Everybody has heard about data science, machine learning, and AI. However, people are often confused with these terms and treat them as synonyms.
In this post, we’ll discuss each of the technologies separately and point out the core differences.
What Is Data Science?
The primary feature of DS is obtaining new outcomes from data. Data analytics uses objective analytical evidence-based criteria and deals with organized and disorganized data. In fact, when you need to choose, prepare, and analyze data, you should appeal to data science.
DS enables us to define the meaning and needed information from large data sets. As there is a lot of raw data collected in the data repository, there are new things to find out while processing the required information.
What is data science applied for?
- Strategic optimization (e.g., boost digital marketing campaigns, streamline business processes)
- Predicted analytics (e.g., predict demand)
- Testimonial systems (e.g., Netflix recommendation service)
AI aims at creating intelligent machines that will act at the level of human intelligence. Smart devices are being learned to solve multiple problems and learn things much faster than humans do.
There are numerous examples of AI – autopilot cars, face recognition systems, AI-powered chatbots, smart assistants, manufacturing robots, etc.
AI-based application examples:
- Min-Max algorithms for game playing
- Control theory of robotics systems
- Optimization (for example, defining the optimal routes for vehicle)
- Language processing
- Reinforcement learning
An excellent example of an AI appliance is AI-powered chatbots. Businesses can get significant benefits by creating a chatbot since it gives customers immediate responses. For instance, Capital One (a US-based bank holding company) built its own AI chatbots called Eno to provide 24/7 support. It helps clients not only pay their credit card bills, but also they can talk about life.
AI specialists deal with the following AI frameworks:
- Pytorch & Torch
What Is the Meaning of Machine Learning?
Machine learning is a core sub-field of AI. It’s the science aiming at taking computers into the way of self-programming. Simply, ML trains computer programs to learn and perform like humans.
You transmit information to the general algorithm without writing a code. Then, the algorithm makes its logic under that data. Machine learning allows us to scale the programming and deliver better results as quickly as possible. If the programming is an automated process, machine learning redoubles its automation.
How does Netflix apply ML? It utilizes a recommendation engine based on machine learning algorithms to provide a personalized experience to its visitors. It analyzes users’ selections to figure out what shows or movies they prefer. In this way, the company tries to keep its users within its platform as long as possible.
The Connection Between Data Science and Machine Learning?
Machine learning belongs to data science. So there’s a bunch of connections between these terms.
Using data science data, machine learning algorithms learn from themselves to become more intelligent and informed. Thus, the ML can predict development in business precisely. Machine learning algorithms can’t learn without data science.
Indeed, data science refers not only to machine learning. In data science, the data can be obtained from a machine process or gathered manually.
However, data science addresses the whole range of data processing. In turn, machine learning covers only algorithmic or statistical components.
Here are some areas data science relates to:
- data integration
- distributed architecture
- data visualization
- data engineering
So while machine learning specialists are engaged with designing practical algorithms during the project lifetime, DS experts have to adjust multiple data roles to the project’s needs.
Data Science vs. AI
Data science is a field that analyzes a large amount of data using multiple analytics methods to get useful business insights. Artificial intelligence speaks about intelligent computers that can figure out, learn, and make decisions using data.
DS utilizes artificial intelligence algorithms to translate historical data, identify patterns in the present, and predict events. Thus, AI and machine learning assist DS experts in collecting valuable insights about competitors.
We can design models that utilize statistical information based on data science. AI interacts with these models that train machines to imitate human behavior.
The Difference Between AI and Machine Learning
First, let’s consider the unique characteristics of AI and machine learning:
- AI focuses on developing computers that will replicate human behavior in decision-making processes.
- ML is a branch of AI that uses analytical methods to enable computers to make conclusions based on data and transmit it to AI-powered apps.
Artificial intelligence is a broader umbrella under which ML comes. AI aims at automating work processes and building machines that act like humans. Machine learning moves data science to the next automation stage.
Smart devices like Google Home or Alexa are working on AI. In turn, recommendation systems like Walmart, YouTube, Netflix, Etsy are associated with machine learning.
Even though ML and AI have their own peculiarities, they can interact with each other to computerize customer services (voice assistants, robot assistants) and vehicles (robo-car).
Hopefully, you’ve understood the difference between AI, machine learning, and data science and how these terms are connected. Using these technologies, businesses can reduce human involvement and save thousands of dollars respectively. As a result, humans can concentrate on more urgent and complex tasks.
Vitaly Kuprenko is a writer for Cleveroad. It’s a web and mobile app development company with headquarters in Ukraine. He enjoys writing about technology and digital marketing.