Machine learning and data science are two major key words of recent times almost all fields of science depend on. If data science is inevitable to explore the knowledge hidden in the data then machine learning is something bringing evolution through feature engineering. But the question is are they very different? In this article, these two fields will be discussed point by point where they are different and if there are any similarities.
The Venn Diagram of sciences
Now with these Venn Diagram structure, the association of all these fields are pretty clear. The lowermost circle essentially indicates the domain knowledge of a particular field. For example, it may be a field of agriculture crop production or population dynamics etc. A data scientist should know about his particular domain besides core knowledge of programming and statistics/mathematics.
Further you can see that data science is common to all three domains. Whereas machine learning lies in the intersection of statistics, mathematics knowledge and the sphere of hacking skill. The major difference between these two lies here. Data science being a more broad concept, requires special subject knowledge to analysis. Where as Machine Learning is more coding and programming oriented field.
Lets dive into elaborate discussion of these differences…
To start with let’s be clear about their domains. Data science is a much bigger term. It comprises multiple disciplines like information technology, modelling and business management. Whereas machine learning is comparatively a specific terms common in data science where the algorithm learns from the data.
Unlike data science, machine learning is more practical than empirical. Data science has much more extensive theoretical base and amenable to mathematical analysis. machine learning, on the other hand, is mainly a computer program based needs coding skills.
Lets first discuss these two fields first.
As we have seen in the above Venn Diagram, data science and machine learning have common uses. Data science uses the tools of machine learning to study transactional data for useful prediction. Machine learning helps in pattern discovery from the data.
Machine learning is actually learning from the data. The historical data trains the machine learning algorithms to make an accurate prediction. Such a learning process is called supervised learning.
There are situations where no such training data available. Then there are some machine learning algorithms which works without training. This type of machine learning is known as unsupervised machine learning. Obviously the accuracy here is less than the supervised one. But here the situation is also different.
Another kind of machine learning is known as Reinforcement learning. This one is the most advanced and popular machine learning. Here is also the training data is absent and the algorithm learns from its experience.
Deep learning is again a special field of machine learning. Lets discuss briefly about it too.
Deep learning is a subfield of Machine learning which is again a subfield of Artificial Intelligence. In this context deep learning deals with the data as machine learning does; the difference lies in the learning process. Scalability is also a point where these two processes are different from each other.
Deep learning especially a superior method when the data in hand is very vast. Deep learning is very efficient in taking benefit of the large data set. Contrary to machine learning models or other conventional regression models, where the models’ accuracy does not increase after a certain level. The deep learning algorithm goes on improving the model by training it with more and more data.
The deep learning process is a black box method. That means we will only see the inputs and the output. What is going on in between, how does the network work remains obscure.
The name deep learning actually refers to the hidden layers of the training process. The backpropagation algorithm takes the feedback signal from the output to adjust the weights used in the hidden layers and refines the output in the next cycle. This process goes on until we get a satisfactory model.
We can consider data science as a bridge between the traditional statistical and mathematical science and their application to solving real-world problems. The theoretical knowledge of basic sciences many times remains unused. Data science makes this knowledge applicable to solve practical problems.
More lightly, we can say that a data scientist must have more programming skill than most of the scientists and more statistical skill than a programmer has. No surprise that just mention of data science in anyone’s CV makes him eligible for an enhanced pay package.
Since in almost all organizations are generating data in an exponential amount, they need data scientists to get meaningful insights out of that. Moreover, after the explosion of internet users, the data generated online is enormous. Data science applies data modelling and data warehousing to keep track of this ever-growing data.
Necessary skills to be a data scientist
A data scientist needs to be proficient in both theoretical concepts as well as programming languages like R and Python etc. One person with a good understanding of the underlying statistical concepts can only develop a sound algorithm for its implementation.
But a data scientist’s job does not end here. These two core subjects knowledge is essential no doubt. But to become a successful data scientist a person must provide a complete business solution. When any organisation appoints some data scientist, they are supposed to analyse the data to gain insights about the potential business opportunities and provide the roadmap.
So, a data scientist should also possess knowledge of the particular business domain and communication skill. Without effective communication and result interpretation, even a good analytical report may lead to a disappointing result. So none of these four pillars of success is less important.
The four pillars of data science
I got a good representation of these four pillars of data science through Venn diagram from this website which was originally created by David Taylor a Biotechnologist in his article “Battle of Data Science Venn Diagrams”.
These four different streams are considered as the pillars of data science. But why so? Let’s take real-world examples to understand how data science plays an important role in our daily lives.
Example 1: online shopping
Think about your online shopping experience. Whenever you log in your favourite online shopping platform you get deals on items you like. Items are organized according to your interest. Have you ever thought how on the earth the website does that?
Every time when you visit the online retail website search for things of your interest or purchase something; you generate data. The website stores historical data of your interaction with the shopping platform. If anyone with data science skill analyses the data properly he may know about your purchase behaviour even better than you.
Example 2: Indian Railways
Indian Railways is the fourth-largest network in the world. Every day thousands of trains are operated through which crores of passengers travel across the country. It has a track length over 70,000 km.
So, quite such a vast network generates a huge amount of data every day. The ticket booking system, train operation, biometrics, crew management, train schedule in every aspect the data generated is big data. And if we consider the historical data is no less than a gold mine of information on Indian passengers’ travel trend over the years.
Application of data science on this big data reveals very important information to enable the authority to take accurate decisions about during which season there is a rush of passengers and additional trains need to run; which routes are profitable, running special trains and many more.
So in a nutshell, the main tasks of data science are:
- Filtering the required data from big data
- Cleaning the raw data to make it amenable to analysis
- Data visualization
- Data analysis
- Interpretation and valid conclusion
As we discussed all of them at length, we came to know that in spite of many similarities these two subjects have some differences in their application. So, now its time to point out the specific differences between machine learning and data science. Herre they are:
|Data science||Machine Learning|
|Based on extensive theoretical concepts of statistics and mathematics||Knowledge of computer programming and computer science fundamentals are essential|
|Generally performs various data operations||It is a subset of Artificial Intelligence|
|Gives emphasis on data visualization||Data evaluation and modelling is required for the feature engineering|
|It extracts insights from the data by cleaning, visualizing and interpreting data||It learns from data and finds out the hidden pattern|
|Knowledge of programming languages like R, Python, SAS, Scala etc. is essential||Knowledge of probability and statistics is essential|
|A data scientist should have knowledge of machine learning||Requires in-depth knowledge of programming skills|
|Popular tools use in data science are like Tableau, Matlab, Apache Spark etc.||Popular tools used in machine learning are like IBM Watson studio, Microsoft azure ML studio etc.|
|Structured and unstructured data are the key ingredients||Here statistical models are the key players|
|It has its applications in fraud detection, trend prediction, credit risk analysis etc.||Image classification, speech recognition, feature extraction are some popular application of machine learning|
To end with I would like to summarize the whole discussion saying that, data science is a comparatively newer field of science and of great demand across the organizations. Mainly because of its immense power of providing insights analyzing big data which otherwise has no meaning to the organisations.
On the other hand machine learning is an approach which enables the computer to learn from the data. A data scientist should have the knowledge of machine learning in order to unravel its full potential. So, they do have some overlapping parts and complimentary skills.
I hope the article contains sufficient discussion to make you understand the similarity as well as difference between machine learning and data science. If you have any question, doubt please comment below. I would like to answer them.