Sampling Models In Data Science

Why Do We Need Sampling?  We do Know in Inferential Statistics we deal with samplings that we take a sample data not the population data. This is because the population data is too large to handle and very complex to handle. 

Sampling Funnel is that it performs an analysis of the population. In general, we don't have access to perform any analysis of the population. So here with the use of sampling funnel, we can actually perform analysis over the population to know positive and negative areas of that product in business analytics.

To perform a general analysis of a particular product, it is difficult to analyze the whole population, because the use of a particular product/service by the people is widely spread all across the globe. It is difficult to spot out the people who are using a particular product/service. So we use sampling funnel method which we implement in data science and it can be conducted in the form of a survey to collect positive and negative feedback from the population.

Here the process of performing analysis which I have listed in the following serial steps 

Population

Sampling Frame

SRS (Simple Random Sampling) and finally 

Sample

The term population refers to a whole and the term sampling frame is that identifying the high use of product/service in various geographies. And the next serial step is Simple random sample, it is said to be known as picking up people to get feedback randomly (Blindfolded). Finally, by deriving the report followed by the serial steps described by the sampling funnel to get a sample. So this why we implement sampling funnel in Data science.

In short, Data science can be used extremely at exit poll results.

For your better understanding to gain practical knowledge and for your effective learning, one should be dedicatedly allocated time to put it into practice. An example of IPhone will be the right one to pick and to demonstrate about the process of performing sampling funnel. If you look right back to the days, we may find those days where people used to stand in a queue out of the store to purchase a major mobile brand “Apple Iphone”. And indeed it`s difficult to perform a survey with a questionnaire. Collecting opinions & feedback by asking people with a loaded set of questions will no longer work. Even to perform post analysis and to retrieve the outcome of the huge data, it is messy to understand and to conclude the quality of the product and its performance.  

Many tricks & techniques and many logics are incubated to perform the required analysis ensuring all the systems are meeting the needs  of the task. 

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