Is Data Science and AI (Artificial Intelligence) Same?

01-Jun-2022

Data Science and Artificial Intelligence are the buzzwords that are dominating this scientific world. While they are both parts of the same area, they each have their own applications and meanings. There may be some overlaps in these sectors from time to time, but each of these three terms has its own set of applications. Hence data science and Artificial Intelligence may be related but they are not the same

Let us have a detailed comprehension of the differences between these two terms here in this post. To have an in-depth understanding of the two, let us discuss them separately.

What is Data Science?

Data science is an extensive branch of study that focuses on data systems and processes with the goal of preserving and making sense of massive amounts of information. Data scientists use a combination of technologies, applications, ideas, and algorithms to make sense of random data clusters because almost all sorts of businesses generate escalating volumes of data all over the world, and monitoring and preserving data is getting increasingly difficult. To keep up with the ever-growing data collection, data science focuses on data modeling and data warehousing. The application of data science is to extract the information required to influence business decisions and processes to achieve organizational objectives.

Understanding the scope of data Science

Business intelligence is one of the areas that data science has a direct impact on. Having said that, each of these roles has its own set of responsibilities. Data scientists work with large amounts of data to analyze patterns, trends, and other factors. These analytic programs generate reports that can then be used to make judgments. A Business Intelligence specialist picks up where a data scientist leaves off, analyzing data trends in any business field and giving business projections and course of action based on these findings. Surprisingly, there's a comparable discipline called Business Analyst that uses data science, data analytics, and business intelligence applications.

A business analyst's job incorporates elements of both to assist businesses in making data-driven decisions.

Data scientists use several formats to analyze historical data in order to meet various needs, such as:

Predictive causal analytics: This methodology is used by data scientists to generate business forecasts. The predictive model depicts the observable outcomes of several business procedures. This can be a useful concept for companies attempting to predict the future of a new company venture.

Prescriptive Analysis: This type of analysis aids firms in achieving their objectives by recommending the actions that are most likely to succeed.

The prescriptive analysis takes the findings from the predictive model and helps organizations achieve their objectives by recommending the best paths to take.

Data science employs data oriented-tools such as Python, SQL, R and Hadoop, etc. However, to extract meaning from sets of data, it makes considerable use of statistical analysis, data visualization, distributed architecture, and other techniques. Data scientists are highly skilled experts with the ability to switch jobs swiftly at any stage of a data science project's life cycle. They are equally comfortable working with artificial intelligence and machine learning.

In fact, machine learning skills are required by data scientists for certain tasks such as:

Machine Learning for Predictive Reporting: Machine learning algorithms are used by data scientists to analyze transactional data and create relevant predictions. This methodology, also known as supervised learning, can be used to recommend the most effective courses of action for any firm.

Machine Learning for Pattern Discovery: Pattern discovery is critical for businesses to define parameters in various data reports, and machine learning is one approach to accomplish it. This is essentially unsupervised learning, in which no parameters have been set in advance. Clustering is the most often used technique for pattern detection.

Understanding Artificial Intelligence

Let's get into the intricacies of Artificial Intelligence and get a broader understanding of what the subject matter entails.

What is Artificial Intelligence?

Artificial intelligence, simply defined, aims to empower machines to function in the same way as humans do. Because one of the main goals of AI is to train machines via experience, it's crucial to offer the necessary data and enable self-correction. Artificial Intelligence practitioners employ tools such as Natural language processing and Deep Learning in order to assist robots in detecting patterns and inferences.

The scope of Artificial Intelligence

AI makes automation simple: AI enables you to streamline and automate, high-volume processes by establishing dependable systems that run frequently.

Intelligent Things: Artificial intelligence (AI) has the potential to transform ordinary products into sophisticated commodities. When AI applications are combined with conversational platforms, bots, and other smart machines, enhanced technologies can be achieved.

AI algorithms may train machines to fulfill any desired function through progressive learning. Predictors and classifiers are built into the algorithms.

Data Analysis: Because machines learn from the data we supply them, it's critical to analyze and discover the proper set of data. Machine learning is made easier using neural networking.

The above-detailed information signifies how AI and data science are interrelated but are not the same.