Data science, machine learning, and AI are significant parts of business processes. However, these terms are often mixed up. That is a mistake.
In this post, I’m going to put everything in its place. I’ll explain the difference between data science, ML, and AI. And disclose how these technologies are intertwined with each other.
But first, let’s discuss what each technology stands for.
The central aspect of data science is receiving new results from data. It is connected with rigorous analytical evidence and deals with structured and unstructured data. We talk about data science when it’s needed to select, prepare or analyze data.
Data science lets you figure out the meaning and needed information from a large amount of data. Since the data warehouses keep tons of raw data, we can learn many new things by processing it.
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What is it used for?
- Tactical optimization
- Predicted analytics (the outlook of demand and events)
- Recommendation systems
- Automatic decision-making systems
- Social research
For example, Netflix utilizes its data quarries to search for viewing patterns. This lets managers figure out users’ preferences and decide on what kind of series they should make another time.
Who’s in charge of DS implementation? There’s always a human behind robotics – a data scientist who is good at data insights and sees the numbers.
Data scientists responsibilities:
- Understanding of various analysis tools like SAS
- Working with R, Python, SQL, RapidMiner
- Process data
- Provide statistical analysis
As for additional plus, DS specialists should be experienced in:
- simulations and quality control
- computational finance
- industrial engineering
The primary purpose of AI is to create a machine that will be smarter than humans.
AI can be used everywhere – from mobile apps for playing cards to voice assistants and smart homes. Just like the Apple Siri voice assistant, that answers questions and recognizes users’ speech.
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Artificial intelligence concentrates on building smart devices capable of thinking and acting like humans. These devices are being developed to enable problems and learn faster than humans do.
Here are the examples of AI application:
- Game-playing algorithms
- Robotics and control theory
- Natural language processing
- Reinforcement learning
The well-known examples of AI applying are self-driving cars and robots.
Machine learning is a branch of AI. It’s the science aimed at computer learning and acting like humans do, and improving their capabilities in an autonomous way.
Instead of coding, you enter data into the generic algorithm, and it creates its logic based on fed information. In other words, in machine learning, computers master to program themselves.
Machine Learning helps us to generate better results within a short time and makes programming more effective. Since programming belongs to the automation process, machine learning doubles automation.
What are the benefits of ML for companies?
For example, Netflix uses predictive analytics to provide better recommendations to customers. That’s how the platform improves users’ experience and expands the value of its services. All recommendations are offered to visitors based on machine learning analysis. ML considers users’ preferences and determines which films they tend to watch.
Machine learning specialists are in charge of implementing the scientific method into business scenarios and providing precise data for machine learning modeling.
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They deal with analytical algorithms to make models that clarify data relationships, predict scenarios, and transform data insights into business value.
ML specialists skills:
- Experience with MALLET
- Deep knowledge of Apache Tomcat/Open Source
- Good knowledge of C++, Python
- Working with GraphLab Create, scikit-learn, NetworkX, NLTK
Difference Between Machine Learning, AI, and Data Science
After we’ve figured out what machine learning, artificial intelligence, and data science stands for, it’s time to move to their particularities. And how they all are interconnected.
Data Science vs. Machine Learning
Machine learning and statistics are the areas of data science.
The ML algorithms work out on data provided by data science to give accurate and informed business forecasts. Hence, machine learning algorithms build upon the data and can’t learn without using data.
Indeed, data science covers more than machine learning. In data science, information may or may not proceed from a mechanical process. For instance, survey data can be gathered manually.
The main difference is that DS is not restricted to algorithmic or statistical aspects. In turn, it encompasses the entire data processing. So if ML specialists focus on creating practical algorithms during the project lifecycle, data experts have to swap between data roles based on the project needs.
AI vs. Data Science
Data science focuses more on the tech part of data management. It utilizes artificial intelligence to explain historical data, identify patterns in the present, and make forecasts. In this matter, AI helps data scientists to get an accurate understanding of their competitors.
Using various statistical techniques, data science lets us analyze, visualize, and predict data. While AI adopts models to forecast future events and use algorithms.
DS allows us to make models that make use of statistical insights. Artificial intelligence operates with models that train machines to think and act as humans do.
AI vs. Machine Learning
First, I would like to underline the main differences between AI and ML:
- AI focuses on building computers that imitate human behavior
- Machine learning is a subgroup of AI that uses methods allowing computers to draw inferences from data and give it to AI-based apps
AI is a wider scientific field compared to ML. It’s used to automate business processes and make machines perform like humans. Machine learning (AI subset) accelerates data science into the new automation level.
Artificial intelligence is usually associated with voice assistants like Google Home or Alexa. In turn, audio and video platforms (Netflix, YouTube, Spotify, etc.) are ML-powered.
Machine learning and AI can interact with each other to automate customer services (personal robot assistants) or vehicles (driverless cars).
These technologies help businesses to save thousands of dollars by eliminating human involvement and allowing them to proceed to more critical issues (e.g., environmental pollution).
As you can see, all these technologies have their own specific features, but you can’t take one technology putting aside another. Data science and ML are closely related: machines can’t advance without data, data science is better performed with machine learning.
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The same thing with AI. You can’t use machine learning for autonomous learning ignoring artificial intelligence. AI builds devices to act like humans. ML makes devices that let algorithms learn from data.
My name is Katherine Orekhova and I am a technical writer at Cleveroad – mobile app development company. I’m keen on technology and innovations. My passion is to tell people about the latest tech trends in the world of IT.