If you are a learner to the big data industry, then i am damn sure you will be get confused with these two terms. That is data science and data analytics. These two terms seems to be pretty similar to each other, but as your secondary mind thinks, this two topics are having huge variations between them. So this article is for the novice people who are getting struggled to understand the difference between data science and data analytics..
The Similarity Between Data Science and Data Analytics
Before understanding the difference we have to fully accept the similarity between these two fields. Yes, these two fields are totally focused on analyzing the data. This is the only and primary similarity to both data science, and data analytics(business analytics). But, inside the data engineering cycle, this two process starts to vary between each of us, as they are occur in two different phases.
Data Analytics :
It is a systematic process, to extract valuable information’s various structured and unstructured data source. Getting the past, present and future business performance through collected business information’s. Determining and explaining the best statistical &data driven business model to the concern business owner
In simple terms :
Collecting, extracting, visualizing the business insights from various structured and unstructured business data, and helping business owners to take timely, data driven, and logical business decisions.
Data science :
Designing, developing, and deploying logical, automated, machine learning algorithms that should support any business intelligence tools inorder to analyze a huge volume of data. it is the foundation for data analysis, through which an applied business problem can be solved.
Image source : The Ultimate Comparison: Data Science vs Analytics
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