Data Science vs Artificial Intelligence

artificial intelligence vs data science
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Data Science vs Artificial Intelligence

Data Science and Artificial Intelligence, are the two most important technologies in the world today. While Data Science makes use of Artificial Intelligence in its operations, it does not completely represent AI. Data Science and Artificial Intelligence are the most commonly used interchangeably. While Data Science may contribute to some aspects of AI, it does not reflect all of it. Data Science is the most popular field in the world today. However, real Artificial Intelligence is far from reachable.

Data Science

Data Science is the current reigning technology that has conquered industries around the world. It has brought about a fourth industrial revolution in the world today. This a result of the contribution by the massive explosion in data and the growing need of the industries to rely on data to create better products. We have become a part of a data-driven society. Data has become a dire need for industries that need data to make careful decisions. Data Science involves various underlying fields like Statistics, Mathematics, and Programming. Therefore, a data scientist is required to be proficient in them in order to understand trends and patterns in the data. This heavy requirement of skills gives Data Science a steep learning curve.

The various steps and procedures in data science involve data extraction, manipulation, visualization and maintenance of data to forecast the occurrence of future events. A Data Scientist is should also have a sound knowledge of machine learning algorithms. Industries require data scientists to help them make necessary data-driven decisions. They help the industries to assess their performance and also suggest necessary changes to boost their performance. They also help the product development team to tailor products that appeal to customers by analyzing their behavior.

Artificial Intelligence

Artificial intelligence refers to the simulation of a human brain function by machines. This is achieved by creating an artificial neural network that can show human intelligence. The primary human functions that an AI machine performs include logical reasoning, learning and self-correction. Artificial intelligence is a wide field with many applications but it also one of the most complicated technology to work on. Machines inherently are not smart and to make them so, we need a lot of computing power and data to empower them to simulate human thinking.

Artificial intelligence is classified into two parts, general Artificial Intelligence and Narrow Artificial Intelligence. General AI refers to making machines intelligent in a wide array of activities that involve thinking and reasoning. Narrow AI, on the other hand, involves the use of artificial intelligence for a very specific task. For instance, general AI would mean an algorithm that is capable of playing all kinds of board game while narrow AI will limit the range of machine capabilities to a specific game like chess or scrabble. Currently, only narrow AI is within the reach of developers and researchers. General AI is just a dream of researchers and perception among the masses that will take a lot of time for the human race to achieve

Machine Learning

Machine Learning is a subset of  Artificial Intelligence which gives machine the ability to learn without being explicitly programed. There are three types of machine learning

1.Supervised learning (2) Unsupervised learning (3) Reinforcement learning 

Supervised learning

Supervised learning is the most common sub branch of machine learning today. Typically, new machine learning practitioners will begin their journey with supervised learning algorithms.. Supervised machine learning algorithms are designed to learn by example. The name “supervised” learning originates from the idea that training this type of algorithm is like having a teacher supervise the whole process.

When training a supervised learning algorithm, the training data will consist of inputs paired with the correct outputs. During training, the algorithm will search for patterns in the data that correlate with the desired outputs. After training, a supervised learning algorithm will take in new unseen inputs and will determine which label the new inputs will be classified as based on prior training data. The objective of a supervised learning model is to predict the correct label for newly presented input data.

Unsupervised learning

Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabeled data. Unsupervised learning algorithms allows you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods

Reinforcement learning 

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of training dataset, it is bound to learn from its experience.

Deep learning

 is a subset of machine learning where algorithms are created and function similarly to machine learning, but there are many levels of these algorithms, each providing a different interpretation of the data it conveys. This network of algorithms is called artificial neural networks. In simple words, it resembles the neural connections that exist in the human brain.

Comparison

Data Science vs Artificial intelligence

Factors Data Science Artificial Intelligence
Scope Involves Various underlining data operations Limited to the implementation of ML algorithms
Type of Data Structured and Unstructured Standardized in the form of embeddings and vectors
Tools R, Python, SAS,SPSS, Tensorflow, Keras,Scikit-learn Scikit-learn, Kaffe, PyTorch, Tensorflow, Shogun, Mahout
Applications  Advertising, Marketing, Internet Search Engines Manufacturing, Automation, Robotics, Transport, Healthcare

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Conclusion

In this Data Science vs Artificial Intelligence, we got to know the two terms used interchangeably. Artificial Intelligence is a broad domain that is still largely unexplored. Data Science is a field that makes use of AI to generate predictions but also focuses on transforming data for analysis and visualizations. Therefore, in the end, we conclude that while Data Science is a job that performs analysis of data, Artificial Intelligence is a tool for creating better products and imparting them with autonomy.

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