Data Science vs. Machine Learning vs. Artificial Intelligence: Three peas in a pod?

Data Science, Machine Learning, and Artificial Intelligence are interconnected. But they are three different unique concepts. For most of us, who are from backgrounds that are not related to data science or machine learning, these exact differences might not be distinguishable. I hope this article would be a great help for a beginner to understand these concepts.

Data Science

Data Science is a broad domain that analyzes data and finds hidden patterns in data to derive useful information from data or to make decisions or to predict. Data Scientists utilize tools and necessary applications to transform the vastly generated data into sensible ones.

The five-stage life cycle of data science is as follows:

  1. Capture- This stage resembles the acquisition of data
  2. Maintain — This stage includes data warehousing and data cleansing
  3. Process- This includes data mining, data modeling, and clustering /classification
  4. Communicate- This includes data visualization, business intelligence, and decision making
  5. Analyze- This resembles regression, text mining, predictive analysis, etc.

Applications of Data Science

Data Science is widely used in the following fields.

  1. Healthcare
  2. Banking
  3. E-Commerce
  4. Transport
  5. Finance
  6. Manufacturing

Artificial intelligence

Simply AI means, the machines are made in a way to think rationally or act rationally or else think like humans or acts like humans. It can help to perform very complex tasks that otherwise need human intelligence.

The types of Artificial Intelligence

In a broader sense, AI can be categorized into three main types as Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI).

Artificial Narrow Intelligence focuses on performing one task in an efficient manner. For example, IBM’s Deep Blue is an AI that can play chess. This comprises the NLPs. This is the type that we have achieved so far.

Artificial General Intelligence (AGI) is somewhat similar to human intelligence not concentrating on a specific task. It should think and act like humans in theory. The complete AGI is not achieved yet as we are still not sure about how the human brain works and we are not technically capable enough to produce something that complex. Research is being carried out in AGI.

Artificial Super Intelligence ( ASI) will surpass humans. As thinking power is achieved through AGI, machines will think and learn from experience that leads to machines outperforming humans.

Applications of AI that have been used today

  1. Siri- digital voice assistant
  2. Alexa-digital voice assistant
  3. Tesla- self-driven vehicles
  4. Netflix-content creation and recommendations
  5. Amazon.com -enhancing customer experience

Machine Learning

ML is a subset of AI that has the basic idea that a machine can improve itself from experience. ML basically finds patterns based on data and sets up reasoning systems.

The types of Machine Learning

The main types of Machine Learning are as follows:

  1. Supervised Machine Learning

In supervised machine learning, the model is trained with labeled data

2. Unsupervised Machine Learning

In unsupervised machine learning, the model is allowed to recognize patterns with unlabeled data

3. Semi-Supervised Machine Learning

It employs both supervised and unsupervised methods

4.Reinforcement Machine Learning

Models learn from a series of trials and errors in an unfamiliar environment in reinforcement learning.

Applications of Machine Learning

  1. Traffic Alerts
  2. Social Media
  3. Google Translate
  4. Fraud Detection
  5. Online Video Streaming

Differences between Machine Learning, Artificial Intelligence and Data Science

Artificial intelligence depicts an action planned based on the feedback of perception whereas Machine Learning is predicting results based on trained data. Data Science finds the patterns in data in order to draw conclusions based on them. Artificial Intelligence has a wide scope that comprises machine learning and deep learning and Machine Learning is a subset of Artificial Intelligence. The purpose of AI is to create intelligent agents that are able to perform a variety of complex tasks whereas, in machine learning, machines are trained in order to perform certain tasks. Artificial Intelligence comprises learning, reasoning, and self-corrections whereas Machine Learning comprises self-correcting when new data is introduced. In addition to this AI system concerns about maximizing the chances of success whereas machine learning is concerned about accuracy and patterns.

Data Science is basically analyzing the data and Artificial Intelligence acts as a basic tool for this. With the help of Artificial Intelligence to impart autonomy to the data model, we can easily find the hidden patterns of data that is called data science. Data science utilizes enormous statistical procedures whereas AI uses computer algorithms. We can simply say Data science uses statistical learning and AI uses machine learning for their relevant purposes. Artificial Intelligence is very much related to machine learning algorithms but data science comprises a wide range of data operations.

Conclusion

Data science, artificial intelligence, and machine learning are interconnected but there are some differences between these concepts. Data Science analyzes data to get meaningful information, Artificial Intelligence is building machines that can perform tasks that require human intelligence whereas Machine Learning is a subset of AI that allows machines to improve from experience.

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Ahrane Mahaganapathy

A Tech Woman with passion, learnability, self-empowerment, interested in cutting — edge technologies and having the knowledge-sharing mentality.