Gallup reports that about six out of every 10 U.S. managers plan to hire more people with data science skills in the next five years. Meanwhile, the U.S. Bureau of Labor Statistics (BLS) expects the number of positions for data scientists to increase by 35 percent between 2025 and 2035, far faster than the average growth projected for all occupations.
With that rising demand in the job market, developing data science expertise is a good way to prepare for a changing economy. And part of understanding data science is clarifying what machine learning is and where the fields overlap and differ.
For professionals ready to advance in their careers, completing a Master of Science in Data Science program can be an excellent way to gain a versatile foundation in the skills needed for roles in data science and machine learning.
What Is Data Science?
Data science is a multidisciplinary field focused on deriving meaning from large and generally messy datasets. Professionals working in the field collect and manipulate raw data and then analyze it to help find solutions to real-world business problems.
Data science developed as a discipline in the early 2000s as computing power and data storage technology improved, making large-scale data analysis possible. Data scientists apply data science methods to test hypotheses and then translate their findings into recommendations that guide organizations’ business decisions.
Data scientists frequently identify new problems and use statistics, data mining, data modeling, programming, and machine learning processes to address them. They also interpret those results using reports and data visualizations to clarify their meaning for their nontechnical colleagues.
What Is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) that refers to systems that learn from data and make decisions with little human involvement. A machine learning model can process volumes of data that far exceed what a human could ever examine in a lifetime.
Machine learning developed from work in pattern recognition and statistical modeling. An early example of machine learning is a computer program for playing checkers created in 1959 that demonstrated that a machine could improve through experience. Research on neural networks during the 1980s and 1990s played an important role in the further development of machine learning. Deep learning, a method of processing data inspired by how the human brain works that was developed in the 2010s, accelerated the evolution of machine learning.
Different types of machine learning include supervised learning (to describe models trained on structured data) and unsupervised learning (for models that can find hidden patterns in unstructured data). Reinforcement learning, another type of machine learning model, enhances its recommendations through trial and error based on successful outcomes.
Similarities Between Data Science and Machine Learning
People often mention data science and machine learning together, even though they are separate fields of work. This is because roles in these fields have some similar responsibilities, skill requirements, and even compensation. In preparing for these roles, individuals often study similar coursework, spanning topics that range from data science to computer science.
The following are some key similarities between data science and machine learning.
Similar or Shared Responsibilities
Access to large datasets is needed by professionals working in both data science and machine learning roles. They use that data to develop models that help make predictions and decisions. Professionals in both types of roles test models for accuracy and update them when new data becomes available. It is common for data scientists and machine learning engineers to collaborate on projects, sharing responsibilities for data cleaning, preparation, and analysis.
Overlapping Skill Requirements
Individuals in both fields need data analysis skills. They must also have knowledge of programming languages such as Python and R. An understanding of statistics, probability and data modeling and evaluation is also typically required for roles in both areas. Data scientists often work directly with machine learning and deep learning models.
Comparable Salaries
Compensation for positions in both areas is similar. The BLS reports that data scientists had a median annual wage of $120,230 in 2025. Payscale reports that machine learning engineers had a median annual salary of approximately $126,000 as of October 2026.
Differences Between Data Science and Machine Learning
The primary difference between data science and machine learning is in the kind of work that professionals in each field carry out every day. The following are some key differences between data science and machine learning.
Areas of Focus
Data science projects begin with a business question. Data scientists identify a problem, then select the right data pool to be analyzed to find a solution. Much of their work goes into finding, cleaning and preparing data from various sources. They then analyze the data to generate useful insights that inform leaders’ decision-making.
Typically, machine learning engineers step in after data scientists have prepared the data. They focus on a specific problem and design algorithms that help systems learn from that prepared data. Once a model is running, it improves its predictions over time with little human supervision.
Distinct Skill Requirements
Data scientists need to be proficient with SQL, which they use for database coding, and big data tools like Apache Hadoop and Apache Spark. To create data visualizations used to communicate their findings to nontechnical audiences, they must know how to use platforms such as Tableau and Microsoft Power BI.
Machine learning engineers focus more on computer science fundamentals. Their work requires them to have knowledge of applied mathematics, data structures and algorithm design. They also are required to have experience in developing machine learning tools and working with deep learning technology.
Develop the Skills for a Data-Driven Career
Choosing between a data science and a machine learning career depends on how an individual wants to work with data and artificial intelligence. If you’re interested in transforming information into insights that can be used to address business problems and then explaining what the data is saying to decision-makers, data science could be a rewarding option for you. If you’re more interested in constructing and improving systems that can learn on their own, and if you enjoy programming and solving technical problems, machine learning may be a better fit.
As both data science and machine learning roles require professionals who understand how to use, manage and analyze data at advanced levels, those interested in either field can benefit from pursuing a graduate education.
SEMO Online’s Master of Science in Data Science program emphasizes hands-on learning and gives students the opportunity to work with real datasets. Offered in a HyFlex (hybrid flexible) format with online and in-person learning options, this 30-credit-hour program’s experiential approach helps ensure that graduates have both the technical skills and the practical experience they need to solve complex data challenges across industries.
Find out how SEMO Online’s technology degree programs can help you develop the skills that employers are looking for.
Recommended Readings
Computer Information Systems vs. Computer Science
What Can You Do with a Master’s in Data Science?
How to Get Into Business Data Analytics
Sources:
Data.org, “Machine Learning: What It Is and Why It Matters”
Digital Society, “Are Artificial Intelligence, Machine Learning, and Data Science All the Same? The Public Sure Thinks So, and Why That’s Problematic”
Gallup, “U.S. Managers Say Data Science Skills Needed Now, in Future”
IBM, “Data Science vs. Machine Learning: What’s the Difference?”
Indeed, “Data Science vs. Machine Learning”
Payscale, Average Machine Learning Engineer Salary
TechTarget, “Data Science vs. Machine Learning: What’s the Difference?”