Data Science and AI: Differences and How They Work Together

In the past two years, employers have created an astonishing 1.3 million artificial intelligence-related jobs, according to LinkedIn research. AI careers are dominating job listings, with AI engineer emerging as one of LinkedIn’s fastest-growing jobs.

At the same time, data science positions are on the rise, with a projected growth rate of 34% between 2024 and 2034, according to the U.S. Bureau of Labor Statistics (BLS).Are you interested in leading the economy of the future? A master’s degree in computer information systems can prepare you for careers in both data science and AI.

What Is Data Science?

Data science uses algorithms and scientific methods to extract insights from raw data. The field uses various mathematical and analytical approaches, such as statistics and computer engineering, to analyze large amounts of data. 

Modern enterprises collect massive amounts of information about their customers and clients. Data science professionals turn that data into actionable insights that impact how organizations operate, interact with their customers, and evolve. For example, an e-commerce retailer could analyze sales data to identify customers who are most likely to try newly released products, then target marketing specifically to those users when a new product launches. 

What Is AI?

AI is an umbrella term that describes the technology that enables computer systems to simulate humanlike thinking, such as problem-solving and reasoning. While sometimes conflated with machine learning, machine learning is just one subset of AI.

Machine learning refers to training algorithms to recognize patterns in data. Computer systems rely on these algorithms to process large datasets.

AI also uses deep learning models with complex neural networks working together to process information. Deep learning models perform advanced mathematical operations, using small data points to solve greater problems.

Different forms of AI, such as smart assistants and generative AI applications, use multiple machine learning and deep learning models at once. 

How Data Science and AI Work Together

Data science and AI overlap in one key way: the central role of data. Information powers both fields. 

Data scientists rely on well-maintained datasets in robust computer systems to extract accurate, actionable insights. AI also requires accurate data to work properly—generative AI models or smart assistants fed inaccurate data are likely to deliver incorrect results or perform tasks incorrectly. 

Data Science and AI: Key Differences

Both data science and AI rely on large volumes of data, but how that data is examined and applied is a critical difference between the two. 

Goals and Focuses

The goal of data science is to better understand data, recognizing patterns and unearthing insights that humans might miss. Data science professionals use raw data to develop knowledge that already exists in the data, waiting to be uncovered.

By contrast, the goal of AI is to use complex datasets and algorithms to perform tasks that usually require human intelligence—automating processes; solving problems; and creating new artifacts such as code, documents, or images. 

For example, while data science can analyze historical trends to predict next-quarter sales, AI can generate personalized marketing materials to engage key potential customers.

Data science primarily approaches data in four ways:

• Descriptive analysis summarizes the current state within a dataset, often through the use of data visualizations or generated descriptions.
• Diagnostic analysis examines why an outcome occurred, such as why sales were particularly low or high at a specific time.
• Predictive analysis uses historical data to forecast future events.
• Prescriptive analysis goes beyond predictive analysis to suggest the best course of action for a desired result.

AI has different focuses and uses, including the following: 

• Neural networks that contain artificial neurons, or nodes, use complex calculations to process information and solve challenging problems.
• Natural language processing (NLP) uses neural networks to understand text data, gathering meaning from human language to create responses.
• Computer vision uses deep learning to gather information from videos and images.
• Speech recognition also relies on deep learning, in this case, to interpret human speech and detect meaning and direction.
• Generative AI uses deep learning and large amounts of data to create new content, such as images, videos, and text.

Career Opportunities

Those working in data science focus on organizing, analyzing, and managing data to extract important knowledge. Careers in this field include the following:

• Data analyst: Data analysts use programming languages to examine data and answer specific questions, such as assessing sales data and sharing findings with stakeholders.
• Computer systems analyst: Computer systems analysts focus on the systems that store, maintain, and organize data to ensure that they meet an organization’s needs.
• Database administrator: Database administrators oversee computer systems’ security and accessibility, restricting access to those who need it.
• Data scientist: Data scientists perform data analysis and develop machine learning models that can make predictions based on data patterns.

AI focuses on creating technologies that automate tasks. AI careers include the following:

• AI consultant: AI consultants help companies implement and develop AI-powered systems.
• AI research scientist: AI research scientists are focused on assessing current AI models and identifying opportunities for improvement.
• Robotics engineer: Robotics engineers create machines that use AI algorithms to perform specialized tasks.
• Machine learning engineer: Machine learning engineers create machine learning models that are used in AI applications.

Advance Your Career in Data Science or AI with NEC

Career paths in both data science and AI show tremendous potential as technology advances. Both fields also need technology professionals who are educated and experienced in computer information systems to ensure that data is properly maintained and governed.

The New England College Master of Science (MS) in Computer Information Systems prepares students to learn how to approach information technology (IT) from a business mindset, allowing them to turn data into actionable, impactful insights. Students can choose from three concentrations: Artificial Intelligence, Data Science, and Health Informatics. Designed to be completed in as little as 18 months, the program teaches students the key skills that employers value.

Develop your skills in data science and AI with NEC. 

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