Data Science vs. AI: What’s the Difference?

Key takeaways

• The differences between data science and AI pertain to how you use data in each field, with data science focusing on informing the decision-making process and AI on recognizing patterns in data to learn to automate tasks.

• Similarly, when examining machine learning versus AI versus data science, you’ll find that AI uses machine learning and deep learning algorithms trained on data sets to perform specific tasks.

• In cybersecurity, AI can help identify vulnerabilities susceptible to attack and detect malware within a network.

When comparing AI and data science to decide which is better, note that it depends on your intended use case, since each uses data differently. Discover more in this article about the differences between data science versus AI and how you can use each. If you’re ready to begin expanding your knowledge, consider enrolling in the Generative AI for Data Scientists Specialization from IBM. In as few as four weeks, you’ll have an opportunity to build in-demand skills in predictive modeling, data ethics, exploratory data analysis, and feature engineering.

Data Science

Data science is the process of extracting valuable insights from data to inform decisions and develop effective strategies. It's a multi-step process consisting of data collection, storage, processing, analysis, and, lastly, communication of your findings. Each of these phases requires different skills and techniques. Knowledge of programming languages such as Python and R is fundamental for a data scientist, as is data visualization, which helps communicate the insights you derive from data effectively.

What is data science used for?

You can use data science to inform decision-making across fields and industries. Take a look at some examples of real-world applications of data science across several industries:

• Finance: The financial sector benefits greatly from having access to large amounts of data. This provides opportunities for data science in fraud detection systems and helps banks manage and assess risk when providing loans.

• Health care: Data science is transforming health care through predictive analytics, in which algorithms can spot patterns that signal potential health concerns and address them quickly. Additionally, data science allows for more personalized health care, helping doctors develop individualized treatment plans informed by unique genetic traits.

• Energy: Using geographic information systems (GIS) data, oil and gas industry data scientists can locate potential drilling sites for their companies and optimize the use of existing sites.

• Gaming: Utilize data science to analyze player behavior and develop a more engrossing game experience. The industry also uses it to reduce cheating and ensure fair play in games such as Call of Duty.

Advantages of data science

Data science offers several advantages, including informing and influencing the decision-making processes of businesses and organizations. For example, businesses can use data science to learn key insights about their customers, enabling them to develop products that align with consumer demand. Additionally, data insights allow businesses to assess their operational efficiency and identify areas for improvement. This can lead to reduced costs and increased profits.

Disadvantages of data science

Challenges frequently encountered in data science include poor data quality and limited access to data. Data can be inaccurate and disorganized, making it challenging to work with. To combat this as a data scientist, you must know how to use tools for tasks such as data integration, storage, and cleaning. This presents a problem for employers since finding employees with the necessary skills is difficult, making data scientists a highly in-demand position.

Artificial intelligence (AI)

Artificial intelligence is a technology that allows computers and machines to learn from data (information) and experience, similarly to humans. You can implement several ways to accomplish AI, but one of the most widespread is machine learning, which uses algorithms trained on data sets to create models that can perform specific tasks, such as identifying patterns or categorizing information.

Within machine learning, you have another subcategory: deep learning. Deep learning is an advanced type of machine learning that requires larger amounts of data and features more complex neural networks, allowing computers to learn almost independently. Many applications used today employ both machine learning and deep learning.

What is artificial intelligence used for?

Artificial intelligence already powers technology you likely use, such as chatbots and the recommendations you receive on your social media feed. AI has a wide range of use cases, including the following:

• Search engines: Google and other popular search engines use artificial intelligence algorithms to provide high-quality search results and information similar to your search.

• Education: Artificial intelligence can personalize the learning process in schools and other learning environments by monitoring progress and identifying areas needing improvement.

• Transportation: Artificial intelligence powers self-driving vehicles. With the input data the systems receive, autonomous vehicles learn to observe their surroundings and make correct, safe decisions.

• Cybersecurity: Artificial intelligence helps keep computer networks safe by scanning for weak points that could be the focus of an attack and identifying potential malware and cyberattacks within the network.

Advantages of AI

AI can help organizations enhance their processes, improve efficiency, and boost productivity. Rather than replacing workers entirely, AI is better suited to freeing employees' time, allowing them to focus more on important tasks rather than simple, repetitive duties. Ultimately, AI enables businesses to enhance outcomes and product offerings.

Disadvantages of AI

Although AI has several benefits, one of the main challenges with implementing AI solutions is the cost of getting things up and running. While businesses can recoup these costs later on, it remains a significant investment. Similarly, AI systems or tools may not be the best for every scenario and in some cases, may be less efficient than a human performing the task themselves.

Which is better, data science or AI?

Neither data science nor AI is necessarily better than the other, as each plays a different role, with data science giving you the ability to learn from data so you can make informed decisions, while artificial intelligence learns directly from data and automatically makes decisions accordingly. Put simply, data science transforms data into usable insights, while artificial intelligence takes data and learns to automate systems and processes.

Data Science vs. AI: Other things to consider

Data science and artificial intelligence share some commonalities. Both fields rely heavily on large amounts of data and sophisticated algorithms. In fact, you can use data science algorithms within machine learning. If you’re considering whether to pursue a career in data science or AI, both fields offer outstanding job outlooks for the future. The US Bureau of Labor Statistics expects demand for data scientists to increase by 35 percent from 2025 to 2035 [1], while the artificial intelligence market is projected to grow by 30.6 percent annually from 2026 to 2033, according to Grand View Research [2].

Read more: Data Science vs. Machine Learning: What’s the Difference?

Explore our free data science and AI resources

Subscribe to our weekly LinkedIn newsletter, Career Chat, for updates on popular skills, tools, certifications, and more. Then, check out some of our other free resources to keep learning more about data science and AI:

• Watch on YouTube: ChatGPT vs Perplexity: Which AI Tool Should You Use?

• Take the quiz: Which Data Science Course Should You Take? Find Out in 1 Minute

• Gain expert insights: 6 Questions with an IBM Data Scientist and AI Engineer

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