Modern terms such as ML, data science, big data, and data science services remain a mystery to many people, and they often confuse these notions. In this article, we will explain these technologies in simple terms to make it easier to see what exactly is the difference between them.
Knowing Who’s Who and What’s What
In order to define what that situation between data science vs machine learning vs big data is, let’s overview each technology separately.
Data science
This is the method for analyzing data and extracting valuable information and knowledge from it. It overlaps closely with the areas of machine learning and, of course, technologies for working with huge massives of data.
Machine learning
Machine learning is the use of algorithms to deeply explore data assets, extracting the most useful info possible. Through ML techniques, one can also employ given datasets to predict future trends. Such software employs predictive and statistical analysis approaches to identify patterns and uncover hidden yet meaningful knowledge.
An excellent example of the implementation of machine learning in real life is algorithms that study your behavior on the network and then provide recommendations.
Big data
The definition of Big data is usually quite simple - it is a huge amount of information, often unsystematic, which is stored on some kind of digital medium. However, the data called “Big” is so vast that it is impossible to efficiently process it with the usual means of structuring and analytics. Therefore, the term “big data” is also understood as technology for searching, processing, and using huge heaps of ill-structured information.
Data Science vs Data Analytics vs Machine Learning
Data science is a broad concept that includes many disciplines. Machine learning falls under data science as it employs several techniques commonly used in this field. Data science, on the other hand, encompasses many disciplines, as opposed to machine learning, which focuses on one subject.
When it comes to data analytics, the focus is on raw data or primary sources. However, the goal of analytics is to identify algorithms and cause-effect chains, for example, user behavior or market trends, using data.
Ultimate Benefits of ML
Let's find out more about the advantages of machine learning.
- Seamlessly pinpoints common tendencies and patterns. ML can process huge amounts of data and detect certain trends and patterns that will not be obvious to humans. For example, e-commerce websites aim to clarify how users behave during browsing and what individual purchase history specifics there are so that more relevant systems for finding relevant products, offers, and reminders related to specific wishes and desires can be implemented. The results of such analyses are used to come up with the most individually relevant ads.
- No human intervention is required. With ML, you don't have to oversee your project every step of the way. Since it means empowering machines to learn, it allows them to make predictions as well as improve algorithms on their own. Antivirus software is a typical example of this. Antiviruses learn to filter new threats as they are recognized. ML is also good at detecting spam.
- Continuous improvement. As you gain experience, machine learning algorithms improve. This allows them to make more informed decisions.
- Processing of multidimensional and diverse info. ML algorithms are truly efficient for processing multidimensional and varied data, and they can do so in uncertain and highly dynamic environments.
- Extensive field of application. You can be an online seller or a healthcare provider and make ML work for you. Where applicable, it can help provide clients with a much more personal experience while also targeting the right clients.
Difference Between the Big Three
Figuring out the difference between data science AI and machine learning, we must understand that AI is a group of methods and algorithms that are used to solve various kinds of problems, used in various fields, from robotics to text content inspection. But ML involves algorithms capable of learning to solve certain problems autonomously over time.
Data science uses machine learning to analyze data and predict the future. It combines machine learning with other disciplines such as analytics of big data. Data science is a hands-on application of machine learning with a full focus on solving real-world problems.
Conclusion
Our team is focused on developing solutions that leverage the above technologies and help solve real-life business problems. If you are looking for a company that provides data science services and machine learning consulting, then contact us.
