Machine Learning vs. Data Science – Key Differences and Similarities

Technology moves at breakneck speed, and we now have more power in our pockets than we had in our homes in the 1990s. We’ve come a long way since then. Machine learning and Data Science are two tech buzzwords that have everyone’s attention today. With big data becoming so prevalent in the business world, a lot of data terms tend to be thrown around. Many of us don’t understand what they mean. What is data science? Is there a difference between machine learning vs data science? How do they connect to each other? Isn’t machine learning just artificial intelligence? All of this questions comes in my head. So today in this blog I m making you clear all the difference between Data Mining and Machine Learning. So, Let’s start.

Machine Learning

Machine Learning is a collection of methods whose purpose is to provide software with the ability to learn. It is a type of “deep learning” that allows machines to process information for themselves on a very sophisticated level, allowing them to perform complex functions like facial recognition. It is a subdomain of artificial intelligence.

Data Science

Date science is an interdisciplinary field of systems and processes to extract information from data in many forms. It is an umbrella term for various techniques and methods that help data scientists transform data into actionable insights. Which may have a massive impact on the bottom line of organizations. It builds and modifies Artifical Intelligence Softwares to obtain information from huge data clusters and data sets.


In data science, Both machine learning vs data science generally fall under that umbrella.Data Science is a relatively new career path, with people coming from various different backgrounds, defining what is ‘Data Science’ is particularly challenging. It is a part of Data Science, an area that deals with statistics, algorithmics, and similar scientific methods used for knowledge extraction.

Data Science is used in so many industries of applications such as banking and financial sector, healthcare, retail, publishing and social media, etc. It is used by Google and Facebook to push relevant advertisements based on users search history. The main advantage is that engineers can apply it in the Data Mining (analysis) stage.


We can easily see how the concepts of machine learning vs data science have become confused. Both of them share the same foundation. Also certainly there is overlap between the two. Engineers use ML models to replace hard, explicitly-coded decision-making processes by providing similar procedures learned from data. It offers Smart solutions for the organization. However, ML models have specific characteristics that help provide useful insights into the collected data. For example, organizations that implement ML models can get their hands on insights that focus on similarities between data entries (for example, people with similar names, address etc), the relative importance of particular type of information (such as specifying whether a person’s surname is vital in their identification), or even the quality of the data with the aim building better models.

Leave a Reply

Notify of