Data Science vs. Machine Learning vs. Artificial Intelligence

Introduction

  • You must have heard about Data Science, Machine Learning, and Artificial Intelligence. But, they are not the same. There are some differences between the three, yet they are connected with each other.
  •  If you are a technology enthusiast, we suggest you read this blog post till the end as there are some interesting things waiting for you to explore.
  •  In this post, we will be talking about the meaning of Data Science, Machine Learning, and Artificial Intelligence, the connection between Data Science and Machine Learning, AI vs. Data Science, the Difference between AI and Machine Learning, and how Data Science, Machine Learning, AI can be used all at the same time.
Data Science vs. Machine Learning vs. Artificial Intelligence

 Meaning of Data Science

With the help of Data Science, you can get meaningful information from large sets of raw data. Data Science, therefore, includes data selection, preparation, and analysis that helps you make crucial decisions. You get to work with both structured data and unstructured data.

 Following are the areas where Data Science is mostly used:

 a)   For improving Business Processes and Marketing Campaigns – Tactical optimization is needed for the same.

 b) To forecast demand and events – Predicted Analytics is put to use for the same.

 c) Used in Amazon and Netflix – For the recommendations that viewers get, Data Science is used.

d)   Used in Face Identification/Recognition Systems and Drones – Data Science helps in creating automatic decision-making systems.

e)   Used for Questionnaire Analysis – Data Science used in Questionnaire Analysis facilitates social research.

 Meaning of Artificial Intelligence (AI)

 Artificial Intelligence enables machines to behave like humans and resolve problems in an advanced way, something which even humans are not capable of doing. It is thus used to build smart devices like Amazon’s Alexa, which is capable of recognizing speech and answering queries.

 Here are the examples of AI applications:

 a)   Algorithms for game playing (E.g- Deep Blue)

 b)   Robotics (E.g – setting a robot to motion, self-driving cars, and robots)

 c)   Optimization ( E.g – Route creation by Google Maps)

 d)   Processing of Natural Languages

 e)   Reinforcement Learning

 Meaning of Machine Learning

 Artificial Intelligence includes Machine Learning that is the science of enabling computers to behave like humans and be more learned without taking any outside help. Machine Learning enables computers to program themselves and that helps in generating better results.

 Examples of Machine Learning:

 a) Predictive Analytics used to provide better recommendations to visitors (E.g – Netflix)

 b) Data cleaning and preparation for Machine Learning and Statistical Modelling (E.g – Businesses)

 The connection between Data Science and Machine Learning

Data Science includes Machine Learning and Statistics and therefore, there are many connections between the two.

 Data Science provides data on which Machine Learning algorithms train to generate more informed results helpful for business predictions.

 Data Science is not limited to Machine Learning as some types of data do not need to be collected from machines. In fact, data in such cases are collected manually. For example- Survey Data.

 There is one difference though and that is the fact that Data Science deals with everything related to data processing and not just the algorithmic or statistical factors.

 Some of the Fields covered by Data Science are:

 a)  Data Integration

 b)  Data Engineering

 c)  Data Visualization

 d) Distributed Architecture

 e) Data-dependent decisions

 f) Deployment (Production Mode)

 AI vs. Data Science

 AI is used by data Science for the interpretation of historical data, recognition of patterns in the current data, and for making predictions. Data Scientists thus use AI and Machine Learning to collect insights about their competitors.

 Data Science uses different statistical techniques to analyze, visualize and predict data. On the other hand, AI uses algorithms and implements models to make predictions.

 While Data Science helps in statistical analysis by creating models, AI aims at transforming machines into humans by using models.

 Difference between AI and Machine Learning

The aim of AI is to automate business processes and operations and enable machines to behave like humans. Machine Learning, on the other hand, is a branch of AI that pushes Data Science into the next automation level.

 AI powers gadgets like Siri, Alexa, and Google Home, whereas, Machine Learning powers audio and video prediction systems like YouTube, Amazon, Netflix, and Spotify.

 However, both AI and Machine Learning can be used together to produce digital assistants that help in automating customer service and self-driving cars that enable automated driverless driving.

 Both AI and Machine Learning help companies save huge costs as humans are replaced by machines and, therefore, employee costs are saved.

How can Data Science, Machine Learning, and AI be used all at the same time?

Are you wondering how? Let’s explore the opportunities. Let’s suppose we are building a self-driving car that is meant to stop at every traffic signal. We can’t use just Machine Learning or AI. We need to use AI, Machine Learning, and Data Science together.

1)    This is how Machine Learning is going to help the self-driving car:

 The car should be able to use its camera to recognize stop signs. What is needed? A data set with photos of street-side objects and train the car using algorithms to recognize the objects having stop signs on them.

2)    This is how AI will help the self-driving car:

 After recognizing the stop sign, the car should be able to automatically press the brakes and at the right time. Control theory can help in controlling the car in case the road is slippery.

3)    This is how Data Science will help in the running of the self-driving car:

 Data Science will help us analyze the number of times the car doesn’t stop or fails to recognize stop signs. For example, it might not stop at night as most of the pictures are daylight pictures. We can thus add nighttime pictures and test it again.

 Conclusion

Although there are differences between Data Science, Machine Learning, and AI, when used to produce a machine, we need to use all the three together as they are interrelated as proved in the above example of a self-driving car.

 

 

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