we 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.
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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.
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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.