We are living in a world where we rely on machines/technology to get every task done. Nowadays, everything has shifted to digital platforms.
We use mobile phones, computers, smart devices, and all other electronic devices on a daily basis. We also refer to them as machines. These machines are programmed to work for us, and intelligence is imparted into them. This process is referred to as machine learning.
While using these modern-day machines or technologies we also generate and exchange a lot of data, which we refer to as big data. This brings the challenge of data management.
The data collected is worked upon by a group of people. This analysis of data with the help of different tools, algorithms, and machine learning principles is known as data science, and the people who work upon these data are reckoned as data scientists.
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“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”
- Andrew Ng, Cofounder of Coursera
In this blog, we will discuss these two different concepts in detail. We will try to discern the meaning of machine learning.
We will also discuss what is data science and will also try to learn about the role of data scientists. Later in the blog, we will look at the differences between machine learning and data science. So, let us begin with the definition of Machine learning.
Machine Learning, as the name suggests, is concerned with learning for machines. It is one of the AI branches, that focuses on building applications that learn on their own, over a time period. It makes machines capable of taking care of themselves and improving their performance and accuracy.
“Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.”
- Tom Mitchell, American Computer Scientist
The computer algorithms mentioned here are the commands/instructions and a set of rules specified by the programmer for the computer to understand and process.
In simpler terms, it is like teaching a student to learn a language, which he can identify when he hears those words again.
Computers are taught algorithms to identify and differentiate situations. Machine learning algorithms are trained to identify patterns and features in a massive amount of data.
This helps them to make better decisions and predictions based on the pattern identified. So, a better algorithm will give more accurate predictions as it would have processed more data.
Computers or machine learning systems are fed these algorithms which they use to perform different tasks. We can also call these algorithms ‘recorded commands’ which the machine learning systems hear when asked to perform a task.
Isn’t it largely similar to teaching your child how to act in a particular situation?
Having discussed the definition of machine learning, we will now look at the definition of data science. This will set the tone for understanding the differences between the two concepts.
Data Science is a highly emerging field. It is growing at a quick rate and is unstoppable with the increasing use of technology. It has already exhibited such enormous possibilities that a wider definition is required to understand it.
However, in its most basic terms, data science is the process of obtaining any sort of valuable information and insights from given data.
Data science is the domain of study that deals with a vast amount of data making use of different modern tools and techniques. It uncovers hidden patterns, derives meaningful information to make business decisions.
The data that is worked upon by data scientists is from different sources and in different patterns. Data science uses complex machine learning models to give predictive models.
Nowadays, the amount of data that is being collected on a daily basis is almost ten times more than the amount of information collected and processed in the history of humankind.
Research in 2013 revealed that a full 90% of all the data in the world was generated over the last two years. This large amount of data is crucial for businesses in many ways. So, data science has become an important field for organizations.
Till now, we have discussed the two concepts. Now we will be looking at their work as it is one more area to differentiate these two concepts.
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Machine Learning uses different techniques to handle large and complex amounts of information to come up with a prediction or decision.
Machine learning systems or computers are fed different algorithms to perform different tasks. These algorithms are not in the form of sentences, instead, they are a set of binary digits, as computers don’t understand the language of humans.
In practice, these patterns are difficult to explain. We can look at an example to understand the working of machine learning.
Have you ever wondered how we get relevant results when we search for anything over the internet?
It is machine learning that makes the system capable of identifying patterns and giving accurate results. There are several machine learning applications in our daily life.
For example, when we search for a dog image in google search, it looks for a large number of examples of images labeled as dogs. The machine learning system then identifies patterns of pixels and patterns of colors that help it guess (predict) if the image queried is indeed a dog.
Before this whole searching event, a testing session would have been carried out where the computer makes a guess about the possible patterns and pixels required to identify a dog’s image. On the off chance, the system fails to make a correct guess, a few adjustments are made so that it gets it right the next time.
In the end, such a collection of patterns absorbed by the main computer system modeled after the human brain (deep neural network), that once is trained, can correctly identify and bring accurate results of dog images on Google search. It can also bring along anything else that you could think of — such a process is called the training phase of a machine learning system.
So, this is how machine learning works. The system is trained and tested multiple times before it is presented to us to make use of it.
Data science requires a plethora of disciplines and areas of expertise to extract crucial information from raw data.
A data scientist must be skilled in everything from data engineering, math, statistics, advanced computing, and visualizations to be able to differentiate important bits of information from muddled masses of information. This helps in driving innovation and increases efficiency.
Data science heavily relies on artificial intelligence and its subfields like machine learning and deep learning. It makes use of different tools and technologies like computer algorithms to create models and make predictions.
Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, and more.
It includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as big data.
The five-stage cycle of data science
Data science has a five-stage life cycle that consists of:
Capture: Data acquisition, data entry, signal reception, data extraction
Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture
Process: Data mining, clustering/classification, data modeling, data summarization
Communicate: Data reporting, data visualization, business intelligence, decision making
Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
- Hal Varian, Economist
One of the disciplines of Data Science
Broad term with multiple disciplines
Techniques used are regression and supervised clustering
Data generated may not be specifically from machines
Makes use of computer algorithms
Looks after whole data processing technology
Used to make machines intelligent
Used for bringing structure to big data
Uses algorithms to feed intelligence
Uses machine learning as a tool to extract information
The two concepts may seem to collide on most occasions but they are different. We have listed the differences below:
Data science is a broad term for multiple disciplines whereas Machine learning fits in one of those disciplines i.e. it fits within data science.
Machine learning leverages different techniques like regression and supervised clustering.
On the other hand, the ‘data’ in data science may or may not have evolved from a machine or through a mechanical process.
The critical difference between the two is that data science, being a broader concept, not only focuses on algorithms, data, and statistics but also looks after the whole data processing methodology.
Machine learning has to make use of algorithms to feed computers.
Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes that suit businesses’ needs.
Machine learning, on the other hand, is concerned with imparting knowledge to machines.
Data science uses machine learning as a tool to extract crucial information and insight from raw data while machine learning makes use of algorithms to feed intelligence.
In the blog, we discussed that Machine learning and data science are among the top trending concepts these days.
Machine learning deals with teaching computers to be able to learn over a time period and improve their performance and accuracy.
Data science, on the other hand, is concerned with all sorts of data processing methodologies. It is the process of obtaining valuable insights from raw data.
These two modern-day technologies serve the same purpose and that is to make our lives easier and better. Moreover, they perform different functions.
We also learned that data science is a broader concept, takes care of data extraction, focuses on algorithms and statistics. Machine learning, on the other hand, focuses only on computer algorithms and making machines learn these algorithms. Machine learning is also reckoned as a sub-topic of data science.
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