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

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

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

And while AI “aims to train technology to accurately mimic or, in some cases, exceed the capabilities of humans,” today it relies on brute force “learning” from data sets. very large ones that have been organized by a data scientist or similar professional. , and algorithms written or guided to apply to a relatively narrow application.

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

What is data science?

Data science deals with large volumes of data, combining tools like math and statistics and modern techniques like specialized programming, advanced analytics, and ML to uncover patterns and gain valuable insights to guide 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 life cycle encompasses multiple stages:

Data acquisition: This involves the collection of raw, structured and unstructured data, including all customer data, log files, video, audio, images, the internet of things (IoT), social networks and much more. Data can be extracted from a myriad of relevant sources using different methods, such as web scraping, manual input, and real-time data streaming from systems and devices.

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

Data analysis: This is where data scientists examine the prepared data for patterns, ranges, value distributions, and biases to determine its relevance for predictive analytics and machine learning. The generated model can be responsible for providing accurate information that facilitates efficient business decisions to achieve scalability.

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

What is artificial intelligence?

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

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

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

Learning: This part of AI programming focuses on acquiring data and creating algorithms or rules that it uses to derive useful information from the data. The rules are straight to the point, with step-by-step instructions for performing specific tasks.

Reasoning: This aspect of AI programming has to do with choosing the right algorithm for a given predetermined outcome.

autocorrect– This aspect of AI programming continually refines and develops existing algorithms to ensure their results are as accurate as possible.

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

weak AI: This is also called Narrow AI or Narrow Artificial Intelligence (ANI). This type of AI is trained to perform specific tasks. The AI ​​developed to date falls into this category, driving the development of applications such as digital assistants, such as Siri and Alexa, and autonomous vehicles.

strong AI: Includes artificial general intelligence (AGI) and artificial superintelligence (ASI). AGI would imply a machine with the same intelligence as humans, with self-awareness and the awareness to solve problems, learn, and plan for the future. ASI is intended to surpass the intelligence and capacity of the human brain. Strong AI is still completely theoretical and perhaps unlikely to be achieved except through advanced mimicry or some kind of biological fusion.

Data Science vs. Artificial Intelligence: Key Similarities and Differences

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

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

basic definition: Modern data science involves the collection, organization, and predictive or prescriptive analysis of data based on ML, while AI encompasses that analysis or advanced machine insight capabilities that can provide data for an AI system.

1. Process: AI involves complex high-level processing, intended to forecast future events using a predictive model; Data science involves data preprocessing, analysis, visualization, and prediction.
2. Techniques: AI uses machine learning techniques by applying computer algorithms; Data science uses data analysis tools and statistical and mathematical methods to accomplish tasks.
3. Aim: The main goal of artificial intelligence is to achieve automation and achieve independent operation, eliminating the need for human intervention. But for data science, it’s finding 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 that are necessary for decision making.