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

While data science uses a combination of statistical tools, methods, and technology to extract insights from data, artificial intelligence (AI) goes beyond this process by utilizing data to solve cognitive problems related to human intelligence, such as pattern recognition and learning. Data science and artificial intelligence play an important role in many modern workplaces. With the help of vast amounts of data, data science and AI lead to data-driven decision-making and support several other tasks. 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.

Data Science

Data science is the process of extracting valuable insights from data and using these insights to make informed decisions and develop strategies. It is 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?

You can use data science to help address many issues as long as you have access to the necessary data and the skills and knowledge to handle the data. 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, such as informing and influencing the decision-making 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 often seen in data science include poor data quality and a lack of access to data. Data can be inaccurate and disorganized, making it difficult 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. This process occurs through a subcategory of AI referred to as machine learning. In machine learning, algorithms receive massive amounts of data, and the algorithm then learns how to categorize the information by identifying patterns. 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.

What is artificial intelligence used for?

Artificial intelligence is likely already impacting your everyday life through the technology you use, such as chatbots and social media websites on your smartphone. 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 stands to positively impact the economy in terms of growth and present new opportunities. Rather than replacing workers entirely, AI will help improve worker productivity and free employees up to work on important tasks rather than simple, repetitive job duties. Ultimately, AI means businesses can improve 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 make up for these costs later on, it’s still a significant investment. The use of AI also raises ethical concerns at times, including plagiarism, data privacy, and deepfakes that can be used to spread misinformation.

Data Science vs AI: Other things to consider

Data science and artificial intelligence do have some things in common. Both fields depend on large amounts of data, as well as algorithms. In fact, you can use data science algorithms within machine learning. If you’re considering data science versus AI for a career, you can’t go wrong with either, as they both offer outstanding job outlooks going into the future. The US Bureau of Labor Statistics expects the demand for data scientists to increase 36 percent from 2023 to 2033 [1], while the artificial intelligence market is projected to grow 26.95 percent annually from 2025 to 2031 [2].

Discover more about comparing data science and AI on Coursera.

Data science and artificial intelligence are two separate fields that utilize data to inform decisions and solve problems. On Coursera, you can find highly rated courses to learn more about data science and AI. What is Data Science from IBM will discuss use cases for data science, and you can utilize this information to discover career opportunities, and what it will take to get you there. 

Consider the Generative AI for Data Analysts Specialization and apply your skills to recognize real-world generative AI uses and identify popular generative AI models and tools. You can gain knowledge of generative AI prompt engineering concepts, examples, common tools, and techniques needed to create effective, impactful prompts.