Data Science vs. Artificial Intelligence & Machine Learning: What’s the Difference?

Though data science, artificial intelligence, and machine learning are all connected, there are key differences. Learn more about the distinctions from Rice.

data science vs machine learning

You may often hear the terms data science, machine learning, and artificial intelligence (AI) used interchangeably. While these are all connected, there are meaningful differences. Machine learning is the science of designing self-running software that can learn autonomously or in concert with other machines or humans. Machine learning helps make artificial intelligence — the science of making machines capable of human-like decision-making — possible.

Data science is the process of developing systems that gather and analyze disparate information to uncover solutions to various business challenges and solve real-world problems. Machine learning is used in data science to help discover patterns and automate the process of data analysis. Data science contributes to the growth of both AI and machine learning. This article will help you better understand the differences between AI, machine learning, and data science as they relate to careers, skills, education, and more.

What is Artificial Intelligence, and How Does it Connect to Data Science?

While there is debate about the definitions of data science vs. artificial intelligence, AI is a sub-discipline of computer science focused on building computers with flexible intelligence capable of solving complex problems using data, learning from those solutions, and making replicable decisions at scale.

AI-equipped machines are designed to gather and process big data, adjust to new inputs and autonomously act on the insights from that analysis. AI is widely used in everyday applications people interact with, from personalized recommendations of products or services served up on social media and online shopping sites to AI-powered safety functions in cars, the analysis of genetic code to detect medical conditions, and more.

Data scientists contribute to the growth and development of AI. They create algorithms designed to learn patterns and correlations from data, which AI can use to create predictive models that generate insight from data. Data scientists also use AI as a tool to understand data and inform business decision-making.

What is Machine Learning, and How Does it Connect to Data Science?

Machine learning is a subfield of artificial intelligence that makes AI possible by enabling computers to learn how to act like humans and perform human-like tasks using data.

The difference between machine learning and AI is that the goal of machine learning is autonomous programming and learning – the enablement of AI. The difference between data science vs. machine learning is that data scientists create the algorithms that make machine learning happen. Data scientists also use machine learning as a tool to extract meaning from data.

Machine learning is ubiquitous in modern life. It’s what makes it possible for Netflix to recommend videos and movies, smart home systems to automatically adjust indoor temperatures, and health systems to monitor and predict epidemics.

How Data Science, AI, and Machine Learning Work Together

It’s important to consider how data science, machine learning and AI intersect. Together, they make it possible for us to better manage business operations, avoid risks, and safely live, work, and enjoy life.

In concert, data science, machine learning and AI make predictive analytics possible, so data scientists can forecast customer behavior that allows retail services to better serve customers through enhanced inventory control and delivery systems. It makes conversational chatbot technology possible, increasing customer service and healthcare support and makes voice recognition technology that controls smart TVs possible.

Machine learning enables personalized product recommendations, financial advice, and medical care. The combination of data science, machine learning, and AI also underpins best-in-class cybersecurity and fraud detection.

Differences in Job Titles & Salaries in Data Science, AI, and ML

Data scientists work to extract valuable insights from big data. They use computer programs to collect, clean, structure, analyze and visualize big data. They may also program algorithms to query data for different purposes. Machine learning engineers work with data scientists to develop and maintain scalable machine learning software models. AI engineers work closely with data scientists to build deployable versions of the machine learning models.

Job Titles & Salaries in Data Science, AI and ML

Data Scientist $120,444
Data Engineer $112,493
Data Architect $151,437
Chief Data Officer $232,759
AI/ML Engineering Leader $154,284
AI Sr. Consultant $125,000
AI/ML Solutions Architect $120,698
Robotics Engineer (Computer Vision) $99,040
NLP Data Scientist $117,790
Bioinformatics Scientist $106,517
Sr. Bioinformatics Analyst $92,155
*Source: U.S. Bureau of Labor Statistics, Glassdoor, ZipRecruiter

Differences in Skills Needed for Data Science, AI, and ML

In the data science vs. machine learning vs. artificial intelligence area, career choices abound. The three practices are interdisciplinary and require many overlapping foundational computer science skills. But the processes, techniques, and use cases differ.

Data Science

Data scientists focus on collecting, processing, analyzing, visualizing, and making predictions based on data. In data science, the focus remains on building models that can extract insights from data. Skills required include programming, data visualization, statistics, and coding. Data scientists are instrumental in every industry, using their skills to identify medical conditions, optimize logistics, inform city planning, fight fraud, improve shopping experiences, and more.

Machine Learning

Data scientists who work in machine learning make it possible for machines to learn from data and generate accurate results. In machine learning, the focus is on enabling machines to easily analyze large sets of data and make correct decisions with minimal human intervention. Skills required include statistics, probability, data modeling, mathematics, and natural language processing. Machine learning specialists develop applications based on algorithms that can detect defects in parts, improve manufacturing processes, streamline inventory and supply chain management, prevent crime, and more.

Artificial Intelligence

Data scientists who specialize in artificial intelligence build models that can emulate human intelligence. AI involves the process of learning, reasoning, and self-correction. Skills required include programming, statistics, signal processing techniques and model evaluation. AI specialists are behind our options to use AI-powered personal assistants and entertainment and social apps, make autonomous vehicles possible and ensure payment technologies are safe to use.

Differences in Degrees Needed to Pursue a Career in Data Science, AI, and ML

Some data scientists begin their careers with a bachelor’s degree. However, those with aspirations for executive-level positions can meet employer requirements and achieve their career goals with a Master of Data Science degree from Rice University. The MDS@Rice degree program offers the opportunity to learn from industry experts and supportive faculty members. The robust curriculum provides exposure to current applications and hands-on experience. No matter if your interest lies in data science vs. machine learning vs. artificial intelligence, the Master of Data Science at Rice University is a great way to position yourself for a rewarding and long-term career.

Learn More About How to Become a Data Scientist

The Master of Data Science at Rice University is a great way to enhance your engineering skills and prepare you for a professional data science career in machine learning or AI. Learn more about the data science career and how the MDS@Rice curriculum will prepare you to meet the demands of employers.


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