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Data science and artificial intelligence (AI) are two complementary technologies in the modern tech environment. Data science organizes and crunches the large, often variably structured, datasets that often fuel AI algorithms. AI tools may likewise be employed in the data science process.

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

And, while AI “aims to train the technology to accurately imitate or — in some cases — exceed the capabilities of humans,” it today relies on somewhat brute-force “learning” from very large datasets that a data scientist or similar professional has organized, and written or guided algorithms for, to apply to a relatively narrow application.

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

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What is data science?

Data science deals with large volumes of data, combining tools like math and statistics, and modern techniques such as specialized programming, advanced analytics and ML to discover patterns and derive valuable information that guides 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, all-inclusive of customer data, log files, video, audio, pictures, the internet of things (IoT), social media and a lot more. The data can be extracted from a myriad of relevant sources using different methods, such as web scraping, manual entry and real-time data streamed from systems and devices. 

Data processing and storage: This involves cleaning, transforming and sorting the data using ETL (extract, transform, load) models or other data integration methods. Data management teams set up storage processes and structures, considering the different formats of data available. The data is prepped to make sure that quality data is loaded into data lakes, data warehouses or other repositories to be used in analytics, ML and deep learning models.

Data analysis: This is where data scientists examine the prepared data for patterns, ranges, distributions of value, and biases to determine its relevance for predictive analysis 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 forms of graphs, charts, reports and other readable formats that aid easy comprehension. An understanding of these analyses promotes business intelligence.

What is artificial intelligence?

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

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

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

Learning: This part of AI programming concentrates on procuring data and creating algorithms or rules that it uses to derive actionable insight from the data. The rules are straight to the point, with step-by-step directions for performing specific tasks.

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

Self-correction: This aspect of AI programming continually refines and develops existing algorithms to ensure that their outcomes 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 artificial narrow intelligence (ANI). This type of AI is trained to perform specific tasks. The AI developed to date falls under this category, driving the development of applications such as digital assistants, like Siri and Alexa, and autonomous vehicles.

Strong AI: This comprises artificial general intelligence (AGI) and artificial super intelligence (ASI). AGI would involve a machine having equal intelligence to humans, with self-awareness and the consciousness to solve problems, learn and plan for the future. ASI is intended to exceed the intelligence and capability of the human brain. Strong AI is still entirely theoretical and perhaps unlikely to be achieved except through advanced mimicry or some sort of biological merger.

Data science vs. artificial intelligence: Key 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 makes use of 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 false, because data science does not represent artificial intelligence. 

Basic definition: Modern data science involves the collection, organization and predictive or prescriptive ML-based analysis of data, while AI encompasses that analysis or advanced machine perception capabilities that may provide data for an AI system.

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

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