The Difference Between Data Science and Machine Learning

The Key Differences Between Data Science and Machine Learning

date_rangeMay 25, 2020

Fields like Data Science and Machine Learning have gained popularity in the past few years. Businesses are looking for new ways to improve their functions and are increasingly taking the help of Machine Learning and Data Science. Even though most people have heard of these terms, many of them consider them to be similar. In reality, Data Science and Machine Learning are related but not the same. In this post, let us look at each of them in detail and get a better understanding of their differences.

Data Science

According to an article on DOMO, 90% of all the world's data was created in the last two years. There are many reasons for so much data being generated. Today, most people have access to the internet as well as the mobile phone. This has made it easy for them to access social media sites like Facebook, Instagram, and Twitter, buy products online, and perform a lot of other activities online. Almost everyone uses Google search daily. All our online activities generate a lot of data. Many companies have learnt that data holds a lot of essential details that can help with the business and profits. This humungous amount of data has patterns that help with vital insights.

The creation of large chunks of data and the realization of its importance has given rise to the popularity of Data Science.

Data Science is an interdisciplinary field that combines Mathematics, Statistical Analysis, Algorithms, and Computer Engineering to find valuable insights in data. A Data Scientist works with large amounts of data from various sources, cleans it, processes it, and applies many methods on it to come up with patterns and trends. Data Scientists then share the information with business, and they use it to make decisions.

Some of the main processes in Data Science are:

Data Extraction

Data Cleansing

Data Analysis and Visualization

Generation of Data Insights

The main skills needed to work in Data Science are good Mathematics skills, Statistics skills, comfort with Algorithms, ability to work with a lot of random data, Research skills, and good communication skills.


Machine Learning:

Machine Learning comes under Artificial Intelligence and is a subject that deals with training machines and systems to perform certain tasks without human supervision. The systems also learn from their experience and hence the term Machine Learning. A simple example of the use of Machine Learning is movie suggestions on Netflix. When we watch a particular movie, Netflix suggests related movie names that we might be interested in watching. This is accomplished by Machine Learning algorithms, where the system learns about our likes and previous watch history and provides suggestions.

Some of the main steps in Machine Learning are:

Data Collection

Data Preparation

Selection a Model System

Training the Model System

Evaluation of the Model System

Tuning Parameters


The skills needed for Machine Learning are Mathematical Skills, Computer Programming, Statistics and Algorithms, Deep Learning, Logical Thinking, Problem Solving, Research skills, Data Science skills.


We have looked at Data Science and Machine Learning in detail. We can see that they are not the same, but they are related. Data Scientists use concepts of Machine Learning in their work, and Machine Learning Engineers use Data and data processing methods to train their systems. While we can say that they are different, both these fields are vital parts of Artificial Intelligence and are here to stay.

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