What Is a Data Analysis?

What Is a Data Analysis?

While we are on the topic of deep learning, we need to take some time to dive into another topic that is closely related. When we were talking about deep learning a bit above, we mentioned quite a bit about data, and how this data is meant to help train and test the models that we try to make with the use of deep learning. In this chapter, we are going to talk about data analytics, which is the process of not only gathering the data but sorting through that data and finding what predictions, insights, and information is inside.

Data analysis can happen in many different ways. But since most of these analyses are done on large sets of data, also known as big data, it is hard for a human to manually go through all of that information and learn what is inside, especially in a timely manner. This is where deep learning comes into the mix and helps us to do a good analysis in no time. With that in mind, let’s dive in a bit and take a closer look at what data analysis is all about.

Data Analysis:

There is a ton of data that is available in our world. Companies like to spend time collecting information from their customers, data from their social media accounts, and data from other sources in their industry. This data is going to help them to really get a competitive edge if they use it the right way and can ensure that they release the right products while providing good customer service in the process.

The problem is that after you collect your data, you then have to be able to go through all of that data. This is not an easy thing to do all of the time. There is often a ton of data, and figuring out how to go through it, and what information is found inside the data is going to be hard. Gathering all of the data that you need and then not following through and analyzing it is such a waste, and you might as well save time and not go through with this at all. But if you have gathered the data, we need to be able to go through and analyze the data in order to make smarter business decisions. And that is what we are going to spend time on in this guidebook.

Data analysis is going to be a practice of a company where they take the raw data and then order and organize it out in a manner that we are able to look through all of the useful information that is in there is extracted out to be used. The process of organizing and then being able to think about the data is going to be the key to understanding what the data does, and what it doesn’t, contain. There can be a lot of data that the company provides you, but if you are not able to go through it and see what information is inside of it, then the data is not going to help you, and you will have no idea what is inside the data, and what is not inside of the data.

Now, when we are working with data analysis, there are going to be quite a few methods that we can add here in order to handle the analysis, and make sure that we will see some results. However, no matter what method you choose to go with, there has to be some caution with how we want to manipulate the data. We don’t want to end up pushing our own agendas and conclusions on the data. We want to see the actual insights and information that are found inside of all that data.

Yes, you will go through this process with some questions that you want to be answered, and maybe also a hypothesis about what you are going to find in the data. But if you are careful, you will not open up your mind to all of the information that is found in that data, and you will miss out on valuable insights and predictions that your business needs. And how much worth or value will we assign to the data if we end up analyzing it in the wrong manner in the first place? Our whole goal here is to find out what is in the information, and learn what the data can actually tell us, without our own biases in the process.

If you are going to ignore this advice, and just dive in with your own conclusions and without paying attention to what the data is actually trying to tell you, then you may as well give up right now. You will find exactly what you want, but that doesn’t mean that we looked at the information in the right manner. And often this steers you, and your business, down the wrong path.

The first thing that we need to take a look at here is what data analysis is all about. When we look at data analytics, we see that it is the science used to analyze lots of raw data to help a company make smart and informed conclusions and decisions about that information. Many of the techniques and the processes that come with data analytics have been automated into mechanical processes and algorithms that are able to work the raw data over in a manner that makes it easier for humans to learn from.
This process is much easier to complete and much faster than having someone manually go through and read that information. This makes it easier for companies to use all of that big data they have been collecting. You can have all of the big data that you want, and you can store it in any manner that you would like, but if that data is never analyzed and read through, you will find that it is basically useless.

The techniques that are available for us to use with data analysis are going to be helpful in that they reveal trends and metrics that would otherwise get really lost in the mass of information. Remember that companies have learned the value of lots of data and this means that while they are collecting all of that data they could easily lose out on some of the insights because it gets hidden inside with all of the noise.

Once the company is able to use a model of deep learning to help with a good data analysis, and they figure out what the metrics and trends for that information entail, they can then use this information to optimize the processes that will help increase the overall efficiency that we see in that system, and maybe even for the whole company.

To make this a bit further, we have to understand that data analytics is a very broad term that is able to encompass a lot of diverse types of data analysis at some point. Any type of information that you have collected and perhaps stored can be subjected to the techniques of data analytics if you want to see what is in a group of information to use to further your business and to beat out the competition, then it doesn’t matter what kind of information you have, the data analyst can still find the insights that will improve your business.

For example, we may find that many manufacturing companies are going to work with data analysis to help them run and perform better. For example, they may record the runtime, the amount of downtime, and then the work queue, or how long it takes the system to do a specific item, for various machines. They can then analyze all of this information and data to make it easier to plan out the workloads, ensuring that each machine is operating closer to the peak capacity that it can during working hours.

Data analytics can help us to make sure that there are no longer any bottlenecks when it comes to production, but this is not the only place where good data analysis is able to shine. For example, we can see that gaming companies are often going to work with a form of data analytics to ensure that they can reward their players on a set schedule, in a manner that makes it more likely that the majority of the users will remain active in the game, rather than giving up and getting frustrated.

Another example of how this data analysis can work, especially with the right deep learning model, is with the world of content companies. Many of these are already relying on data analytics to keep their customers clicking, re-organizing, or watching content. The more that they can get these clicks and views, the more money they are able to generate from advertisements and such on their website, so they come up with content, headings, and more that keep readers there for as long as possible.

Now, the steps that come with doing a data analysis can be quite involved and take some time to get through. But we are going to focus on some of the basics that come with each step so that we can get a good general idea of how this process is meant to work, and why so many businesses are able to see some great results when they use data analysis on their own data:

The first thing that we need to do is make a determination of the requirements we want to place on the data, or how we would like to group our data. Data may be separated out in any manner that you would like including by gender, income, demographics, and age. And the values of the data can be numerical, or we can divide them out by category. Knowing your business problem and what you are hoping to solve with this data analysis can make it easier to know exactly what the requirements of the data should be.

The next step that we need to focus on here is to collect the data. Without any data, we are not going to be able to create the deep learning models that we want, and we will be stuck without any analysis at all. We are able to collect the data from a variety of sources, including from computers, online sources, cameras, and through personnel, and environmental sources.

Once we have been able to collect the data that we want to use, it is time to go through a few steps in order to organize that data. This makes it easier to analyze the data without mistakes or errors showing up. The organization may take some time and the method that you use will depend on the type of data and what you are trying to learn.

While you are organizing the data, we need to also take some time to clean it up. This means that we want the data scrubbed and then checked to make sure that there are no duplications or errors that will be there and that the data is not incomplete in any manner. This is a good step to work with because it will fix up any of the errors in the set of data before you try to move the data along and perform your analysis on it.