Data Science vs. Artificial Intelligence: What's the Difference?

Data science is a broad field that involves analyzing data systems to draw inferences and make predictions. Artificial intelligence (AI) is a subset of data science that involves processing information to perform tasks usually done by humans.

This article explains the difference between data science vs. artificial intelligence, including how they relate and differ, as well as their applications, advantages, and limitations.

Overall Findings

Data science combines statistical analysis, computer science, and the scientific method to draw inferences from raw, unstructured data. Businesses and other organizations rely on data science (usually in the form of charts and graphs) to make important decisions about allocating resources.

Artificial intelligence is a collection of algorithms designed to simulate human intelligence. These algorithms use machine learning and deep learning to improve decision-making processes as they are fed more data.

Data science has been around for a long time, but the advent of artificial intelligence has revolutionized the field because AI algorithms can analyze data much faster than humans can.

Applications: AI Makes Decisions Based on Data Science

The main job of a data scientist is to generate reports to help decision-making. They make predictions, and sometimes recommendations, but other humans, such as business analysts, usually do the decision-making. AI can actually make data-driven, logical decisions for humans.

Practical applications of AI include speech and facial recognition, quality control, customer service, environmental analysis, stock trading, and even medical diagnosis. AI can be especially helpful for automating repetitive tasks, but it can also be used for much more complex jobs. For example, self-driving vehicles use AI to navigate traffic using real-time sensor data. AI also powers chatbots like ChatGPT and virtual assistants like Alexa and Siri.

Careers: Both Fields Are Growing and Changing

AI engineering and data science are lucrative career options with salaries in the six figures. Both fields are projected to grow as artificial intelligence becomes crucial to everyday business operations.

While AI engineering is considered a niche field within the broader umbrella of data science, an understanding of AI is increasingly important in all areas of data science. Just as there are data scientists who focus on AI development, there are data scientists who specialize in fraud detection, finance, and risk analysis.

All data scientists may use AI in their work, but the AI algorithms themselves are typically developed by specialists called AI engineers. AI engineers and other data scientists work closely together.

Training: Data Scientists and AI Engineers Need Similar Backgrounds

Aspiring data scientists require extensive training in statistics and computer programming. Data scientists also benefit from strong writing and speaking skills to communicate their findings. Tools and methods used by data scientists include Python, MATLAB, R, SAS, SQL, data visualization, predictive causal analytics, and prescriptive analysis.

On top of general data science skills, AI engineers should be comfortable with programming languages like C++ and Java. As with data science, there are specialties in the field, including AI research, machine learning engineering, and AI architecture.

Many other professions use AI for various purposes, from data analysis to customer service. Not everyone needs to be an AI expert, but anyone pursuing data science needs to be comfortable with the technology.

Final Verdict

AI has obvious limitations because it depends on the accuracy of the data provided to it. For that reason, human data scientists will always be necessary, but their jobs are changing thanks to AI.