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

Data science and AI both rely on data sets to accomplish their impactful work. At a glance, here's what you need to know about these separate but often overlapping fields:

• Both AI and data science are experiencing significant growth. While the US BLS projects that job openings for data scientists will increase by 34% between 2024 and 2034, Statista research suggests the AI market will grow 26.95% by 2031 [1,2].

• While data science utilizes data to inform decision-making and generate insights, AI leverages it to enable machines to act autonomously.

• Data scientists often employ AI techniques, such as machine learning, in their work, although AI remains a distinct field from data science.

In this article, you'll learn about the wide range of applications of data science and AI, some of the benefits and challenges associated with each, and how the two fields overlap. Afterward, if you want to learn about how to use AI for data science, consider enrolling in IBM's Generative AI for Data Scientists Specialization.

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, communicating your findings. Each of these phases requires different skills and techniques. Knowledge of computer programming languages such as Python and R is fundamental to possess as a data scientist, along with data visualization, which helps to communicate the insights you derive from data effectively. 

What is data science used for?

Data science can be used 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, where algorithms can help spot patterns that signal possible health concerns and address them quickly. Additionally, data science allows for more personalized health care, assisting doctors in making individualized treatment plans influenced 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 optimally utilize current working sites.

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 important information about their customers, enabling them to develop products that align with consumer demands. Additionally, insights from data allow businesses to learn about 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. There are several ways to accomplish AI, but one of the most widespread is machine learning, which uses algorithms trained on datasets 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 is used to power technology you likely already 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 cyberattacks and malware in 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.

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, as well as 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 the demand for data scientists to increase 34 percent from 2024 to 2034 [1], while the artificial intelligence market is projected to grow 26.95 percent annually from 2025 to 2031 [2].

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