Decoding Data Science vs Artificial Intelligence: Exploring Roles and Realms in Tech

Data science and artificial intelligence are closely connected. Data scientists and AI engineers work together, keep up with the same industry news, and even hang out in the same forums.
So, does that mean a career in AI will be pretty much the same as a career in data science? Well, no, not really. To begin with, working in AI is about creating AI programs—things like ChatGPT or Copilot—while data science is about building predictive and machine learning models. They’re using the same tools, but their goals are fairly different.

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What is artificial intelligence?

When it comes to normal computer programs, we get the results we want by telling the computer exactly what to do. In the field of artificial intelligence, the goal is to create a different kind of computer program that can achieve results without explicit instructions. Instead of being told what to do, it draws on the “knowledge” it gained from consuming huge sets of data to generate solutions.

What is data science?

Data science is about using computers to sort through massive sets of data that are too big and complicated for humans to read themselves. By designing and running different kinds of algorithms and predictive models to find patterns, data scientists can uncover just about any kind of information. Their talents are essential in all sorts of industries from tech to healthcare and government.

Does AI need data science?

Of course! Since AI models need mountains of data to learn from, we need data scientists to curate and prepare the right data for the job. They also help design and choose the most effective algorithms and improve them by tuning their parameters to evaluate and improve their performance.

Does data science need AI?

Yes, AI is an essential tool in data science. We use AI algorithms to search through messy and unpredictable data because they’re much better than standard data-handling algorithms at handling uncertainty and reacting to new situations. If we tried to use non-AI algorithms to do the job, they would stop as soon as they found something that didn’t fit into any of their explicitly programmed scenarios.

Key differences between data science and artificial intelligence

Data science and artificial intelligence use similar technology for different purposes. For data science, the main goal is to discover information for humans to use in their decision-making processes. Information is useful in all industries, so data science is used just about everywhere, from medical and humanitarian fields to finance, government, or retail.

Artificial intelligence, on the other hand, uses data to create powerful computer programs that can handle complex tasks. The “intelligence” part of AI refers to the programs’ ability to adapt to new situations and overcome obstacles. Instead of stopping when something unexpected happens, they’re designed to consider relevant information and find a new solution — just like humans do.

Skills

Working in artificial intelligence requires a lot of specialized skills, and there are high-level data science roles with similar requirements. However, not all data scientists write lots of code or develop new algorithms. There are also roles that focus on using commercial data analytics tools and software to extract information, visualize it, and present it to stakeholders. Jobs like this usually have titles like data analyst or business analyst, and they don’t require the same amount of academic and specialized knowledge.

Common ground between data science and AI

The fields of artificial intelligence and data science can get pretty academic. For a high-level data science role or pretty much any AI role, you need to understand mathematical concepts like calculus, statistics, and linear algebra. You’ll use these skills to design machine learning algorithms or AI systems, along with programming languages like: 

• Python
• R
• Java‍
• JavaScript‍
• C++ 

Because there is so much overlap in the techniques used in data science and AI, and because both fields center around data, the communities are always in close contact. Professionals in both fields need to stay up to date with anything related to big data, and many data scientists stay updated on cutting-edge AI technology (and vice versa).

Artificial intelligence engineers and data scientists often also work together, either as closely collaborating teams or as one big team. AI professionals need data scientists on hand to help them curate data to train their models on, and data scientists need input from AI engineers because they use AI algorithms a lot.

Comparing career prospects in data science and artificial intelligence

Despite their similarities, a career in data analytics can be very different from a career in artificial intelligence. Here are some of the key comparisons:

Salaries for artificial intelligence engineers and data scientists

Generally speaking, salaries are influenced by two main factors: the skills required and the demand for the role. In this case, the advanced skills needed for AI and the high demand for data scientists across all industries effectively balance each other out. This results in salaries that are extremely similar, from entry-level data scientist jobs to senior AI roles.

Artificial intelligence engineers

• Overall pay range: $114,000 - $211,000‍
• Average pay: $151,000‍
• Entry-level: $119,000‍
• Mid-level: $137,000‍
• Senior: $175,000

Data scientists

• Overall pay range: $118,000 - $206,000‍
• Average pay: $155,000‍
• Entry-level: $117,000‍
• Mid-level: $141,000‍
• Senior: $192,000

Key trends in AI and data science for 2024

Things change quickly in the world of AI and data science, so it’s good to read up on what the experts think this year has in store.

• ‍Multimodal AI: We’ll see more generative models specializing in text-to-video and image-to-video tasks, as well as the other way around. This means users will be able to show the AI an image or video and ask questions about it.‍
• Smaller language models: Products like ChatGPT are all about the parameter count, but experts such as Open AI’s Sam Altman think the industry will start focusing on different techniques to improve AI models. Smaller models will also require less GPU power and storage, making them more accessible.‍
• Customizing AI models: Instead of just using the commercial versions of tools like ChatGPT or Copilot, more organizations will start paying for a customized model that’s trained specifically on their data and optimized for their unique tasks.‍
• Shadow AI: Unofficial use of AI could become more of a problem. This includes companies using AI tools without making it clear to customers, and employees using personal AI tools to do their jobs.‍
• Fewer data scientists: Some experts think the jack-of-all-trades data scientist role will be broken down into separate, more focused roles like data engineer, data wrangler, machine learning engineer, and data translator.

What to choose in 2024: Data science or artificial intelligence?

Both data science and AI are looking to be top industries this year, with necessity driving stable demand for data professionals and continued hype for AI fuelling research and development. But the right industry for you depends on your interests and your aspirations. Do you see yourself as more of a business-savvy analyst or a cutting-edge researcher? And — an awkward but necessary question—just how good are you at math? Weighing your strengths and weaknesses will help you pinpoint which field is best for you, and what kind of role within that field will suit you best.

FAQ

What are the differences between data science vs. machine learning vs. AI?

Data science focuses on getting information from data and using that information to make better decisions. AI, on the other hand, is about creating computer programs that work without explicit instructions. Machine learning is a technique that both data scientists and AI engineers use, which involves developing algorithms that enable computers to learn from data.

What skills are required to work in data science and AI?

Both of these fields typically require a foundational knowledge of calculus, statistics, and linear algebra. You also need to learn about computer science and programming languages like Python, R, and Java. If you want to work in a role where you present your findings to people, you’ll also need to know how to use data visualization tools like Power BI or Tableau.

What are some popular tools and frameworks used in data science and AI?

If you’ve already started reading up on data science, you might have seen names like NumPy, Pandas, and Matplotlib. These are popular Python libraries for data science that help people analyze and visualize data. For AI, Scikit-learn, TensorFlow, PyTorch, and Apache Spark are common machine learning frameworks for designing, building, and training AI models.

Can data science be replaced by AI?

AI is already a large part of data science, but we still need humans to make machine learning models and, of course, decide what information to search for. Data scientists run data through many different models to get their final answers, and it’s their human intuition that helps them figure out what kind of test to run next.

What is better data science or artificial intelligence?

There isn’t a set answer to this question, of course. It depends on what your interests are and what you want to get out of your career. Both fields are dependent on data, and it’s hard to imagine a future where data becomes less important — which is why becoming a data professional of any sort is sure to provide good job stability!