Machine learning and data science are the twenty-first century’s buzz words. These two terms are interchangeably thrown around but should not be mistaken as synonyms for each other. As both of them have many features in common, they can not be replaced by each other. Both are different tools to operate on.
Machine Learning vs Data Science
The main difference between Machine Learning and Data Science is that Machine Learning is a group of techniques that allow computers to learn from the data. On the other hand, Data Science is the field of study that aims to extract meaning and insights from data. Machine learning is the discipline under data science, while data science has multiple disciplines in it.
Machine learning is a bunch of techniques that are used by data scientists to allow computers to harvest meaningful data and use it. This way, computers produce good performance results without explicit programming rules. Machine learning is included in data science.
Data science is a field of study that uses a scientific approach to fragment data into meanings and get insights from that. It can be described as a combination of information technology, modelling, and business management. Although data science is used interchangeably with machine learning, it is a huge field.
Comparison Table Between Machine Learning and Data Science
|Parameters of Comparison||Machine Learning||Data Science|
|Definition||Machine Learning is a group of techniques that allow computers to learn from data.||Data Science is the field of study that aims to extract meaning and insights from data.|
|Based On||Combination of machine and data science.||Analytics and statistics.|
|Use||Machines utilize techniques to learn without being explicitly programmed.||Branch dealing with data.|
|Demands||Focused only on algorithm statistics.||It is a wide term including algorithm statistics and data processing.|
|Category||Included in data science.||It is a broad field with multiple disciplines.|
|Operations||Is of three types, unsupervised learning, reinforcement learning, supervised learning.||It includes data gathering, data cleaning, data manipulation, etc. |
What is Machine Learning?
It is the field of study included under data sciences, which allow computers to learn from data without being programmed. It is applied using algorithm statistics to process gathered data and prepare for future predictions without any human intervention. To allow these, computers need the input of a set of instructions or data or observations.
The strengths of machine learning make it useful in different industries. It has shown its potential by saving lives in healthcare and solving complex problems in computer security, and more.
Even though there are a lot of limitations of machine learning. Engineers and programmers need to constrain and optimize the input algorithms to make them more efficient. A traditional equation can solve a problem very easily, but the involvement of machine learning may lead to complications rather than simplification.
Machine learning engineers need strong skills in computer science fundamentals, data evolution and modelling, understanding and application of algorithms, natural language processing, text representation techniques, etc.
The application of machine learning in various fields can provide lucrative solutions to many problems. But applications in industries like lending, hiring and medicine raise some ethical concerns. As the algorithms are created and operated by humans, they incorporate hidden social biases.
Companies like Google Facebook work on machine learning.
What is Data Science?
It is a field involving the study of huge amounts of data in an organization’s repository. This study is important for organizations to gain information about business and market patterns. The data can be structured or unstructured. It is used extensively by companies such as Netflix, Amazon, airlines, internet search, etc.
Due to digitization and smartphone availability, the internet is loaded with enormous amounts of data. Also, because the massive use of the internet has made it cheaper, computing power has dramatically increased while cost has decreased. Data science uses both of the components to derive insights into trends.
The huge leap in data resources spurred the availability of genuine resources. With a small dataset, messy data or incorrect data, data science is useless and will waste a lot of time. It also creates misleading results that are meaningless. Data science will fail to explain the variation if data do not have an actual cause.
To become a successful data scientist, a person should have skills like statistics, data mining and cleaning, programming languages like R and Python, SQL databases. People also need to know tools like Hadoop, Hive and Pig.
Main Differences Between Machine Learning and Data Science
- Machine learning is one of the tools used by data scientists, while data science is the field of study involving data gathering, data processing, Etc.
- Machine learning is a hybrid of data science and machine, while data science mainly involves analytics and statistics.
- Machine learning only focuses on algorithms statistics, while data science focuses on many more aspects of data rather than just algorithm statistics.
- Machine learning is of three types: unsupervised learning, reinforcement learning, supervised learning, while data science includes data gathering, data cleaning, data manipulation, etc.
- Machine learning is a part of data science, while data science is a multidisciplinary field.
The widespread use of computers made data science a popular tool to analyze business or market trends. Also, machine learning is providing lucrative solutions to many problems without human interventions. Both of these are wide fields with great applications.
The growth of these fields is making the best and most popular job attributions. The technologists working in the field of machine learning are called Machine Learning Engineers, while technologists working in the data science field are called data scientists.
In these fields, humans can harness huge amounts of data and process it in certain ways to gain potential insights into the future. The applications of both of these fields are vast but not unlimited. Both of them have their limitations to overcome. Also, these fields require highly skilled employees and quality data to give meaningful results.