Data Science vs. Artificial Intelligence (AI): Key Comparisons

Data science and artificial intelligence (AI) are two complementary technologies in the modern tech environment. Data science organizes and cracks the large, often variably structured datasets that often feed AI algorithms. AI tools can also be used in the data science process.

As VentureBeat has explained, “Data science is the application of scientific techniques and mathematics in making business decisions. More specifically, it has become known for the processes of data mining, machine learning (ML), and artificial intelligence (AI) increasingly being applied to very large (“large”) and often heterogeneous sets of semi-structured and unstructured data sets.

And while AI “aims to train the technology to accurately imitate or – in some cases – exceed the capabilities of humans,” these days it relies on somewhat brute force “learning” from very large data sets organized by a data scientist or similar professional. and written or guided algorithms for application to a relatively limited application.

For example, a data scientist may be responsible for integrating real-time data feeds about the economic and physical environment, and social media consumer confidence feeds, with operational demand, supply, delivery, and production data. A data scientist can also write and use machine learning (ML) AI algorithms to optimize and predict the business response to these various factors.

What is Data Science?

Data science deals with large amounts of data, combining tools such as mathematics and statistics with modern techniques such as specialized programming, advanced analytics and ML to discover patterns and derive valuable information that drives decision making, strategic planning and other processes.

The discipline applies ML to numbers, images, audio, video, text, etc. to produce predictive and prescriptive results.

The data science lifecycle includes several stages:

Data acquisition: This includes the collection of raw, structured and unstructured data, including customer data, log files, video, audio, photos, the internet of things (IoT), social media and much more. The data can be extracted from a variety of relevant sources using various methods such as web scraping, manual input, and real-time data streamed from systems and devices.

Data processing and storage: This includes cleaning, transforming, and sorting the data using extract, transform, load (ETL) models or other data integration methods. Data management teams set up storage processes and structures taking into account the different data formats available. The data is prepared to ensure quality data is loaded into data lakes, data warehouses, or other repositories for use in analytics, ML, and deep learning models.

Data analysis: Here, data scientists examine the prepared data for patterns, ranges, value distributions, and biases to determine its relevance to predictive analytics and ML. The generated model can be responsible for providing accurate insights that facilitate efficient business decisions to achieve scalability.

Communication: In this final stage, data visualization tools are used to present analysis results in the form of graphs, charts, reports, and other readable formats that aid understanding. Understanding these analyzes promotes business intelligence.

What is Artificial Intelligence?

AI is a branch of computer science that deals with the simulation of human intelligence processes by smart machines programmed to think like humans and mimic their actions.

This includes not only ML, but also machine sensing functionality such as sight, sound, touch, and other sensing capabilities of and beyond human capabilities. Applications of AI systems include ML, speech recognition, natural language processing (NLP) and machine vision, for example.

AI programming involves three cognitive skills: learning, reasoning, and self-correction.

Learning: This part of AI programming focuses on getting data and creating algorithms or rules that are used to extract actionable insights from the data. The rules are straight forward, with step-by-step directions for performing specific tasks.

Reasoning: This aspect of AI programming is concerned with choosing the right algorithm for a certain predetermined result.

Self-correction: This aspect of AI programming is constantly refining and developing existing algorithms to ensure that their results are as accurate as possible.

Artificial intelligence is also roughly divided into weak AI and strong AI.

Weak AI: This is also called narrow AI or artificial narrow intelligence (ANI). This type of AI is trained to perform specific tasks. The AI ​​developed so far falls under this category and is driving the development of applications such as digital assistants, such as Siri and Alexa, and autonomous vehicles.

Strong AI: This includes Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). AGI would be a machine with the same intelligence as humans, with self-awareness and the awareness to solve problems, learn and plan for the future. ASI aims to surpass the intelligence and ability of the human brain. Strong AI is still entirely theoretical and will probably only be achieved through advanced mimicry or some sort of biological fusion.

Data science vs artificial intelligence: main similarities and differences

The similarities and differences between data science and AI are best understood through clarity on two key concepts:

Common interdependence: Data science typically uses AI in its operations, and vice versa, which is why the concepts are often used interchangeably. However, the assumption that they are the same is incorrect because data science does not represent artificial intelligence.

Basic definition: Modern data science involves collecting, organizing, and predictive or prescriptive ML-based analysis of data, while AI includes those analytics or advanced machine perception capabilities that can provide data for an AI system.

1. Method: AI involves high-level complex processing aimed at predicting future events using a predictive model; data science includes data pre-processing, analysis, visualization and prediction.
2. Techniques: AI uses machine learning techniques by applying computer algorithms; data science uses data analysis tools and methods of statistics and mathematics to accomplish tasks.
3. Objectively: The primary goal of artificial intelligence is to achieve automation and achieve independent operation, making human input unnecessary. But for data science it is to find the hidden patterns in the data.
4. Models: Artificial intelligence models are designed to simulate human understanding and cognition. In data science, models are built to produce statistical insights needed for decision making.