Data Science And Artificial Intelligence: A Hot Combination

Data science and Artificial Intelligence: If you are in the IT field, you know that artificial intelligence (AI) has been out of science fiction for years and is present in our daily lives.

The numbers only tend to increase with digital transformation, Big Data, and automation. Whoever masters the main concepts of data science and artificial intelligence will be a sought-after professional in this scenario. So you don’t miss the opportunity, we’ve prepared a summary of the main ideas in each area. Let’s start with defining each of them.

What Is Data Science?

Data science is an interdisciplinary field that studies and analyzes structured and unstructured data to extract knowledge, detect patterns and gain insights. The main scientific disciplines that the area draws on are mathematics, statistics, and computing. Data, in turn, is nothing more than a value assigned to something. This value is awarded through documented observations or measurement results.

This definition of data is pretty broad. Here, when we talk about data, we will refer to the traces left by users in the online environment when accessing websites or using mobile device applications.

Returning to data science, the area has gained notoriety in recent decades with the massification of the internet and the dissemination of the concept of Big Data. The term refers to the collection and strategic analysis of a large volume of data, which are transformed into useful information for public and private entities.

The professional specializing in data science is responsible for collecting, storing, and classifying all this information to make it intelligible to decision-makers.

How Artificial Intelligence Works

Artificial intelligence works from the combination of algorithms and a large volume of data. An algorithm is a set of rules and logical procedures previously defined to reach a certain goal, following a finite number of steps.

The union between data and algorithms allows artificial intelligence to read and interpret patterns, in addition to learning autonomously, without human action. This is possible thanks to machine learning.

Other technologies used in artificial intelligence are deep learning and Natural Language Processing (NLP):

  • Deep learning: It is a more complex variant of machine learning. It employs artificial neural networks distributed in abstraction layers, where data circulates. It is widely used in pattern identification, image and speech recognition, and data processing.
  • Natural Language Processing (NLP): studies and develops reproductions of processes linked to the functioning of human language. In practice, attempts are made to make computers understand texts entirely, recognizing the context, extracting information, and offering summaries. NLP is widely used in user service sectors.

The term “artificial intelligence” refers to the ability of a machine to perform activities in a way that is considered intelligent. This device must simulate human skills such as analysis, reasoning, and perception of the environment.

Despite seeming futuristic, artificial intelligence was already discussed in the 1940s in the works of Alan Turing, such as the article “Computing Machinery and Intelligence.” Today, it is a consolidated interdisciplinary scientific field that brings together researchers from Philosophy, Linguistics, Biology, Psychology, and Neuroscience, to name a few examples.

What Is The Relationship Between Data Science And Artificial Intelligence?

Data science and artificial intelligence are complementary areas that borrow concepts and tools from each other, particularly those employed in machine learning. While providing information for machine learning, data scientists are turning to AI processing and standardization capabilities to develop their work.

To exemplify the relationship between data science and artificial intelligence, imagine a job description for a data scientist and another for an AI analyst. First, IT professionals must know AI tools to classify data and make predictions, such as deep learning algorithms.

In the second, the job title will likely reveal the difference between the work the candidate and a data scientist will do: Deep Learning Scientist, Machine Learning Engineer, NLP Scientist, and so on. Whoever fills the vacancy will likely use data science tools daily, such as R and Python.

Below you will find some more examples of this exchange of tools between data science and artificial intelligence:

Text Mining

Semi-automatic process of extracting information from unstructured data. Document datasets are created, and their knowledge is transformed into a numerical matrix.

Time-Series Forecasting

Similar to the concept of extrapolation from statistics, this artificial intelligence resource is used to make predictions. Models are created from the history of collected data.

Recommendation Systems

This artificial intelligence technology offers item recommendations to a given user based on machine learning algorithms. It relies on information provided by the data scientist to discover patterns of consumer behavior.

KNN (K-Nearest Neighbors)

KNN is a supervised learning algorithm widely used in data mining and machine learning. It is considered a classifier, performing the teaching through the similarity between the data.

Bayesian Inference

With its origins in statistics, Bayesian inference is fundamental in machine learning because it offers an evaluation of hypotheses based on likelihood. They are widely used in data mining.

Decision Tree

Like flowcharts, a decision tree combines the best data science and artificial intelligence to provide a machine learning method. Decision trees are generally used in classification and prediction tasks.

Also Read: Trends In AI: The Best Researchers In The World Are Working On This

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