you could become a machine learning engineer, a Natural Language Processing Scientist, a software developer focused on ML, and of course, a data scientist.

With data science, you could become a data scientist, business intelligence developer, data analyst, data engineer, data architect, and machine learning engineer.

Bear in mind that many of these roles are accessible to both fields of study, though a few specific ones might require some specialization. Either way, you’ll need a solid understanding of mathematics, statistics, and some basic software engineering. Machine learning engineers will require more programming experience under their belt. 

There are several professional courses to help you get started, including MIT’s machine learning and AI course, and famous Stanford courses taught by Andrew Ng.

How Do You Choose Between Data Science and Machine Learning?

That’s it for our data science vs machine learning comparison. The fact is you cannot choose only one. Both data science and machine learning go hand in hand.In the future, data scientists will need at least a basic understanding of machine learning to model and interpret big data that is generated every single day.

If you are just starting your career or are from different background like Java or .NET, there is nothing to worry about. 

Data Science is vast but not difficult. Since it has many stages, a data scientist’s job is divided into different sub-fields. 

Check out the tutorials and start learning the basics. Once you understand the core concepts, go deeper into machine learning and deep learning through the resources we’ve mentioned. 

Regardless of whether you have programming experience, you can become a good data scientist by understanding the necessary tools and techniques to work on data and acquiring domain knowledge. One good place to start is by learning R for data science.

Frequently Asked Questions

1. Which is better: data science or machine learning?

Neither is better than the other - it all depends on what roles you’re seeking. If you like to work with big data and find a career in the business world, then perhaps data science is better. If you’d like to work as a machine learning engineer developing algorithms, then perhaps machine learning is better.

2. Is data science the same as machine learning?

There are differences, which we’ve outlined above, but there are many similarities in terms of what you’ll be studying. Your career paths could be different depending on which route you take. Furthermore, data science typically involves finding patterns in data and turning them into actionable insights. Machine learning involves actually building models and algorithms.

3. Which pays more: data science or machine learning?

Machine learning engineers get paid more than data scientists. Their responsibilities typically require more knowledge and an understanding of a wider variety of subjects.

4. Is data science easier than machine learning?

The consensus is that data science is in fact easier than machine learning. Data science involves more statistics, while machine learning involves more computer science in addition to statistics. 

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