The Difference Between Data Science vs AI vs ML

Data Science, Artificial Intelligence, and Machine Learning are the most buzzwords nowadays. We hear a lot about them, but what do they mean? what is the difference between data science, AI, and ML? Are they connected and how?

What is Data Science?

The goal of data science is to get new results from data. Tons of raw data is stored in warehouses, and we’ve learned a lot by studying it. Data science creatively uses the information to add value to the business. It’s about uncovering hidden information that will help businesses make better decisions. That’s what data science is for:

  • Tactical optimization (to improve business processes)
  • Forecast analysis (demand forecast for products or services)
  • Recommendation systems (such as YouTube or Spotify)
  • Automatic decision-making systems (such as facial recognition systems)
  • Social survey (for processing questionnaires)

But big data means nothing if you don’t know how to put it into action. Behind the technology is a person – a data scientist who understands the ideas and sees the numbers.

Here are the skills of data scientists:

  • Cloud Tools like Amazon S3
  • Big Data Platforms (Hive & Pig, Hadoop)
  • Python, Perl, Java, C / C ++
  • SQL databases
  • SAS and R languages
  • Statistics
  • math

And that’s just the beginning. Data scientists are experienced in data mining, cleaning and cleaning, data visualization, and reporting techniques. This list is always changing, as is data science.

What do you mean by AI and how it is used?

The heart of artificial intelligence is the transfer of human intellect to machines. AI is focused on developing smart devices that act like humans. They are trained to solve problems and learn from them. AI can refer to anything from voice recognition systems like Siri or Alexa to Amazon delivery bots. Or AI in logistics.

How do people use AI? Here are some use cases:

  • For game algorithms
  • Robotics and control theory (for example, motion planning)
  • Optimization (when online cards wake up quickly)
  • Natural language processing

Machine Learning: What Is It And Why You Should Use ML?

Machine learning is clearly and simply a branch of AI. Not all AI is about machine learning, but all machine learning is about AI. The idea of ML is that computers learn things – without being programmed to do so. Instead of writing code, engineers provide information for a generic algorithm. It then builds logic based on that data.

Machine learning makes programming scalable and achieves better results in less time. If the programming is called as “automation”, can we call machine learning as “double automation”. How machine learning used? machine learning is used in data science to create systems that predict future trends. ML is used for medication, security systems, robotics and even spam filters based on machine learning and recognition models

ML Vs AI Vs Data Science

So what’s the real difference between ML and AI, what does data science have to do with it?

ML and statistics are part of data science. Machine learning algorithms train with data collected from data science; This is how they get smarter. Therefore, ML algorithms are data-dependent – they don’t learn anything else.
Data science is more than Machine Learning. Information can be collected manually, e.g. B. Survey Data. Some of the time it has nothing to do with learning. The difference is that data science covers the entire spectrum of data processing; it is not limited to algorithmic or statistical aspects.

What about AI versus Data Science?

Data science is a process that includes analysis, visualization, and prediction and uses various statistical techniques. AI is the use of a predictive model to predict future events. It uses logical and decision trees. We use data science to create models that use statistical information. Artificial intelligence, on the other hand, works with models that make machines behave like humans.

AI versus Machine Learning?

As I said before, machine learning is a subset of artificial intelligence. ML consists of methods that allow computers to draw conclusions from data and make them available for AI applications. AI is a vast field that deals with automation processes and makes machines work like humans. Machine learning takes data science to the next level of automation. AI is about human-AI interaction devices like Siri, Alexa, Google Home, and many more. But we call it ML technology audio and video prediction systems (like Netflix, Amazo, Spotify, YouTube).

How do Data Science, AI, and Machine Learning Work Together?

For example, suppose you are building a self-driving car and you want it to stop at stop signs. You need to make this happen with all three. Machine learning. In order for the car to recognize stop signs with cameras, you need to create a dataset of images of objects on the roadside and train an algorithm to identify which stop signs contain.

Artificial intelligence. When the car detects the signal, it should break at the right time, neither too early nor too late. It’s a matter of control theory.

Read more on Data Science Training in Pune

Data Science. Imagine that during the test we find that sometimes the car does not respond to signals to stop. What do we do? Analyze the test data and find out why. It could be the time of day. At night, the car loses stop signals because the driving data only contains daylight objects. We added some nighttime footage and went back to testing.

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