Disclosure: Hackr.io is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.
Data Science vs Machine learning
Table of Contents
The words data science and machine learning are often used in conjunction, however, if you are planning to build a career in one of these, it is important to know the differences between machine learning and data science.
Before doing so, we need to understand a few important terms that are related but different.
AI (Artificial intelligence) – AI or machine intelligence refers to the intelligent decisions made by machines at par with their human counterparts. It is a study where we enable machines to learn through experience and make it intelligent enough to perform human-like tasks. In my article about AI vs ML, I have listed the differences between AI and Machine learning. For this article, let me give you a simple definition of machine learning.
Machine learning – Think of ML as a subset of AI. Same way as humans learn with experience, machines can learn with data (experience) rather than just following simple instructions. This is called as machine learning. Machine learning uses 3 types of algorithms – supervised, unsupervised and reinforced.
Deep learning – Deep learning is a part of Machine learning, which is based on artificial neural networks (think of neural networks similar to our own human brain). Unlike machine learning, deep learning uses multiple layers and structures algorithms such that an artificial neural network is created that learns and makes decisions on its own!
Big Data – Humongous sets of data that can be computationally analyzed to understand and process trends, patterns and human behavior.
Data Science – How is all the big data analyzed? Fine, the machine learns on its own through machine learning algorithms – but how? Who gives the necessary inputs to a machine for creating algorithms and models? No points for guessing that it is data science. Data Science is a uses different methods, algorithms, processes, and systems to extract, analyze and get insights from data.
Check out our exciting data science tutorials here.
If we were to see the relationship between all the above in a simple diagram, this is how it would look like –
Artificial Intelligence (AI)
Artificial Intelligence includes both Machine learning and Data science which are correlated. Thus, data science is also a part (the most popular and most important one) of AI.
As we see above, Data science and machine learning are closely related and provide useful insights and generate the necessary trends or ‘experience’. In both, we use supervised methods of learning i.e. learning from huge data sets.
How are both correlated?
Data Science is a broader field of study that uses algorithms and models of machine learning to analyse and process data. Apart from learning, data science also involves data integration, visualization, data engineering, deployment and business decisions.
Data Science vs Machine learning
So, what’s the difference?
On one hand, data science focuses on data visualization and a better presentation, whereas machine learning focuses more on the learning algorithms and learning from real-time data and experience.
Always remember – data is the main focus for data science and learning is the main focus for machine learning and that is where the difference lies.
To appreciate this difference more, let us take a use case and see how both data science and machine learning can be used to achieve the results we want –
Let us say you want to purchase a phone on xyz.com. This is the first time you are visiting xyz.com and you are browsing through phones of all ranges. You use various filters to narrow down your preferences and out of the results you get, you choose 4-5 of the phones and compare those. Once you select a phone model, you will see a recommendation below the product – for a similar product in a lesser price or with more features, or related accessories for the phone you have chosen and so on. How does the website recommend you these things? It has no history about you!
That’s through the data from millions of other people who may have tried to purchase the same phone, and searched/bought other accessories along. This makes the system automatically recommend the same to you.
The entire process of collection of data from the users, cleaning and filtering out the required data for evaluation, evaluation of the filtered data for building patterns, finding similar trends and building a model for a recommendation of the same thing to other users and finally the optimization – is data science.
Where is machine learning in all this? Well, how do you build a model? Through machine learning algorithms. Based on the data collected and trends generated, the machine understands that these are the accessories that are usually bought by other users with a particular phone. Hence, it suggests you the same thing based on what it has ‘experienced’ before.
The modeling (second last) step is the most critical step because that is what improves the overall business and makes the machine understand human behavior. If the right machine learning model is applied, it could mean more progressive learning for the machine as well as success for the business model.
This step is called as the data modeling step – which is essentially the machine learning phase of the data science lifecycle.
Data modeling – how does machine learning work?
There are different types of machine learning algorithms, the most common being clustering, matrix factorization, content-based, recommendations, collaborative filtering and so on. Machine learning involves the 5 basic steps –
The huge set of data that we receive in the first step is split into the training set and testing set and the model is built and test using the training set. A significant portion of data is used for training purposes so that different conditions of input and output can be achieved and the model built is closest to the required result (recommendation, human behavior, trends, etc…).
Once built, the model is tested for efficiency and accuracy using the test data so that it can be cross-validated.
As we can see, Machine Learning comes into picture only during the data modeling phase of the Data Science lifecycle. Data Science thus contains machine learning.
With machine learning, the machine can generate complex mathematical algorithms that need not be programmed by a human, and further can improvise and improve the programs all by itself. When compared to the traditional statistical analysis techniques, machine learning evolves as a better way of extraction and processing the most complex sets of big data, thereby making data science easier and less chaotic.
Furthermore, machines tend to be more accurate and have a better memory than humans, they can learn and produce accurate results based on experiences. We get fast algorithms and data-driven models without the errors that are possible by humans.
Data Science vs Machine learning: Head to head Comparison table
Here is a side by side comparison for easy reference and a quick recap of all that we have discussed and derived so far –
|It is an interdisciplinary field where unstructured data is cleaned, filtered, analyzed and business innovations are churned out of the result.||It is a part of data science where tools and techniques are used to create algorithms so that the machine can learn from data via experience.|
|It has a vast scope||It comes only in the data modeling stage of data science.|
|Data science can work with manual methods as well though they are not as efficient as machine algorithms||Machine learning cannot exist without data science as data has to be first prepared to create, train and test the model.|
|Data science helps define new problems that can be solved using machine learning techniques and statistical analysis.||The problem is already known and tools and techniques are used to find an intelligent solution.|
|Knowledge of SQL is necessary to perform operations on data.||Knowledge of SQL is not necessary. Programs are written in languages like R, Python, Java, Lisp etc…|
|Data science is a complete process.||Machine learning is a single step in data science that uses the other steps of data science to create the best suitable algorithm for predictive analysis.|
|Data science is not a subset of AI.||Machine learning is a subset of AI and also a connection between AI and data science since it evolves as more and more data is processed.|
How to choose between Data Science and Machine learning?
Well, you cannot choose one. Both Data Science and Machine learning go hand in hand. Machines cannot learn without data and Data Science is better done with machine learning as we have discussed above. In the future, data scientists will need at least a basic understanding of machine learning to model and interpret big data that is generated every single day.
If you are just starting your career or are from different backgrounds like Java or .NET, there is nothing to worry about. Data Science is vast but not difficult. Since it has many stages, a data scientist’s job is divided into different sub-fields. For one, check out the tutorials and start learning the basics. Once you have got the core concepts sorted, go deeper into machine learning and deep learning through the tutorial links given. Whether you have programming experience or not, you can become a good data scientist by learning the necessary tools and techniques to work on data and acquiring good domain knowledge.
People are also Reading
- Difference between Supervised vs Unsupervised Machine Learning
- Decision Tree in Machine Learning
- Machine Learning Algorithm
- Difference between Data Science vs Machine Learning
- Difference between Machine Learning and Deep Learning
- Best Data Science Tutorials
- Top 10 Python Data Science Libraries
- Top Data Science Interview Questions
- R for Data Science
- 10 Best Data Science Books