Descriptive Analysis in data science is actually a term offered to the evaluation of info which allows describing, show or perhaps summarize the info in a major way, that way, for example, patterns can come out starting from a data. Descriptive analysis in data science is a part of Data Analysis and comes after data collection.
We have already covered what is Data science & what is Data collection. Let’s now move to Data analysis & the type of data analysis we have in the field of data science.
Data Analysis is truly a method of checking out, cleaning, transforming along modeling details with all of the aim of obtaining information that is useful and supporting decision drawing and making conclusions. So if go back to our 1st blog in this series, when we discussed or rather I spoke about Data science. The crux of Data analysis and data science is the same. You are trying to help your organization or trying to help your clients to make decisions for a certain line of business.
For example, your organization is planning to hire more people but they want to analyze if they will have that much of business in the future. So basis the data you will get or the information that you have, you will do data analysis and at the end when a conclusion is drawn you will be able to provide information to your management that yes we can hire these many people. As per my analysis, we will have this much of work during this period of time so we may need more people. This is how it helps your organization to plan decisions for the future.
Types of Data Analysis:
We have got primarily two types of Data Analysis:
- Descriptive Analysis and
2. Predictive Analysis
You can have other types of Data Analysis as well but trust me if you know these two, you can handle any sort of data, anywhere in any organization.
Descriptive Analysis: As the descriptive word is self-explanatory, it describes the data. Let me just read out the definition :
“It’s a phrase given to the evaluation of information which helps describe, show or even summarize information in a significant manner like that, for instance, patterns could come out from a data”
Descriptive analysis is simply a way to describe our data. So basically descriptive analysis is done on something that happened. Quoting an example: when you go to a doctor, you will tell your doctor that I am not feeling well or I feel weakness or something like that. Then your doctor will start asking questions like if you’re feeling feverish or if you had a cough, cold or anything, you ate from outside or any sort of information. The moment you provide that information to your doctor, he/she will be able to draw a conclusion for the reason behind your weakness. It is then the doctor will prescribe medicine or syrup depending on whatever symptoms that you have shared or whatever issue you may have with your body. That is a perfect example of a descriptive analysis.
Another example would be in shares trading. So let’s say a stockbroker has reached out to you stating that his stocks earnings are doing well and if we can do some analysis on it.
In the first step, you will ask questions to your broker like:
1. What has transformed in the previous 6 months?
2. Is there any government policy that has come into existence which may have impacted the outcomings?
Basis the information that you will gather from your broker client, you will be able to tell what might be the reasons your stocks earnings doing well or bad. This is a descriptive analysis. In the next topic, we will be talking about predictive analysis.
