September 27, 2023 1:22 am
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

  • Broad field of study.

  • Has been around forever.

  • Essential to business and government planning.

Artificial Intelligence

  • Specialty within data science.

  • Newer area of study.

  • Mimics human intelligence using algorithms.

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

Data Science

  • Makes predictions based on data.

  • Creates reports to guide human behavior.

Artificial Intelligence

  • Makes decisions based on data.

  • Autonomously preforms tasks usually performed by humans.

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

Data Science

  • Rapidly changing due to advances in AI.

  • Other specialties include finance and database administration.

Artificial Intelligence

  • Rapidly growing with new technologies and opportunities.

  • Specialties include AI research, machine learning engineering, and AI architecture.

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

Data Science

  • Required skills include stats, programming, and communication.

  • Uses Python, MATLAB, R, SAS, and SQL.

  • Heavily relies on AI.

Artificial Intelligence

  • Requires a general understanding of data science.

  • Also uses C++ and Java.

  • Will eventually become part of most jobs.

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.

FAQ

  • Is ChatGPT AI?

    While AI tries to mimic a human’s mind, ChatGPT can only use what it has learned from existing information. We tend to think of AI as an all-knowing entity with its own ideas, but we haven’t quite achieved that level of sophistication. Our article linked above goes into more detail about ChatGPT.

  • What is Machine Learning?

    Using algorithms, Machine Learning takes a set of data and categorizes the data into similar types of information. A common use of this technology is in your phone when you search your photo library for a dog or a tree.

  • What is an algorithm?

    In it’s most simplistic form, an algorithm is a set of instructions. Computers can usually perform instructions very quickly so a very simple algorithm can be processed so fast the results seem to appear instantaneously.

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