Data Science Hierarchy of Needs
The Data science hierarchy of needs or pyramids simulated after the Maslow’s Hierarchy of Needs describes the various steps and concepts needed to derive the best profits and benefits from AI actualization,”the Self Actualization”in Maslow’s hierarchy of needs and how to reach AI Actualization.
It details the numerous methods,algorithms,tools and resources required when doing data science.
In order to arrive at the pinnacle we will need the basics and the fundamentals such as data. Data is the basic unit of Data Science and AI. So we need to know how to collect the data which is the Data Collection.
And then we can move on to the next step on the ladder or pyramid, which includes Data Flow( Data Storage ,E.T.L.,Exploration and Analysis, Model Building and Deployment ,etc
Let us see how to it is and how to identify the various perspective of how to look at it.
To make the most of data, you will need to combine two different perspective when looking and working with any data. First of all there are two ways people look at data. Either they can see from the perspective of a programmer or data scientist or ML Engineer or they may see it from the perspective of a business enterprise person. All of these perspective are very essential in deriving benefits from data.
Most engineers look at it from the bottom up,ie they concentrate on how the data will be collected, stored ,accessed and then analyzed to derive insight. They mostly concentrate on the engineering aspect till the derivation of insight and that is all.
On the other hand , an enterprise person or business folks would like to know the profits they will gain from the data . They are more concerned about the profits they can get from the data.
But the best approach to this data science pyramid is to combine both perspectives.
You will have to know how the data is collected,the data flow and the various data analysis done to derive useful and profitable insight and then how to use this insight to influence your decisions on making profits.
Below is a video of the entire description of the data science pyramid.
Thanks For Your Time
Jesse E. Agbe(JCharis)