Before starting, it is important to note that all posts related to this matter are the result of a self-study based on different sources (indicated in the bibliography) but focused on the content made available in the course “The Data Science Course 2020: Complete Data Science Bootcamp” created by 365 Carrers and available on the Udemy online learning platform.
Before learning more about data science and machine learning, it is necessary to understand the not so obvious differences between the term’s analysis and analytics due to the similarity of the words.
Most people believe these terms have the same meaning, but technically this isn’t correct. There is in fact a distinct difference between the two. The reason one is often used instead of the other is the lack of a transparent understanding of both. So, in this post, I will clarify the difference between the two terms.
Consider a huge data set containing data of various types.
Instead of dealing with the complete data set and running the risk of becoming overwhelmed, separate it into individual datasets (next figure), study them and examine how they report to other parts, and that’s an analysis.
It is important to remember that you perform analysis on things that have already happened in the past, such as, using an analysis to explain how a story ended the way it did or how there was a decrease in company sales last year.
All this means that we do analyses to explain how and/or why something happened.
Analytics generally refers to the future (explore potential futures) instead of explaining past events.
Analytics is essentially the application of logical and computational reasoning to the component parts obtained in an analysis, and in doing so, you look for patterns to explore what you can do with them in the future.
Analytics branches off into two areas.
Uses your intuition and experience together with analysis to plan your next business move.
Apply formulas and algorithms to the data gathered from your analysis.
Imagine you are the owner of an online clothing store. You are ahead of the competition in understanding your customers’ needs and also what they want. You’ve performed a very detailed analysis from men’s clothing articles and feel sure about which fashion trends to follow, so you may use this intuition to decide on which styles of clothing to start selling. This would be qualitative analytics but you might not know when to introduce the new collection.
In that case, relying on past sales data and user experience data you could predict in which month it would be best to do that. This is an example of using quantitative analytics.
 Udemy – The Data Science Course 2020: Complete Data Science Bootcamp – 365 Careers