AI in Data Science: Uses, Examples, and Tools to Consider

Artificial intelligence (AI) and data science are two of the hottest fields in today's business world. And, it’s little surprise why: after all, they both have the potential to profoundly improve an organization’s decision-making, operational efficiency, and overall productivity. 

But what is AI's actual role in data science? And how might AI advances be incorporated into existing data workflows? 

In this article, you’ll learn more about the connection between AI and data science and how data professionals leverage AI to do some of their most impactful work. At the end, you’ll explore some common AI tools used by data professionals and find suggested training resources that can help your team make the most of this impactful technology. 

Data science and AI: Definitions and differences

In order to understand the role of AI in data science, it’s important to first understand how each of these fields is defined and how they differ from one another. Below, we dive into these fields and how they relate.  

What is data science? 

Data science is an interdisciplinary field focused on the study of data and the extraction of insights from it. Professionals in the field, such as data scientists and data analysts, use their knowledge of statistics, computer science, machine learning, databases, and information theory – among other things – to collect and analyze data and design experiments around it. 

In the business world, data science is often used for business intelligence purposes, particularly to generate actionable insights and inform decision-making. By analyzing large amounts of data, organizations are able to identify trends and patterns that can reveal important insights about the nature of their business, their customers, and the impact of both internal and external events on their overall performance. 

To do this impactful work, data scientists use various analytical methods. For example, while some projects may only need relatively straightforward statistical analysis, others may require advanced analytics techniques like machine learning and deep learning to be completed. The range of techniques that data professionals can use to do their work ensures that they can effectively conduct diagnostic, descriptive, predictive, and prescriptive analyses for organizations. 

What is AI? 

Artificial intelligence (AI) is an interdisciplinary field focused on developing machines capable of performing complex tasks historically only humans could do, such as identifying trends and patterns, translating texts, and driving cars. Today, much of the AI we interact with is generally some type of machine learning, which uses algorithms trained on data sets to create models capable of performing certain decision-oriented tasks.

Rather than being a novel introduction to the field, forms of AI have been used in data science for quite some time. In fact, data scientists often create and rely on machine learning models to efficiently analyze the vast amounts of data held by their employers. As a result, AI allows data professionals to efficiently provide the insights and guidance that organizations need to stay competitive in the modern marketplace. 

That said, AI is an ever-evolving field, and new advancements are being made at an exponential rate. Generative AI tools like ChatGPT, for instance, represent a milestone in the development and widespread adoption of large language models (LLMs), which is already impacting many fields, including data science, and will continue to do so as the technology becomes more sophisticated and capable. 

Machine learning vs. Data science 

Data science makes extensive use of machine learning to analyze data – big data, in particular – and identify insights. As a result, the relationship between data science and machine learning can often confuse those who don’t work in their respective fields.   

Simply put, data science is an interdisciplinary field that’s all about studying data, so one of the ways that data professionals do that is by using machine learning models. Machine learning, meanwhile, is a specific subset of AI that trains algorithms on data sets in order to create models capable of performing certain, clearly defined tasks. As a result, the two fields are interrelated but also distinct from each other. 

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

Uses of AI in data science 

As noted above, data science extensively uses AI for data analysis purposes. Some common uses include: 

• Efficiently analyze big data sets: Through both their internal processes and external channels, many organizations hold vast stores of data that have the potential to reveal important insights about their customers and organizations. In order to effectively process this information, data professionals often create machine learning models to analyze them for insights. 

• Make predictions and recommendations: Data professionals often use machine learning, deep learning, and neural networks to create predictive models that help stakeholders understand the possible outcomes of different courses of action. 

• Automate data management: Data scientists and analysts spend much of their time cleaning, inputting, and managing data sets held within database management systems just so they can analyze them effectively. AI-integrated data platforms, though, are becoming increasingly capable of performing these tasks automatically so that professionals can spend more of their time actually analyzing data. 

• Support data professionals in their daily tasks: Generative AI is able to quickly produce text and code with simple prompts from users. As a result, data professionals can use this technology to cut down on how long it takes them to write lines of code, create reports, and compose emails. Furthermore, AI can also create the data visualizations needed to communicate key insights to many non-technical stakeholders. 

Examples 

There are countless ways that AI technology is used by data science teams in the real world to help organizations achieve their business goals. These can range from using generative AI to quickly summarize findings for leadership teams to building complex deep learning models capable of providing personalized content to users. 

The precise ways that a data science team may employ AI depends largely on their overall objectives. For example, a team focused on improving an organization’s customer experience may use AI to conduct a sentiment analysis on online reviews and social media posts to identify ways to improve their brand image. A data team focused on improving the efficiency of a supply chain, meanwhile, might use AI to identify where bottlenecks are occurring and recommend improvements. 

Read more: Supply Chain Analytics: What It Is, Why It Matters, and More

What is the impact of generative AI on data science? 

In addition to helping data professionals do common tasks like writing code, building reports, and composing emails, generative AI may also make them more productive. One 2023 study, for example, indicates that skilled knowledge workers who use generative AI have the potential to improve their productivity by as much as 42% when doing certain tasks [1]. 

Not all workers achieved such lofty gains. According to the paper, the most pronounced productivity increases were experienced by workers who had access to generative AI and were provided with instructions on how best to use it. As a result, organizations might want to consider investing in generative AI training to ensure their workforce has the knowledge needed to optimize their productivity with such tools. 

Read more: How To Write ChatGPT Prompts

AI tools for data science 

There are many different AI tools available to data science professionals. Some you might consider exploring include: 

• ChatGPT

• BardAI

• Jupyter Notebook

• TensorFlow 

• Github Copilot 

Build AI skills on Coursera 

AI is quickly changing the way that we work. For a quick, one-hour introduction to generative AI, consider enrolling in Google’s AI Essentials course. Learn what it is, how it’s used, and why it is different from other machine learning methods.

Continue building a foundation in AI with AI for Everyone from DeepLearning.AI, or go a step further in your career with IBM’s Applied AI Professional Certificate. Whether you’re an aspiring developer, technology consultant, or data professional, understanding AI has countless benefits that can give you an edge in your career.