Microsoft Excel is an amazing tool that 750 million people use for their work and studies. However, some people do not consider using it to analyze large sets of data. Excel has some limitations in terms of the number of rows on a spreadsheet, which is only one million. In fact, there are millions and billions of rows of big data, therefore, people could say that it is hard to integrate all the data in one file. In this article, keySkillset will show you how to use Excel for Big Data and demystify the statement.
First of all, consider a data set of millions of visitors to your website that share millions of likes and each like generates a particular option price. Your goal is to investigate the data and find different trends or patterns that might interest the company. Based on that you will be able to build your future strategic goals. The question is how to analyze efficiently the data without any third party experts.
There is no need to use all billions rows of data when you can take representatives or data samples. It is the same concept when we conduct interviews and choose a sample of population to find out certain patterns. However, we have to make sure that the sample we pick will answer at least three of the main questions. For example, we need to know the number of records we want to extract, the modality of extraction as well as the reliability of those data sets.
How big is the data set?
Consider a data set of 500 million records and we need to extract a maximum of 1 million. There are two broad types of sampling: non-random and random, which we will use in our case because we want to approximate the probability of something happening in a large set of data. We can do the random selection with bernouillan formula by using the confidence interval, population size and error level.
How to extract the right random sample data set?
The best solution to get the sample of records is the sampling statistics. In Excel you should enter the formula =RAND()*[Dataset size] and transform the formulas in values. Then, remove the decimals, make sure there are no duplicates and you sort the range. That is going to be the sample of random records to work with. If you are ready to accept the approximation, then you can be hands-on with the data again. The size of the sample that we obtained helps us to manage it in Excel.
How reliable are the data sets?
There is an evidence of proving the reliability of the data set. For example, we established a couple of characteristics for the initial set of records. After that, we extracted the main characteristics of the sample set and compared with the previous records. At the end, we created several tests to confirm and compare the measurements on both data sets.
This is an easy way to integrate the advantages of Excel to analyze the big data. Today, there is a big demand for skillful analysts that can operate with data.Therefore, you should start considering to sharpen your abilities to work with both software coding and statistics. One of the tools that will also help in that is the Excel software, so enjoy the use of Excel for big data to make wise business decisions.