The Role of AI in Data Science

It's no secret that artificial intelligence (AI) has taken many industries by storm. In fact, according to a Pew Research report, 62.5 percent of adults in the United States now interact with AI at least seven times per week — and a 2025 McKinsey report found that 88% of organizations now report regular AI use in at least one business function.

Data science is no exception to the benefits of AI. Data scientists are finding practical applications for AI technology, allowing them to save significant time and resources.

What Is the Role of AI in Data Science?

AI has many potential applications in data science, ranging from automation to predictive modeling and more.

Automating Data Processing and Cleaning

AI tools can free up data scientists' valuable time by automating previously manual, time-consuming tasks, such as data cleaning and preparation, and data processing. As a result, data scientists may have more time to focus on other essential aspects of their work.

Enhancing Data Analysis and Pattern Recognition

One of the inherent challenges of working with large amounts of data is finding patterns and making sense of it. AI can assist with this through the use of algorithms that can pinpoint potentially meaningful patterns that may be missed by the human eye. 

Powering Predictive and Prescriptive Analytics

Predictive analytics is a common application of AI across many industries, but it is perhaps instrumental in data analytics, where scientists aim to understand what's most likely to happen in the future based on past trends. AI can also be used for prescriptive analytics, or using predictions to help organizations determine the next steps they should take.

Enabling Real-Time Insights

AI tools can also be leveraged for continuous, non-stop data processing. As new data comes in, it can be incorporated into existing analyses and reports in real time — saving data scientists valuable time while ensuring information is always up to date.

Supporting Natural Language and Image Analysis

Today, data scientists may not always have access to organized data — and instead may need to use textual data or other sources of unstructured data. Through AI's natural language processing (NLP) and image analysis tools, it is possible for data scientists to simplify the analysis of this complex type of data in their work.

Driving Model Optimization and Continuous Learning

Finally, the intersection of AI and data science is constantly evolving through driving model optimization and continuous learning. AI models are regularly reworked and retrained to overcome common challenges and improve efficiency, allowing data scientists to play a pivotal role in their future development and application.

Real-World Use Cases of AI in Data Science

Of course, data science is a far-reaching field with applications across numerous industries. Already, though, AI is being used to improve data analysis practices and outcomes across such fields as healthcare, finance, marketing, cybersecurity, and many more.

Healthcare

In healthcare, AI can process large amounts of patient data to predict the likelihood of future health challenges or complications. Likewise, AI can analyze patients' medical charts to recommend more personalized treatment plans that could improve patient outcomes.

Finance

In the finance industry, AI can be especially useful when it comes to forecasting. Specifically, AI models can be used to create financial forecasts and models based on historical data, while incorporating real-time information to improve accuracy. This reporting can then be used to improve investment decision-making.

Marketing

In the dynamic field of marketing, AI is now being used for predictive analytics purposes. By processing large amounts of marketing data, AI tools can now generate reports and provide valuable insights on things like customer demand and future sales.

Supply Chain and Manufacturing

Many supply chain and manufacturing operations are now using AI to provide real-time reporting on the availability of certain materials/resources. When combined with demand forecasting, this information can help businesses optimize their inventory to keep up with demand while avoiding waste.

Cybersecurity

In a world where cyber threats lurk around every corner of the web, AI has enormous potential to drive positive change. Today, for example, AI can automatically scan large amounts of network traffic and similar data to detect anomalies that could signal a potential threat. This could help stop attacks before they happen.

Types of AI Used in Data Science

Within the realm of artificial intelligence, several types are often applied to data science, each with a different purpose.

Machine Learning (ML)

Machine learning refers to AI algorithms that automatically learn from data without explicit programming or intervention. Data scientists rely on ML not just to train algorithms, but for predictive modeling that can drive decision-making as well.

Deep Learning

On an even more complex level, deep learning serves as a subset of machine learning. Through deep learning, layers of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can process and learn from even the largest imaginable data sets.

Natural Language Processing (NLP)

Through AI's natural language processing models, computers can make sense of and extract insights from human language. Using NLP, data scientists can gain valuable insights from less conventional data sources, such as written customer reviews or social media posts.

Computer Vision

Similar to NLP, computer vision is a component of AI that empowers computers to make sense of and analyze visual data components, such as images and videos. 

Benefits of AI in Data Science

Since major advancements in AI just a few years ago, data scientists have already begun to reap the benefits of its use in their work.

Enhanced Efficiency

When data scientists use AI to tackle time-consuming, manual tasks, they free up more of their own valuable time for high-level strategic work. This, in turn, improves efficiency and productivity.

Improved Accuracy

Because AI isn't subject to dreaded human error, many data scientists find that they enjoy improved accuracy in their reporting and analysis. Likewise, because AI can pinpoint subtle patterns that the human eye often misses, this can also lead to more accurate results and predictions.

Scalable Analysis

Because AI tools can automatically incorporate real-time data into existing algorithms as it becomes available, data scientists and organizations can enjoy scalable analysis. This could enable more rapid adaptation to market changes and streamlined workflows.

Smarter Decision-Making

When organizations use AI for data analysis, predictive modeling, and forecasting, they're often able to make better-informed decisions that can mitigate unnecessary risks while supporting long-term strategic goals.

Career Paths at the AI–Data Science Intersection

If you're fascinated by the practical applications of AI and data science, one of these career paths could be right for you.

Data Analyst

Data analysts work with large amounts of data, often leveraging AI tools to prepare data for processing. From there, they're also responsible for analyzing data to extract valuable insights and predict future trends, informing organizational decision-making.

Data Scientist

Like data analysts, data scientists are responsible for working with data to extract meaningful insights, often on behalf of a company or organization. Typically, however, data scientists are more focused on the predictive modeling side of the equation, helping organizations figure out what could happen in the future.

Machine Learning Engineer

These professionals build, deploy, and maintain machine learning algorithms on behalf of organizations, taking measures to test and iterate on these models for long-term improvement.

Data Engineer

Data engineers are an important piece of the puzzle, as they are responsible for designing and maintaining the systems and databases that store data. This, in turn, ensures that data is readily accessible for engineers, data scientists, and data analysts when they need it.

Business Intelligence Analyst

BI analysts are essentially data analysts who work for a specific organization and use their data analyst skills to help stakeholders identify trends and inform long-term strategic planning.

AI Specialist or AI Consultant

These professionals meet with different businesses and organizations to make personalized recommendations regarding how AI tools could be used to improve their processes and achieve goals.

The Future of AI in Data Science

As AI technology continues to advance, its applications in data science are likely to evolve even further. In the future, it is predicted that innovations like multimodal AI will make it possible to better process and analyze data from a wide range of sources — including text, images, videos, and even voices.

Prepare for a Career in Data Science and AI with Champlain College Online

Regardless of how you feel about AI, it's not going anywhere anytime soon. In fact, as the technology continues to evolve, it is only likely to become more advanced and more widely adopted. 

Whether you're already working in a data-heavy field or have plans to break into data science, having a strong grasp of how to use the latest AI tools can make all the difference in your work. At Champlain College Online, our Bachelor's in Data Science and Artificial Intelligence program is designed to prepare students to lead fields in machine learning and data analytics with dedicated coursework in AI tools, algorithm development, computational foundations, and more. 

And because this program is offered in a 100% online format, you can enjoy the flexibility and convenience you need while building your skill set. Get in touch to learn more about CCO's Data Science and AI program, or start your application now.