Data Science and Artificial Intelligence: What’s the Difference?

Data science is the broad field of analyzing data systems to draw inferences and make predictions. Artificial intelligence (AI) is a subset of data science that processes information to perform tasks normally performed by humans.

This article discusses the differences between data science and artificial intelligence, including their relationship and differences, uses, benefits, and limitations.

Overall findings

Data science combines statistical analysis, computer science, and the scientific method of drawing 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 resource allocation.

Artificial intelligence is a set of algorithms designed to simulate human intelligence. These algorithms use machine learning and deep learning to improve the decision-making process as they are fed with 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.

Application: AI Makes Decisions Based on Data Science

A data scientist’s main job is to create reports that help with decision making. They make predictions and sometimes recommendations, but usually someone else, such as a business analyst, makes the decisions. In fact, AI can replace humans in making logical, data-driven decisions.

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

Careers: Both fields are growing and changing

AI engineering and data science are high-paying career options with salaries in the six figures. Both areas are projected to grow as artificial intelligence becomes integral to day-to-day business operations.

AI engineering is considered a niche area within the wider data science spectrum, but understanding AI across all areas of data science is becoming increasingly important. Just as there are data scientists who focus on AI development, there are also 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 usually developed by specialists called AI engineers. AI engineers and other data scientists work closely together.

Training: Data scientists and AI engineers should have similar backgrounds

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

AI engineers should be familiar with programming languages ​​such as C++ and Java, in addition to general data science skills. Similar to data science, this field also has his specialties in AI research, machine learning engineering, and AI architecture.

Many other professions are also using AI for a variety of purposes, from data analysis to customer service. Not everyone needs to be an AI expert, but those pursuing data science should be tech savvy.

final verdict

AI has obvious limitations as it relies on the accuracy of the data provided. So human data scientists will always be needed, but their jobs are changing thanks to AI.