Online Degrees Blog at New York Tech
Data Science vs Machine Learning: Understanding the Differences

Data Science vs Machine Learning: Understanding the Differences

Close up of a man working on computer, lines of code reflecting on his glasses.

Data scientists and machine learning engineers are highly in demand thanks to the explosive growth of the global artificial intelligence (AI) market, which has been projected to top $2 trillion.1 As organizations focus on using more data analytics and building strong AI teams, these two roles are deemed essential to creating AI systems that can improve workplace efficiency and innovate products and services. 2

Data science and machine learning are connected, but the focus and applications of these disciplines are different. While data scientists focus on extracting meaning from structured and unstructured data to inform business decision-making and planning, machine learning engineers devise ways for systems to synthesize data that is often complex, learn from it, and then use the insights to fine-tune models over time.

This article examines what distinguishes data scientists from machine learning engineers, the education and skillsets required for both roles, real-life applications, and current demand.

What is Data Science?

In 1962, nearly 20 years before personal computers, American mathematician John W. Tukey predicted the evolution of a new field we know today as modern data science.3 Simply put, data science is “the study of data to extract meaningful insights for business.” 4 Data scientists might also draw on machine learning to carry out this work, but it’s only one element of their toolkit. Think of data science as a broad, multidisciplinary approach to analyzing vast quantities of data that involves combining principles and practices from the fields of artificial intelligence, mathematics, statistics, computer engineering, and more.

What is Machine Learning?

If you’re familiar with self-driving cars, have used speech recognition, or shopped on Amazon, these are just a few examples of machine learning applications in our daily lives. 5

The origins of machine learning date back to 1952 when computer scientist Arthur Samuel developed a checkers program that won against a known checkers master. 3 Machine learning is a subset of artificial intelligence, but it’s also considered a data science technique that involves training AI models to learn from complex datasets faster than any human.

Pursuing a machine learning career means you might build programs that control computers and robots. You will also likely write production-level code that generates product recommendations to customers, which data scientists typically don’t do.6 Additionally, ML engineers work with models data scientists design and then feed the data to computers. If no algorithm exists to solve a complex problem, they may create and implement their own. 6,7

Education and Skill Sets

Whether you’re pursuing a degree in data science or computer science or recently graduated and are launching a job search, it’s important to understand the roles available in data science and machine learning and what credentials and skills are required. That way, you can determine which career path best matches your skills, strengths, and interests.

Data Science Skills

If you choose to specialize in data science, your day will likely involve finding, cleaning, and preparing data for statistical analysis, so it’s crucial to get the data right. But you will also need strong presentation and data visualization skills, so you can communicate your findings to target audiences.

Working in data science typically requires at least a bachelor's degree in mathematics, statistics, computer science, or a related subject. However, the increasing complexity of machine learning means more organizations are seeking specialists, so a master’s degree could give you a competitive edge.

The top skills that employers seek in a data scientist include:

  • Statistical methods
  • Machine learning algorithms
  • Data mining and collection
  • Data structures and database architecture
  • Data visualization
  • Storytelling and design

Machine Learning Skills

Similar to data science, there are a range of career paths available. Some might land a machine learning role after graduating with a degree in computer science, while others first work as data scientists, data engineers, or software engineers.1 However, it’s also possible to transition to a machine learning career after gaining hands-on experience with machine learning models and systems. Regardless of the route you choose, you will need to know how to leverage big data, software development, and IT operations tools.1

The skills required to be successful in machine learning careers include:

  • Applied mathematics
  • Computer programming
  • Probability concepts
  • Statistic methods
  • Data pipelines and infrastructure
  • Data tools such as Hadoop and Hive
  • Computer languages such as Python

Application and Use Cases

Check out some examples of use cases for both data science and machine learning.

Data Science Examples

Some use cases in data science include banks optimizing portfolio recommendations and companies tracking hiring patterns to ensure a diverse workforce.5 Other examples are healthcare companies analyzing data to improve breast cancer detection in patients, while media companies are developing personalized content and target advertising based on data, such as customer interests.5

Machine Learning Examples

Machine learning systems have been used to automate workflows, improving efficiency in the workplace as well as forecasting for better decision-making. For example, in the legal field, automating document reviews allows attorneys and paralegals to focus on client meetings and trial preparation.5 In the energy sector, machine learning shows promise in being able to forecast energy consumption and the maintenance required for infrastructure, such as wind turbines.5 Because machine learning is evolving as a profession, there will continue to be new opportunities for innovation.

Career Opportunities and Demand

There is a myriad of exciting opportunities available to you in data science and machine learning.

Data Science Careers

With so much valuable data on the line, companies are increasingly invested in finding the right people to help capitalize on it—and they’re paying them well, too. Employment of data scientists is projected to grow 35% through 2032, much faster than the average for all occupations.8 With a base pay range between $91,000 and $142,000 per year and a booming job market, data scientists are in demand.9 At large tech companies like Apple, Google, and Meta, salaries range from $150,000 to $170,000 annually.10

Potential job titles in data science include:

  • Data scientist
  • Database administrator
  • Data engineer
  • Analytics manager
  • Quantitative analyst

Machine Learning Careers

With the rise in data from so many sources, such as social media, internet searches, and customer surveys, companies need ML engineers who can deal with the “increase in complexity of machine learning solutions and the constraints being put on these processes, such as the speed of data that needs to get to a model.” Because of the expertise required to perform these functions, the demand for ML engineers continues to grow. The national average annual salary for a machine learning engineer in the U.S. is $127,448.11

Potential job titles in machine learning include:

  • Artificial intelligence engineer
  • Data analyst
  • Machine learning engineer
  • Research scientist
  • Software developer

Find the Best of Both Worlds at New York Tech

Now that you know more about the similarities and differences between data science and machine learning, you can take the next steps you need for your career. Whichever path you choose, you can be confident that you’re gaining skills and knowledge that will be in demand for years to come.

Building on your undergraduate studies in computer science or data science with an online master’s degree is an efficient and effective way to achieve your personal and professional goals. The classes and coursework are offered entirely online and can be completed whenever, and wherever, works best for you. Every aspect of the online master’s programs—the faculty, content, and learning outcomes—are exactly the same as our on-campus degrees but require less time and money to complete.

With a master’s-level designation, as well as the valuable New York network and career resources that New York Institute of Technology provides, you’ll find that the Online Data Science, M.S. is an extremely valuable investments in your future.

Interested? Set up some time to talk with an admissions outreach advisor or take the first step by starting an application.

New York Institute of Technology has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.