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Data Science vs Machine Learning: Which is Right for You, and Why?

Published on: Oct 18, 2022
By: Editorial Staff
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Data Science vs Machine Learning: What’s the relationship?

Once you know more about the disciplines themselves, the relationship between data science and machine learning becomes much more understandable:

AI, ML, Deep Learning, Data Sci diagram

A visualization of the relationship between artificial intelligence, machine learning, deep learning, and data science by Meet Patel.

If there were a battle of the tech buzzwords, the final round would surely be fought out between ‘data science’ and ‘machine learning’. Businesses have flocked to data-driven decision-making over the last two decades. As they have increasingly deployed AI technologies to increase productivity, drive value, and improve customer satisfaction, data science and machine learning have become ubiquitous, gracing the tongues of shrewd business execs and eager undergrads alike.Ten years ago, the Harvard Business Review ordained data scientist the “sexiest job of the 21st century.” This now feels like an understatement.In recent years, machine learning earned similar superlatives, with ‘machine learning engineer’ topping Indeed’s Best Jobs of 2019 list and placing fourth among LinkedIn’s 2022 Jobs on the Rise.

Despite this acclaim, there remains considerable confusion about what specifically data science and machine learning entail, driven not only by their relative novelty and highly technical natures but also by the considerable overlap between them. This confusion presents a problem at both ends of the workforce pipeline, with business leaders unsure of how to leverage data science and machine learning to improve their businesses and students unsure as to which of the two career paths — data scientist or machine learning engineer — would best fit their interests and aptitudes.

In this article, we will dispel this confusion by examining the differences — and overlap — between data science and machine learning. We’ll also provide insights into how the two are being applied every day. Finally, we’ll give an overview of what it’s like to work in data science and machine learning before suggesting some ways you can get started in each.

What is machine learning?

A subset of artificial intelligence, machine learning focuses on the development of mathematical algorithms that allow computers to progressively improve their capabilities — “learning” as they go. As Stuart Russell and Peter Norvig put it in Artificial Intelligence: A Modern Approach, the leading AI textbook, in machine learning “a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems.” There are three fundamental paradigms of machine learning: supervised learning, unsupervised learning, and reinforcement learning. 

Supervised Learning

Supervised learning refers to the use of labeled data sets — where each piece of data is tagged and classified — to train a machine learning algorithm to give the correct output when fed an input. Essentially, supervised learning helps algorithms build an ML model by learning by example. A common use of supervised learning is in email spam filters, where an algorithm is fed many examples of confirmed spam and gradually learns to identify it without prompting. 

Unsupervised Learning

Unsupervised learning refers to the use of unlabeled data sets to train a machine learning algorithm. Rather than learning by example, unsupervised ML algorithms are written to be able to make sense of the data themselves: discovering patterns and relationships, forming clusters, and cutting through the data to get to the most important data points. Recently, a group of researchers detailed in Nature how they used unsupervised learning to distinguish groups of Alzheimer’s and Parkinson’s Disease patients based on their molecular make-up rather than on how the diseases were presenting.

Reinforcement Learning

Reinforcement learning differs from supervised and unsupervised learning in that it is explicitly goal-oriented, with machine learning algorithms written to behave in ways that will maximize a numerical reward as they engage with a complex environment, usually through things called Markov decision processes. Reinforcement learning is applied throughout industry, including in teaching cars how to drive themselves.

Deep Learning

While not one of these key paradigms, another important method of machine learning is deep learning, where “artificial neural networks” (ANNs) of algorithms inspired by the synapses and neurons of the human brain are used for tasks in natural language processing, computer vision, and many other areas. One notable application of deep learning comes in the form of the chatbots that e-commerce outfits are increasingly deploying as their first line of customer service.

graphic of neural network

A visualization of a neural network by MIT.

What is data science?

According to IBM, data science is “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. [It] encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.” A data scientist follows data through the data pipeline:

  • Identifying research interests and questions

  • Engaging in data preparation to turn unstructured data (or “raw data”) into useable data

  • Analyzing that data, often with the help of machine learning algorithms and models

  • Using data visualization tools to persuasively communicate findings

While the examples of machine learning above foreground the problem-solving capabilities Russell and Norvig identify, with machine learning models interacting with our day-to-day lives, IBM’s definition of data science emphasizes the other side of machine learning’s utility: its ability to provide “hypotheses about the world” that can drive business intelligence and decision-making.

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

Once you know more about the disciplines themselves, the relationship between data science and machine learning becomes much more understandable:

Data science:

extracts business intelligence from big data sets, frequently employing machine learning algorithms and models among its other analytical tools.

Machine learning:

is an important tool for data science, but algorithms that can teach themselves have applications extending beyond mere business analytics, including medical diagnosis, image recognition, and product recommendation.

To make this relationship clearer, in the next sections we’ll look at how data science and machine learning have recently been used in healthcare.

Data Science in Healthcare

Data science is increasingly being used in healthcare to draw insights from the massive amounts of health-related data being gathered from medical imaging, metric-tracking wearables, clinical trials, and electronic health records. In a paper published in the Irish Journal of Medical Science, researchers describe how insights drawn from big data analytics are leading to advancements in areas of healthcare including:

Disease Surveillance: Fed with data sets from wearables, medical imaging, and the internet, data scientists help drive disease-infrastructure planning, evaluate treatment efficacy, and better understand the spread of disease. Take, for example, Bryn Mawr research assistant Kate Petrova’s study of the correlation between COVID and bad Yankee Candle reviews on Amazon.

Pharmacovigilance: Data science has also made strides in pharmacovigilance, the oversight of drugs for potential adverse effects. Using data mining, for example, data scientists are able to process a vast number of “adverse event reports” and detect potentially lethal drug interactions.

Mental Health: In the field of mental health, data mining has helped researchers develop models that help doctors better understand their patients’ anxiety disorders and predict potentially high-stress situations.

Healthcare Administration: In healthcare administration, data science has helped administrators predict no-shows to optimize clinic capacity, better understand patient sentiment, and minimize unnecessary testing.

ai careers illustration

Source: Michele Marconi for Nature

Machine Learning in Healthcare

While machine learning is behind many of data science’s advancements to healthcare detailed above, machine learning also has substantial applications to healthcare outside of a data science context. An article for Future Healthcare Journal details the ongoing potential of artificial intelligence to improve “diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities.” In particular, machine learning in particular is helping push healthcare in areas including:

Treatment: Through machine learning, doctors are increasingly able to predict which treatments will be most effective for a given patient.

Diagnosis: Deep learning can accurately detect ever more forms of cancer from radiology.

Data Management: Combined with natural language processing, machine learning can analyze unstructured patient notes, perform literature reviews, and transcribe doctor-patient encounters.

Patient Engagement: Machine learning can help drive automated messaging strategies to increase patient compliance with physicians’ instructions and thus boost health outcomes.

Check out our article on machine learning in healthcare to learn more about the advances to diagnosis, treatment, patient experience, and health infrastructure machine learning is making possible. If you’re more interested in how machine learning is impacting other industries, head to our articles on machine learning in business and other machine learning applications

Working in Data Science vs Machine Learning

Given their close relationship, there’s considerable overlap in what’s required to work in data science and machine learning. A machine learning engineer and a data scientist must both have a deep background in statistical analysis, as well as programming skills and experience using cloud computing for managing and analyzing data. Salaries are also comparable: Salary.com estimates the average annual data scientist salary in the US to be $136,609 and the average annual machine learning engineer salary in the US to be $123,134. While data scientists earn slightly more on average than machine learning engineers, both salaries eclipse the average annual salary in the US, which the U.S. Bureau of Labor Statistics estimated to be $58,260 in 2021.

Despite these similarities, there are some important differences to take into consideration if you’re trying to decide which profession to pursue:

Machine Learning Engineer

The primary responsibility of a machine learning engineer is to design, develop, and ship machine learning models — and then upkeep them once they’re shipped and deployed to a live product. An ML engineer might work on a team focused on building a particular product or feature, or they might work as a generalist and move between a variety of different types of projects.

For this work, machine learning engineers must have substantial experience writing algorithms and building machine learning models on platforms like PyTorch and TensorFlow. They generally also need experience with cloud computing platforms like Azure and Amazon Web Services (AWS) and data pipeline tools like Apache Bearn. Oftentimes, companies also like machine learning engineers to come in with some industry experience. Amazon, for example, might wish its new machine learning engineers to have already worked on online-recommendation systems. Tesla, on the other hand, might want the machine learning engineers working on its autonomous driving systems to have already built ML systems employing computer vision.

Data Scientist

As noted above, a data scientist ideates and executes novel approaches that turn raw data into business insights and solutions. After understanding business needs, they determine what types of data are relevant in addressing those needs and what kinds of questions need to be asked of this data, and then help develop machine learning models and other predictive analytics to efficiently carry out this analysis. After the analysis, a data scientist is usually responsible for communicating the results to relevant stakeholders.

While machine learning chops are often necessary to be a successful data scientist, oftentimes data scientists are not required to work as closely with machine learning models as ML engineers. In fact, a data scientist will often work with machine learning engineers to ensure these models and algorithms are developed using the newest and best industry practices. Instead of specializing only in machine learning, data scientists wear many hats throughout the data pipeline, from data preparation, which turns unstructured data (also known as raw data) into data ready for analysis, to data mining, which looks for patterns in big data sets, to data visualization, which makes data science findings more easily understandable to business leaders.

Getting Started: Data Science vs Machine Learning

The overlap between data science and machine learning means that professionals often move between the two, but if you’re just getting started, we have some recommendations for educational paths that will get you interviewing for data science and machine learning positions in no time. 

Machine learning

If you’re coming out of high school and looking to get into ML, you should check out our picks for the best bachelor’s degree programs in computer science, machine learning, and artificial intelligence.

If you already have a bachelor’s degree in a STEM-field, check out these great computer science and machine learning master’s programs. If your bachelor’s degree is in the social sciences or humanities (or even if it’s in certain STEM disciplines), you might need to first take a bridge course or bootcamp in machine learning.

Data science

If you’re coming out of high school and looking to become a data scientist, head over to Data Science Programs for great recommendations for data science bootcamps and degree programs. 

If you already have a bachelor’s degree in a STEM-field, a data science bootcamp or master’s program is a great way to take the next step towards being a data scientist. Data Science Programs has some great ideas to get you started. 

Executive Education

If you aren’t looking for a degree program, but rather a way to get smart on machine learning and data science and how they might be applied to your industry, you can find some stellar programs on our executive education recommendation page.