What’s the difference between data science and artificial intelligence?
Both data science and artificial intelligence (AI) are umbrella terms for methods and techniques related to understanding and using digital data. Modern organizations collect information from a range of online and physical systems on every aspect of human life. We have text, audio, video, and image data available in large quantities. Data science combines statistical tools, methods, and technology to generate meaning from data. Artificial Intelligence takes this one step further and uses the data to solve cognitive problems commonly associated with human intelligence, such as learning, pattern recognition, and human-like expression. It is a collection of complex algorithms that "learn" as they go, becoming better at solving problems over time.
Similarities between data science and artificial intelligence
Both AI and data science include tools, techniques, and algorithms to analyze and utilize large volumes of data. The following are some similarities.
Predictive applications
Both artificial intelligence and data science technologies make predictions based on new data, as a result of applying models and methods learned in analyzing previous data. For example, predicting future monthly umbrella sales based on previous years’ data is an example of time series data analysis within data science.
Similarly, a self-driving car is an example of a predictive artificial intelligence system. When a self-driving car is on the road, it calculates the distance to the car in front and the speed of both cars. It keeps its speed at a rate that would avoid a crash, based on predicting the sudden braking of the car in front.
Data quality requirements
Both AI and data science technologies give less accurate results if the training data is inconsistent, biased, or incomplete. For example, data science and AI algorithms may:
- Filter out new data if it is completely new and not in their original dataset.
- Prioritize specific attributes in the dataset over all others if the input data lacked variation.
- Create non-existent or fictional information because the input data was false.
Machine learning
Machine learning (ML) is considered a sub-type of both data science and AI. This means all ML models are considered data science models and all ML algorithms are also considered AI algorithms. There is a common misconception that all AI uses ML, but this is not the case. ML is not always required in complex AI solutions. Similarly, not all data science solutions involve ML.
Key differences: data science compared with artificial intelligence
Data science involves analyzing data to determine underlying patterns and points of interest for making predictions. Applied data science takes the models and methods used in data analysis and applies these to new data in real-world situations to give probabilistic outputs. In contrast, AI uses applied data science techniques and other algorithms to compose and run complex machine-based systems that approximate human intelligence.
Data science can also be used in applications other than AI and computer science.
Goals
The goal of data science is to apply existing statistical and computational models and methods to understand points of interest or patterns in gathered data. Outcomes are pre-determined and easy to define from the start. For example, you can use data to predict future sales or identify when a piece of machinery is ready for repair.
The goal of AI is to use computers to produce an outcome from complex new data that is indistinguishable from intelligent human reasoning. Outcomes are generic and hard to define—for example, generating creative text or generating images from text. The details of the problem set are too large to define accurately and the AI system interprets the problem by itself.
Scope
Data science has a smaller scope as the outcome is pre-determined. The process begins by identifying questions that can be answered from data. The scope includes:
- Data collection and preprocessing.
- Applying appropriate models and algorithms to the data to answer these questions.
- Interpreting the results.
In contrast, AI has a much wider scope and steps vary based on the problem being solved. The process begins by identifying a labor-intensive manual task or complex reasoning task that humans perform successfully and we want the machine to replicate. The scope may include:
- Exploratory data analysis.
- Dividing the task into algorithmic components to form a system.
- Gathering test data to review and refine the suitability of the logical flow and complexity of the system.
- Testing the system.
Methods
Data science has a large range of techniques for modeling data. Selecting the correct technique is dependent on the data and the question being posed. These include linear regression, logistic regression, anomaly detection, binary classification, k-means clustering, principal component analysis, and many more. Incorrectly applied statistical analysis will produce unexpected results.
AI applications typically rely on complex, pre-built, productized components. These may include facial recognition, natural language processing, reinforcement learning, knowledge graphs, generative artificial intelligence (generative AI), and many more.
Applications: data science compared with artificial intelligence
Data science can be applied anywhere there is enough quality data and a model to assist in answering a particular question. Applications include:
- Sales demand forecasting.
- Fraud detection.
- Sporting odds.
- Risk assessment.
- Energy consumption forecasting.
- Revenue optimization.
- Candidate screening processes.
AI applications are nearly endless. Popular applications include:
- Robotic production lines.
- Chatbots.
- Biometrics recognition systems.
- Medical imaging analysis.
- Predictive maintenance.
- Town planning.
- Marketing personalization.
Careers: data science compared with artificial intelligence
The main focus for a data scientist is typically technical, working deep in the data. Data scientists may work on the collection and processing of data, choosing the right models for the data, and interpreting results to make recommendations. Work may occur within specific software or systems, or even building systems themselves.
Types of roles
Data science jobs include data scientist, data analyst, data engineer, machine learning engineer, research scientist, data visualization specialist, field-specific analyst roles, and more. AI also encompasses all of these roles. However, as the scope of the field is so broad, there are many additional associated roles and areas of job focus such as software developer, product manager, marketing specialist, AI tester, AI engineer, and more.
Skillset
Data scientists have skills in the practical application of statistical and algorithmic methods to qualify and analyze data to find relevant insights. Data scientists require a background in statistical mathematics and computer science and proficiency in applicable tools.
Depending on the role within AI, the skillset required may be more technical or soft skills-based. In some roles, there may be no technical experience required. For example, an AI software developer would need practical knowledge of relevant programming languages, libraries, and tools. However, an AI tester for a generative AI tool would require linguistic skills, creative thinking, and understanding how users should interact with the system.
Career progression
As data science tools and workflows become more automated and productized, the number of pure data science roles decreases. Data science professionals seeking pure data science roles tend towards academic and cutting-edge applications. Analyst roles where the data scientist owns the operation of the tools remain relevant. From a junior role, data scientists gain more senior positions, move to people or project management, and even progress to chief data officer.
Depending on the focus of the AI role itself, career progression can be similarly expected. You can progress to chief technology officer, chief marketing officer, chief product officer, and so on. Thinking critically about which jobs will be automated over the next ten years can help future-proof a career direction.
Summary of differences: data science compared with artificial intelligence
| Data science |
Artificial intelligence |
|
| What is it? |
The use of statistical and algorithmic modeling to obtain insights from data. |
A broad-spectrum term for machine-based applications that mimic human intelligence. |
| Best suited for |
Answering a question from a set of data. |
Completing a complex human task with efficiency. |
| Methods |
Linear regression, logistic regression, anomaly detection, binary classification, k-means clustering, principal component analysis, and more. |
Facial recognition, natural language processing, reinforcement learning, knowledge graphs, generative AI, and more. |
| Scope |
Pre-defined questions that can be answered from the data. |
Broad and difficult to define—task-based. |
| Implementation |
Uses a range of different tools to capture, clean, model, analyze, and report on data. |
Task-dependent. Typically relies on complex, pre-built, productized components. |
How AWS can help with your data science and artificial intelligence requirements?
AWS has a full range of data science and AI products and services designed to help you strengthen and grow your organizational and individual data analytics and intelligence.
This includes API-based data science and AI models for structured and unstructured data and fully-managed environments that provide for the end-to-end creation and deployment of data science and AI solutions.
- Amazon SageMaker Studio is an integrated development environment (IDE) that includes a purpose-built tool stack for developing data science and ML solutions.
- Amazon Lex helps you build your own chatbots with conversational AI.
- Amazon Rekognition offers pre-trained and customizable computer vision (CV) capabilities to extract information and insights from your images and videos.
- Amazon Comprehend helps you derive and understand valuable insights from text within documents.
- Amazon Personalize leverages ML to help you personalize the customer experience.
- Amazon Forecast helps perform time-series forecasting.
- Amazon Fraud Detector helps you build, deploy, and manage fraud detection models.
AWS also offers a growing list of world-class generative AI solutions that can create new content and ideas, including conversations, stories, images, videos, and music. Generative AI solutions include:
- Amazon Bedrock helps organizations build and scale generative AI solutions.
- AWS Trainium helps train generative AI models faster.
- Amazon Q Developer is a generative AI powered assistant for software development.
Get started with data science and artificial intelligence on AWS by creating an account today.
