From the beginning of time, man has been an analytic being surrounded by several problems to solve. However, society has evolved and grown to can be able to develop different methods for the resolution of problems.
Certainly, at this moment we cannot compare the problems of other times with the current problems. But, an undeniable fact is that it does not matter the time, they’re always are problems. For this reason, it is necessary to find the best model to solve them efficiently.
Fortunately for all of us, three decades ago born the data science to try to act like a model capable to solve problems in any field. Despite the data science was created at that time, it was in the 70′ when the term started to be used.
The years kept passing until 2001 when data science could establish as real and independent science. Despite has been passed around twenty years since the establishment of data science, currently a big part of the population does not meet anything about data science and its life cycle.
For this reason, we want to tell you a little bit more about the data science life cycle, and all the stages that make it one of the best methods for problem resolution. In this way, you are going to be able to implement data science in the different fields in which you need to solve a problem.
What is the data science life cycle?
Before start to talk about the stages of data science, we have to know what is data science. As its name says, it is a science that bases its studies on the data. This science is capable to take a huge amount of data and analyze them to get a conclusion.
In some way, data science is a mix of different sciences which includes math, statistic, and informatics. With the work in conjunction with these three sciences, data science can recollect a group of data, organize them, analyze them, and find a solution for the found problems.
From the beginning of the process that involves data recollection, this science tries to use all the updated technology. It means use platforms like social media, electronic devices, websites, leads, and others. For sure, with the development of new technologies and platforms, data recollection has been easier.
But, the data recollection is just a simple part of the whole process of the data science cycle. It is necessary to know all the stages and the details of each one of them to be able to apply the data science life cycle in the field we need.
Read More- What is Data Science? A Complete Guide
The importance behind the data science cycle.
Normally, a big part of society thinks or confuses data science with big data. After all, both processes involve data recollection and organization. However, data science goes beyond because it not only tries to solve problems related to the storage and handle of the data.
Data science can solve the problem, but more than that it processes all the data to give it an important value. We cannot forget that the data is more than just numbers. The collected data could be views on Facebook, comments on another platform, or even reviews of the clients of a business.
For this reason, it is not enough to collect the information and find the problem. It is necessary to give a special value to this problem to find the right solution. Besides, the solution has to last through time and not just for a few days.
To make this possible, data science develops tools to solve the problem through different systems like neuronal networks similar to the neuronal human system. Besides, it also works with artificial intelligence. In general lines, it uses all the tools that be necessary to solve the problems from the data.
Stages of the data science life cycle.
We already told you some basic things and concepts about data science, but we do not tell you yet the different stages that conform to it. The stages of data science are a debate point for different groups of the science community.
It is why some people say that are more than ten steps while there is another group that says five steps is enough. From debates and opinions, we think that to explain a complex process like data science, it is necessary to try to make things simpler.
For this reason, we want to explain to you the data science life cycle through five stages. These stages are long enough to understand the whole cycle and be able to use it to solve any problem we have. It will help you to organize better your data and give it the sense to use it for your well.
Stage 1: Definition of the problem.
The first stage of the data science life cycle is the definition of the problem that is going to mark the rhythm of the cycle. Even before thinking of a solution, we have to find the origin of the problem.
At the beginning of this stage, the most important are the answers to one question: why are you want to start a process with data science. Most of the time, the reason why is to increase the earnings of a business or find the reason why something is not working.
The main key of the definition of the problem is leadership because all the members of your time need a guide or a way to follow. It will help you to work efficiently and solve any problem faster.
The first time you should do is confirm a proper team to help you to solve the problem. This team has to be made of professionals who need to have skills that add a special value to your team. Then talk about the problem with your team and why is so important to the business to solve it.
Besides, your team is going to help you to determine how big is your problem or even if there are other problems involved in the main problem. The first stage of the data science life cycle maybe can sound a little bit like a cliché, but this stage is essential to guarantee the success of the cycle.
Stage 2: data investigation and cleaning.
In this second stage, data science starts to work because is the basis of this science. Without the data, we could not find the problems and neither the solution. For this reason, the investigation of the data is a very important part of the data science life cycle.
However, you probably are wondering how can you recollect all the data or where you can find it. Both you and your team have to determine if the data you are looking for is on the internal performance of the company like sales statistics to get access to them.
Also, exist the possibility that you have to start to recollect the data. In this case, it is important to investigate if the process of recollecting is easy or there are difficulties in the process.
Besides, you also can see if the data which you want or need is available on the market. If it is available, you have to determine if you can buy it and if its cost worth the information.
Once you already have collected the information, you can start to work with your team to process it. The first thing your team has to do with the data is to qualify their quality. We cannot forget that all data is not good data. For this reason, it is indispensable to determine that the data that you collected or bought is going to work to solve your problem.
After determining the data is of good quality, we need to clean the data to avoid get to wrong conclusions. In some way, it is like clean the cache of our cellphones or laptops. We need to eliminate those data that can create noise and change the results of our process.
Finally, it is essential to process the data; it means combining the different data groups, create graphics to visualize better the data, and make a preliminary report with the first findings. This preliminary report is going to help you to make the proper modifications and see the way that is taking your data science life cycle.
Stage 3: minimal viable model.
At this point, we are on stage three that is the creation of a minimal viable model. The minimal word can confuse a little bit, but do not worry because in this case, less is more.
The data science life cycle proposes a minimal viable model because it does not have the sense to spend time, money, and efforts on a test which you do not know if it is going to work or not working. For this reason, we talk about the minimal model that needs to be like a minimalistic version of the solution that you want to implement.
However, despite the recommendation is a minimal model, it does not mean that it does not matter if works or not. The idea is developing long enough a model to make it viable. After all, we are looking for solutions to our problems, and they have to be functional and permanent beyond time.
For sure, like any other experiment that any science can do, the model needs validity. The validity is going to let us measure the test and give us true results. It is why we have to be very careful at the time to design the minimal viable model because we should reduce the external variables.
Reduce these variables is important because they can change the course of our model and give us false positives. However, if we are capable to control and carefully this stage, success is going to be imminent.
Stage 4: deployment and enhancements.
Step by step, now we are on four-stage which are based on the deployment and enhancements. We already have the model; at this moment, but it is not created just to be seen on paper. The purpose of the cycle deploys the model to see how it works.
The deployment is going to give us a clear vision of the nature and functioning of our model. When we start to deploy the model, we can see a lot of mistakes or failures. But, everything cannot be entirely bad. Of this process, we also are going to be the success parts of our model and use them like motivations to be better.
In this way, all the results gotten on the deployment are going to let us think about the proper enhancements. After all, the main goal is to create a model better than the start that could be the final.
Besides, maybe this stage can be repeated more than once because if we make enhancements, test the model again, and need more changes, it has to be proved as many times as necessary.
Stage 5: data science ops.
The final stage is explaining to us the different operations that the data science uses to make a follow-up of the process, the data, the models, and all the elements involved in the data science.
In this way, the data science ops are made of three processes:
- Management of the data and the models.
- Continue management of the parts involves in the data science life cycle.
- Software management.
The whole step five depends on the performance of these three processes which are just looking for the proper control of the experimentation. We cannot forget that control is an essential part of the cycle because is going to let us make adjustments at the right time.
Besides, you can notice that the constant revision is not just for the models but for the data too. At the end of the day, the only thing that matters are how we implement the cycle and how we get what we want in the best way.
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