Even an organization offering a new technology powered by any of these may talk about their high-end data science techniques without having much knowledge about them.
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In this post, we’ve outlined the relation between these technologies. But first, let’s have a quick look at what each of them stands for.
1- Data science
Put simply, data science refers to the process of extraction of useful insights from data. This interdisciplinary approach merges various fields of computer science, scientific processes and methods, and statistics in order to extract data in automated ways.
In order to mine big data, which is closely associated with the field, data science uses a diverse range of techniques, tools and algorithms gleaned from the fields. Data science training advances these techniques. If you join events or meetup that organized by Data Science Courses (as Magnimind Academy etc.), you can get more information about these techniques.
2- Machine learning
In machine learning (ML), statistical methods are used to empower machines to learn without being programmed explicitly.
The field focuses on letting algorithms learn from the provided data, collect insights, and make predictions on unanalyzed data based on the gathered information. In general, machine learning is based on three key models of learning algorithms:
- supervised machine learning algorithms,
- unsupervised machine learning algorithms,
- reinforcement machine learning algorithms.
In the first model, a dataset is present with inputs and known outputs. In the second one, the machine learns from a dataset that comes with input variables only. In reinforcement learning model, algorithms are used to select an action.
3- Artificial intelligence
Though it’s a broad term, at its core, artificial intelligence (AI) refers to the process of making machines enable to simulate the human brain function.
In the modern technology landscape, artificial intelligence is divided into two key areas.
The first one is general AI, which is based on the concept that a system can handle tasks like speaking and translating, recognizing sounds and objects, performing business or social transactions etc. The other one is applied AI that refers to concepts like driverless cars.
4- How are all these fields related to each other?
The interdisciplinary field of data science uses key skills of a wide range of fields including machine learning, statistics, visualization etc. It enables us to identify meaning and appropriate information from huge volumes of data to make informed decisions in technology, science, business etc.
For a simpler view on the relation between these technologies, artificial intelligence is applied based on machine learning. And machine learning is a part of data science that draws features from algorithms and statistics to work on the data extracted from and produced by multiple resources. Thus, you can say data science merges together a bunch of algorithms obtained from machine learning to develop a solution, and during the process, lots of ideas from traditional domain expertise, statistics and mathematics are borrowed.
In other words, data science stands for an all-inclusive term that consists of aspects of ML for functionality. Interestingly, ML is also an element of artificial intelligence, where a diverse set of purpose is achieved on a whole new level. ML and AI are a part and parcel of data science. All of these are considered, you can learn data science in Silicon Valley.
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Connection Between Data Science, ML and AI was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.