Artificial Intelligence vs. Data Science: What Are the Differences?

Artificial intelligence (AI) and data science are two interconnected fields pivotal to modern IT. Data science revolves around identifying patterns in data, while AI technologies primarily focus on learning from data to automate various tasks and processes.

While there is some overlap between the two, using AI and data science as interchangeable terms would be a mistake. If you have any confusion about what these fields have in common and what sets them apart, this is the post for you.

This article offers an in-depth comparison of artificial intelligence and data science. Jump in to learn how these fields differ, how they complement each other, and what your business can gain from these technologies.

What Is Artificial Intelligence?

Artificial intelligence (AI) is a branch of computer science focused on creating systems that perform tasks commonly associated with human intelligence. AI-based systems rely on algorithms that can analyze and interpret data to make decisions and learn from experience. There are two broad types of AI:

• Narrow (weak) AI. These AI systems exclusively perform a single or a small set of specific tasks within a limited context. Narrow AI cannot perform tasks outside its predefined scope.
• General (strong) AI. These systems, which remain a theoretical concept, possess human-like cognitive abilities and can perform a wide range of tasks across various domains. Another common name for this type of AI is artificial general intelligence (AGI).

The development of true AGI is still decades away. However, narrow AI development has reached a point where systems can reliably perform various tasks, such as:

• Recognize images, objects, and other elements in unstructured data.
• Spot patterns and anomalies in data sets.
• Generate derived insights and use them to run predictive analytics.
• Run autonomous systems with minimal or no human involvement.
• Translate text or speech between different languages in real-time.
• Perform sentiment analysis on customer reviews, social media, or feedback.
• Generate creative content like text, images, music, or videos.

Most AI systems rely on machine learning (ML) algorithms that improve over time by analyzing large volumes of training data (often gathered and preprocessed by data scientists). Some AI systems rely on deep learning, which uses artificial neural networks to interpret provided data. AI also includes other approaches, such as rule-based systems and evolutionary algorithms.

What Is Data Science?

Data science is a field that combines various methods to extract meaningful insights from massive volumes of structured or unstructured data.

Data scientists use programming languages (most commonly Python or R) along with machine learning algorithms and statistical models to derive useful information from available data. In general, this data analysis process follows three broad steps:

• Data wrangling. The initial step is to prepare raw data for analysis by cleaning, transforming, and organizing files into a usable format.
• Exploratory Data Analysis (EDA). The EDA phase requires data scientists to use statistical methods to identify patterns, trends, and relationships within data. Many data science teams use AI technologies during EDA.
• Modeling. This step requires teams to build predictive or descriptive models using regression, classification, and clustering techniques. These models analyze data to detect meaningful insights. Like with EDA, AI-based analysis is a common part of the modeling process.

Once data scientists extract insights, they present data in an understandable format through charts, graphs, and dashboards. Stakeholders then go over these findings and choose how best to act upon the newly found information. Data science helps businesses find data-based answers to questions like:

• Why is our customer churn rate up in recent weeks?
• What patterns can we identify in customer behavior?
• What factors contribute most significantly to a particular business outcome?
• Which market segments are most likely to respond to a new product or service?
• How can we improve operational efficiency based on existing data?

Data scientists often work with data engineers who help collect and wrangle data from multiple sources. Scientists also commonly collaborate with business analysts who help put available data in the correct business context.

Artificial Intelligence vs. Data Science: What Are the Differences?

The table below offers a high-level overview of the differences between artificial intelligence and data science:

Point of Comparison	Artificial Intelligence	Data Science
Definition	A branch of computer science focused on systems that mimic human intelligence.	An IT field focused on extracting meaningful information from raw data.
Main Objective	Perform tasks and make decisions without human involvement.	Extract insights and provide data-driven recommendations for human decision-makers.
Data Usage	Utilizes labeled or unlabeled data to train algorithms.	Primarily uses cleaned and structured data for analysis.
Dependence on Data	Requires large and diverse data sets for effective training and fine-tuning.	Can work with smaller data sets if the analysis is well-structured.
Level of Automation	High (aims to replace human involvement).	Moderate (provides insights and recommendations for human decision-making).
Go-To Tools and Frameworks	TensorFlow, PyTorch, OpenAI Gym, Keras, Hugging Face.	Pandas, Tableau, Scikit-learn, Jupyter Notebooks, Power BI.
Dependency on Machine Learning	Heavily reliant on machine learning techniques.	May use machine learning as a tool, but ML is not always necessary.
Output	Predictions, classifications, or autonomous actions.	Insights, trends, and actionable visualizations.
Required Skills	Advanced programming, mathematics, machine learning, and neural network expertise.	Statistical analysis, data manipulation, and visualization skills.
Degree of Complexity	Complex due to the focus on dynamic learning systems and generalization.	Can range from simple statistical analysis to complex predictive modeling.
Usual Use Cases	Chatbots, recommendation systems, autonomous vehicles, fraud detection, voice assistants.	Trend analysis, predictive modeling, marketing analytics, customer segmentation, business reports.

The next few sections provide an in-depth look at the most notable differences between artificial intelligence and data science.

Main Objectives

Artificial intelligence and data science differ fundamentally in their goals. AI focuses on creating systems that mimic human cognition and perform tasks without manual intervention. Meanwhile, data science centers on analyzing data to generate insights that guide human decision-making.

AI’s Objective

The main objective of AI is to create systems capable of performing tasks without direct human involvement. In the right environment, these systems can approximate or even surpass human capabilities in terms of speed and precision. For example, AI can analyze vast amounts of data to diagnose diseases or detect fraudulent transactions much faster than any human specialist.

AI systems learn from data and past experiences to refine performance over time without explicit programming. AI’s ability to evolve through continuous learning makes it indispensable for addressing challenges in dynamic environments (e.g., supply chain optimization, autonomous vehicle navigation, disaster response management).

Data Science’s Objective

Data science’s main objective is to systematically explore and analyze data to derive meaningful insights. This field aims to help organizations make informed, data-driven decisions by:

• Identifying trends within the provided data.
• Uncovering correlations between data points.
• Delivering actionable intelligence.

For example, a data scientist working in healthcare might analyze patient records to uncover trends in disease development. The hospital decision-makers could then use these insights to allocate resources more effectively.

Data science primarily focuses on understanding past and present data to answer specific questions. This field helps identify trends or anomalies in data sets and transform raw data into useful information. The focus is on supporting human experts rather than automating decisions entirely.

Scope

The scope of AI is broad and encompasses various systems that learn, adapt, and act autonomously. Data science, by contrast, has a much narrower scope that focuses only on the extraction of knowledge and insights from available data.

AI’s Scope

AI encompasses a wide range of fields and applications, aiming to replicate cognitive abilities and even go beyond human limits in some areas. AI’s scope includes:

• Machine learning and deep learning algorithms that enable systems to learn and improve over time.
• Natural Language Processing (NLP) applications that understand, generate, and translate human language.
• Computer vision systems that can recognize and interpret images and videos.
• Intelligent machines that perform physical tasks in industries like manufacturing, healthcare, and exploration.
• AI models capable of analyzing scenarios and making autonomous decisions in fields like finance, healthcare, and logistics.
• Generative AI models like GANs and transformers that create original content.

The broad scope of AI makes this technology applicable across most industries, which you can learn about in our article on notable examples of AI. Its ultimate scope even includes speculative goals like creating strong AI that can perform any intellectual task a human can do.

Data Science’s Scope

The scope of data science is data-focused and revolves around processes that transform raw data into actionable insights. Its scope covers:

• Gathering and organizing large data sets.
• Using statistical methods to uncover patterns within data.
• Creating models to forecast trends and outcomes.

While applicable across a broad range of use cases and highly valuable, the scope of data science is limited compared to AI. Its scope remains grounded in supporting decision-making with data-driven insights.

Data Usage

AI developers use large amounts of data to train systems for automation and intelligent decision-making. On the other hand, data scientists use data to extract meaningful insights for human interpretation.

AI’s Data Usage

AI systems require vast amounts of training data. Developers can feed two types of data into an AI model:

• Labeled data. In a labeled data set, each data point has a predefined tag or category. These labels make it easier for algorithms to learn the relationship between inputs and outputs. For example, a collection of pet photos where each image of a cat has a “cat” label would be a labeled data set.
• Unlabeled data. Unlabeled data sets do not have any tags or categories. Instead of relying on explicit categorization, models must learn by autonomously analyzing patterns and relationships within provided data.

Labeled data is the go-to choice for supervised learning, while unlabeled data enables unsupervised learning. Learn the difference between these two strategies in our supervised vs. unsupervised learning article.

AI models learn from historical data to perform classification, regression, or generation tasks. For example, you can train an AI-powered fraud detection system on a labeled data set of past fraudulent and legitimate transactions. Once trained, the model can analyze real-time transaction data to flag anomalies.

Data Science’s Data Usage

Data scientists systematically use structured, semi-structured, and unstructured data to uncover trends and actionable insights. For instance, a data scientist working in e-commerce might analyze sales data to identify seasonal trends. Business owners then use that data to optimize inventory and marketing strategies.

While AI systems require vast amounts of data to train algorithms, data science often provides the techniques to preprocess and analyze this data. For that reason, many AI projects require developers to apply data science processes before they start training models.

Tools and Techniques

AI and data science employ different tools and techniques. AI relies on specialized frameworks and algorithms for building intelligent systems. Data science focuses on statistical methods and general-purpose tools for analyzing and visualizing data.

AI’s Tools and Techniques

AI tools and techniques enable developers to train models that mimic human cognition and automate tasks. Here’s what you’re likely to find in an AI developer’s toolkit:

• ML libraries. Machine learning libraries like TensorFlow, PyTorch, and Scikit-learn speed up and simplify the training and deployment of ML models.
• Deep learning frameworks. AI developers use deep learning frameworks (Keras, MXNet, Caffe, etc.) to create deep nets (DNNs) capable of processing images, text, and other unstructured data.
• Natural Language Processing (NLP) tools. Tools like SpaCy and Hugging Face Transformers enable models to engage in artificial conversations.
• Reinforcement learning tools. Tools like OpenAI Gym enable ML models to learn independently through trial and error.
• Computer vision libraries. Frameworks like OpenCV and Detectron enable facial recognition and object detection.
• Edge AI libraries. Edge computing frameworks like TensorFlow Lite enable the deployment of AI models on resource-constrained hardware.

AI developers also use a variety of workflow orchestration tools. Kubeflow and MLflow stand out in terms of popularity, two tools that help manage the end-to-end lifecycle of AI models.

Data Science’s Tools and Techniques

Data science tools and techniques help extract insights from raw data and communicate findings with non-technical personnel. Common data science tools and techniques include:

• Data manipulation tools for cleaning and organizing data sets (e.g., Pandas or NumPy).
• Visualization libraries for creating detailed charts and graphs (e.g., Matplotlib, Seaborn, Tableau).
• Statistical methods for exploring data relationships (regression, clustering, hypothesis testing, etc.).
• Machine learning algorithms for modeling data trends (decision trees, support vector machines, k-means clustering, etc.).

Output

AI systems generate predictions, automated decisions, or autonomous actions. On the other hand, data science produces visualizations and insight reports.

AI’s Output

AI systems produce a wide range of outputs. Here are the most common ones:

• Predictions that forecast outcomes, such as customer behavior or stock price trends.
• Automated tasks, such as self-driving cars navigating routes or chatbots responding to customer queries.
• Classifications, such as identifying spam emails or diagnosing diseases based on symptoms.
• Optimization recommendations, such as recommending personalized content or optimizing resource allocation.
• Generated content (images, text, videos, music, sound effects, etc.).

While the output does not purposely aim to take humans out of the equation, the AI does not require human involvement to create an output once the model receives instructions.

Data Science’s Output

Data science outputs are descriptive, and they provide insights into past trends, current patterns, or potential opportunities. These outputs empower human decision-makers to act based on the evidence derived from data. Most outputs generated by data scientists are:

• Data visualizations (graphs, heatmaps, dashboards).
• Statistical summaries (averages, variances, confidence intervals).
• Detailed reports.

Data science outputs are often used as input for other processes, including AI model training. For instance, a report on customer segmentation might be a part of the training data set for an AI-based recommendation system.

Required Skills

AI development demands expertise in programming and advanced mathematics. Meanwhile, data scientists must primarily be proficient in statistical analysis and data handling.

AI’s Required Skills

Being an AI developer requires excellent knowledge of a wide range of fields and practices. Here’s an overview of what you would need to be a well-rounded AI developer:

• Proficiency in Python, C++, or Java.
• Knowledge of libraries like TensorFlow or PyTorch.
• Advanced understanding of linear algebra and probability.
• Knowledge of supervised, unsupervised, and reinforcement learning techniques.
• A strong grasp of optimization techniques (such as gradient descent) and model evaluation metrics (like precision, recall, and F1-score).
• Experience with neural networks and architectures like CNNs, RNNs, and transformers.

Being an AI developer also requires high levels of creativity. Developers must often combine technical expertise with creativity to design systems that simulate human intelligence.

Data Science’s Required Skills

Data science requires a diverse skill set for handling and analyzing data, as well as presenting findings to non-technical staff members. Here’s an overview of what you’ll need to be a successful data scientist:

• Proficiency in data cleaning, wrangling, and transformation.
• Familiarity with query languages like SQL for working with databases.
• Ability to perform descriptive and inferential statistical analysis.
• A good grasp of linear algebra, probability, and statistics.
• Experience with time-series analysis.
• Knowledge of tools such as Tableau, Matplotlib, or Power BI to create visual representations.
• Familiarity with regression, clustering, and classification models.
• The ability to explain complex data findings in relatively simple terms.

Artificial Intelligence vs. Data Science: FAQ

Below are answers to some of the most frequently asked questions about the differences between artificial intelligence and data science.

Are There Any Similarities Between Artificial Intelligence and Data Science?

Yes, AI and data science share many commonalities. They integrate knowledge from multiple disciplines (mathematics, statistics, computer science, etc.) and rely on data to achieve their objectives. Both fields also use machine learning algorithms to identify patterns and generate insights, and they often involve the use of the same tools and libraries (e.g., TensorFlow and Scikit-learn).

AI and data science also frequently complement each other. Many high-level AI systems depend on preprocessed data, which is often prepared and analyzed through data science processes. Likewise, many data scientists use AI to speed up and streamline various data science tasks (e.g., data cleaning, feature selection, model building).

Which Is Better: Artificial Intelligence or Data Science?

Neither is inherently better. Whether AI or data science is the right option for a specific project depends on your goals and available data.

AI excels in automation and creating systems that mimic human cognition, making it ideal for systems that require autonomous decision-making. Data science is well-suited for extracting actionable insights and guiding decision-making based on historical data.

Will Artificial Intelligence Replace Data Science?

AI is unlikely to replace data science entirely. AI systems depend on data science for preparing, analyzing, and interpreting data. While AI can automate some aspects of data handling, data scientists bring critical domain knowledge, creativity, and decision-making abilities that AI cannot replicate.

Two Complementary, Future-Proof IT Fields

The use of AI will continue to grow in the coming years, a trend that will create a massive need for well-processed data only data science can deliver. Expect organizations to continue investing heavily in artificial intelligence and data science as they explore the limits of what they can achieve with AI and ML models.