Artificial Intelligence vs Data Science

Cole Ingraham

Written By Cole Ingraham

Jess Feldman

Edited By Jess Feldman

Last updated on December 4, 2023

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With all the amazing advancements that have been happening over the past months and years, it may seem that artificial intelligence appeared out of nowhere. In fact, AI has been an area of research and development for decades, and there are many facets to it beyond large language models and ChatGPT. AI itself is a difficult concept to pin down, as the term has been used to describe many different things over the years, sometimes correctly, other times not so much. Since there are so many topics and terms that get thrown around all the time, it is a good idea to distinguish between the plethora of terms that get thrown around seemingly interchangeably.

🧑🏽‍💻 Ready to dive deeper into all things data science, AI, and machine learning? Enroll in an immersive bootcamp or short course at NYC Data Science Academy!

AI vs Data Science

It is important to remember that all of the seemingly magical technology we have seen spring onto the scene recently has its roots in statistics and probability. Both seek to define and understand fundamental concepts such as likelihood, confidence, and generally help us reason about uncertainty and make predictions. One of the primary goals of statistics is to give us a quantitative way to measure if something has any impact on an outcome. This allows us to, for instance, conduct tests to determine if our advertising is actually driving increased sales, which in turn informs whether we should continue spending money on it versus other initiatives. Probability gives us a way to measure how confident we can be regarding uncertain or nondeterministic outcomes. The large language models (LLMs) we are all familiar with rely on probability to choose the next word to use, given all of the previous text.

Artificial intelligence (AI) is a subfield of statistics that aims to allow machines to determine their actions based on observations. 

AI is an application of statistics and probability similar to how physics is an application of mathematics. There are many subfields of AI, but the currently most talked about being machine learning (ML). 

  • The main distinction between AI and ML is that the goal of ML is to allow machines to learn their behavior directly from data, rather than from hand written logic. There are many subsets of ML that employ various methods to learn from data, the currently most in vogue being artificial neural networks.

Data Science is very closely related to statistics, probability, and AI. It can be thought of as the application of these disciplines to solving real world problems. 

There are typically two areas that a data scientist focuses on: data analytics and ML. 

  • Analytics is about transforming raw data into a form that makes it easier for people to gain insights into what is going on and understand trends, typically focusing on generating reports and dashboards. 
  • Their ML work would relate more to applying data to solve problems or create functionality. Examples of this would be creating recommendation systems or automating repetitive tasks that require some level of reasoning.

The Similarities Between AI and Data Science

AI and Data Science are very closely related in practice. Many, but not all, data scientists work on AI as a major part of their job. As the availability and amount of data increases and as computers and algorithms become more powerful, more and more data science positions are focused on AI/ML, as there is increased interest in leveraging these resources to streamline processes and create new products. This is true for almost every field. Retailers, social media, and other platforms rely on suggesting what their customers should interact with next in an increasingly personalized way. Quality control systems can be, at least partially, automated in order to both speed up the task and increase the accuracy of detecting defects. Tasks that require searching through and reasoning about a large set of possibilities, such as drug discovery in pharmaceuticals, can use AI to significantly reduce the time and money spent by weeding out dead ends.

The Differences Between AI and Data Science

Despite their interrelatedness, data science and AI are not exactly the same thing. Outside of industry, there is significant research into AI in academia such as developing new modeling techniques or algorithms. In industry as much if not more effort goes into supporting and deploying models as it does designing them, which is typically called ML engineering, MLOps, or other similar titles. Data scientists are usually focused on understanding data, applying existing techniques to build a model, and handing the result off to others to put it into production.

Will AI Replace Tech Jobs?

You cannot talk about AI without someone talking about all the jobs it will replace. Will AI replace writers? Are software developers obsolete? You can ask the same about data scientists. The answer is almost always the same: no, but their job will certainly be changed. It is important to remember that, even with systems as impressive as large language models, current AI and ML methods can only reason about data they have been trained on. That means that while they can excel in situations they have been exposed to, they cannot innovate, and their ability to adapt to new situations can be limited. The world is constantly changing, new ideas, opportunities, and challenges are always appearing, and thus there will always be a need for people who understand and can perform these tasks.

Learning AI & Data Science at NYC Data Science Academy

For the past 10 years, we at NYC Data Science Academy have been preparing our students for jobs in data science across many industries and levels, from junior analysts to directors and vice presidents, and AI is a vital part of their training. Many of our students' capstone projects involve AI, and some of them end up being the basis for starting new companies. One recent project used computer vision to detect patterns in defects that occur in silicon wafers manufacturing, which can be used to help determine the root cause by allowing many different machines and teams data to be compared in a consistent manner. Others have used real estate data to identify factors which impact the value of a property the most when looking at different regions.

NYC Data Science Academy made ChatGPT available for all of our alumni in February 2022 through a partnership with OpenAI before they made headlines in November 2023! With the rise of large language models, NYC Data Science Academy has been preparing our students to understand what these tools are, what they are not, and how they can be used in a variety of ways and situations. 

Many of our live learning sessions focus on things like semantic search and retrieval augmented generation, which are techniques for allowing language models to access data they otherwise would not be able to, such as private documents. We have also developed intensive courses in Generative AI for Everyone, Generative AI for Finance, and Generative AI for Marketing, with others in the works. These classes are designed to give you the tools you need to apply things like language models, text to image, and others to real world problems. For those interested in how to deploy and maintain models, we offer training in MLOps as well. We focus on preparing our students regardless of which part of the pipeline they end up in. Our students come from a wide range of backgrounds, some looking to change careers, others wanting to take their data science skills to the next level.

About The Author

Cole Ingraham

Cole Ingraham

Dr. Cole Ingraham is a musician and composer turned software engineer and data scientist who currently teaches at NYC Data Science Academy as Lead AI Instructor.

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