Both the terms Data Science and Machine Learning have become the talk of the tech town nowadays. Though both the words are often used together in most cases, they are not synonyms by any chance. It’s true that both of them are interconnected; still, each of them has its own purpose and functionality.
Anyway, if you are one of those fellow tech enthusiasts wondering to know about data science certification and Machine Learning in brief, we assure you, you have landed in the right place. Let’s get started without further due!
In general, data science is a concept mainly used to collect, prepare, clean, and analyze big data for a specific purpose. To be more precise, data science is the process and analysis of the data a user generates for various insights on a particular platform that serves for a number of business purposes later on.
Now, to make things clearer, let’s assume that you logged in on a website that sells different products or services. As most of the visitors on the website do, let’s suppose you too browsed through several products or services available for the customers on that platform.
Thus, you are generating data on that particular website, like most of the visitors do when they login to that site. A data scientist at the back end of the website uses the data you are generating to understand your behavior and then show you the deals on the products or services you were browsing through retargeted advertisements and pursue you to buy their products or services.
In such cases, a data scientist gathers user data from multiple sources then applies machine learning, sentiment analysis, predictive analytics, etc. to extract valuable information from the collected data that ultimately benefits the business. Usually, data science includes the following processes:
- Data extraction
- Data Cleansing
- Data Analysis
- and Actionable Insights Generation
First of all, machine learning is a part of data science that has the ability to process collected data autonomously. Yes, you read it right! Machine Learning draws aspects using the algorithms and statistics given in the system to work on the user-generated data, which is extracted from multiple resources.
Machine learning combines a number of complex algorithms and techniques, including supervised clustering, regression, naïve Bayes, and more to process the generated data sets on its own without any human intervention.
To make things clearer, let’s assume that you logged in on a website that streams videos of different genres. Now, after you watch a couple of different videos on that platform, you will find that the site is suggesting more videos to watch based on your preferences. Thus, machine learning creates a handy model on its own using the available data and serves the best possible solution.
When you scrolled down to this particular section, we expect that you read between the lines and come to know about the terms Machine Learning and Data Science in the most straightforward manner, as presented above. Hope you found the post helpful and enjoyed reading!