What is data analysis and why is it important?

What is data analysis?
By definition, data analysis is the science of discovering and interpreting the meaning of data and putting the acquired knowledge into practice. It's the link between big data and decision making.

Take the example of Bob's business. He founded his small business: "Bob's Socks" and opened a small shop. However, he quickly understood how frustrated people were because they found it difficult to find what they needed.

Awesome, Bob managed his online business. He created an online shop. In order to place an order, people had to create a profile.

Then Bob had already found a way to collect user data. However, being just human and checking hundreds of profiles and jobs isn't an option.

Here Bob would have to use data analysis. Hire Steve, a data analyst.

Steve uses computer programming, operational research and statistics to translate the data into plain English. Now Bob knows that baby-size cartoon socks, for example, are very popular with women over the age of 20.

With the right tools, you can optimize your website. This group will see a variety of colorful baby socks on the blanket that will help Bob's business grow.

Although the technology behind the analysis is quite complicated, the concept is easy to understand. The methods of collecting and processing data vary by area. However, positive results are consistent. So that's it:

When Fortune 1000 companies use data analytics methods, their operating margins increase by up to 60%!
Even if they only improve data processing by 10%, they could still generate additional revenues of USD 65 million per year.
* Data analysis vs data analysis
It is a common mistake to think that analysis and analysis are synonyms and can be used interchangeably. Well, they are not. If you need to know, these are really paronymous.

Steve knows. In fact, his hard and thorough work has helped the company expand into another niche, albeit a little different than Bob would have thought. In all cases, the stock of "bob socks and stockings" is out of stock.

BA dum TSS.

Now what is the difference between data analysis and analysis since this is no longer the case?

The analysis is used to understand the past. Or "Is my new product successful?"

Analytics focuses on the reason for the results. It is also used to predict how things will develop in the future.

In other words, companies observe and learn through analysis and develop their strategies through analysis. The former therefore require more computing power: data analysts are not only experts in statistics, but also in programming and mathematics. You can use these tools to create complete prediction models.

* Data analysis vs data science
It is not as complicated as the previous one. Now that you know what data analysis is, let's focus on data science.

Data science is a general term for a group of areas that deal with the cleansing, preparation, and analysis of data. Your main goal is to find a way to collect information and to derive meaning from it.

The main difference between data science and data analysis is the scope. You can think of the analysis as the "most restricted" version of data science because it addresses certain problems that can be solved immediately. Data Science looks for ideas but does not explain the reasons for them. Expressed differently:

The data analysis answers questions about data science.

And as everyone who has read the "Galaxy Hitchhiker's Guide" knows, asking the right questions are just as important as knowing the answer.

* Data Science vs. Big Data
It is probably the most intuitive of all comparisons.

Data sets that are too large to be processed with conventional tools are considered big data. They consist of massive amounts of structured, semi-structured and unstructured information.

Big data itself is useless: it is chaotic and impossible for a human to decipher.

This is where data science comes in. Use a variety of tools to extract, categorize, and analyze big data. Find patterns in seemingly meaningless information and provide businesses with valuable information.

So if big data is a dough, data science is the cook who prepares the pizza to please you.

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