What is DataScience Pipeline-StarAgile

In this world where we are living there is a lot of amounts of data. There are various ways that are used to process that data and today we are going to take that in the direction of data science. We are going to discuss pipeline in data science. Not only we are going to discuss the data science pipeline, but we are also going to know what the benefits are of having this in your team and what elements are needed in the data science pipeline. The data science pipeline is the procedure and the equipment that is used to compile the raw data that is collected from many sources. This data needs to be evaluated and then the findings are needed to be given in a clear and concise manner. This is needed for various business planning and the insights are very useful for the organization. There is an ever-growing complexity that is found in the data, but they are needed for decision-making. So, investing in data science is a really good option for many organizations.

So, if you are looking to have a great career in data science, then going for a Data Science course will help you understand what the latest trends in the market are. This will make you understand what are the concepts that are needed when you want to have a great career in this field. So let us now go down to understand what the data science pipeline is.

What is a Data Science Pipeline?

The data science pipeline is the collection of the processes that will be used to transform the raw data into various useful solutions that helps in overcoming business issues. There is a data movement that is needed to be streamlined from the source to the destination in the pipeline for data science. This will help in making better decisions for the businesses. It is basically the methods and tools that are needed to gather raw data from various sources. They are popularly used by companies to come up with real-world solutions that they can apply in their business and analyze as well.

What Are The Benefits Of Using Data Science Pipelines?

It is needless to say that there are many benefits associated with data science. So below are some of them:

The pattern can be replicated

One of the best advantages of using these pipelines is that the large architecture can be reused or replicated, and the team will not need to spend too many hours on the same work. The new data can be processed and viewed as a network of pipelines.

Integration takes less time

As we have already mentioned that the sources where we can find raw data are increasing day by day, so you need a system that will help you come up with a solution that saves time every time you need to integrate the new data source. With the use of a data science pipeline, you can make this integration process much easier.

Increased data quality

If we are taking data streams as pipelines, we will tend to regulate them more precisely and this will increase the data quality which is going to be received by the end users. With these regulations, there will be lesser chances of pipeline breakdowns and they will be discovered timely to take the right measures.

Enhanced security

With the data science pipeline, you are going to get repetitive patterns and also consistent knowledge that will help in keeping the security of data in check. You can always make sure that the data from the new source is also safe and it will not block the entire pipeline as well.

Building in stages

The data is flowing in the pipeline, and this can make scaling that data easier.  You can start early and also get many benefits from going in this in the beginning. You can have a modest controllable segment from the data source to the end user directly.

More flexibility

If you want to have better data sources and also have a change in end users, then with the use of pipelines in data science, you can have that as this will help you respond dynamically to the modifications needed when you want to make these changes. With the advance of extensible, modular, and reusable data pipelines, the use of data engineering is having so much significant

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How does the data science pipeline work?

Now that we have understood what the data science pipeline is, we need to know how this works. Below are some of the steps that are needed when you want to implement this pipeline in your project.

1.       Obtain your data

If you are a data scientist, then you must be very well aware of the fact that there is nothing that you can do if you do not have any data. There are many things that are needed to be considered when you are going to obtain your data. You need to choose the right data set which will help you come up with the solution. Not only this, but you also need to take the right format for your obtained date. There are many skills needed like- MySQL, PostgressSQL, and knowledge of querying relational databases. You should have a good understanding of Hadoop, Panda, and Apache/Flink.

2.       Cleaning of your data

This is one of the most time-consuming steps in the data science pipeline. You should have a dataset that will not only give your useful insights but also accurate ones So to make sure that you are going in the right direction, you need to examine the data, identify errors, and find corrupt records. Then once you have examined what are the issues, you need to clean the data which will include throwing away the unnecessary values/errors from the data set. For this, you will need some knowledge of python, R, and SAS. These tools will help you get to the data-cleaning process efficiently and quickly.

Also, read SAS vs R vs Python

3.       Analysing the data

This step includes the exploratory measures needed to find the solutions/analysis in the given dataset. We need to understand what the patterns and trends in the given dataset are. This will help us visualize and understand our statistical testing to back up our findings. You need to put your exploratory hats on for this and find the hidden meanings behind the data. Moreover, you will find significant variables here in this step. You need to use python where you need to use Numpy and Matplotlib.

4.       Modelling

Now it is time to make the sense of your findings and in this step, we are developing models that will help the business to work towards the problem. This will majorly include machine learning a lot. In this step, various algorithms needed to be developed which will meet different business goals. If you are going to use better tools, then you are going to get better predictive analysis for your work. This will enhance the business decision-making process. So, in this step, you need to evaluate and refine the model and the skills needed are machine learning- both supervised and unsupervised, along with python and R. Also, linear algebra is a critical skill that will help you devise such models.

5.       Interpreting

Now it is story time. This is one of the major steps in the pipeline for data science as this is the implementation of all the work that has been done previously. N this, you need to connect with people, make them understand your findings, and then help them to come up with decisions that will help the organizations. This will require you to understand your audience and talk to them in their language. So, in this step, you are returning back to the business with your insights and keeping it simple for them to understand. You will need business domain knowledge along with Tableau, matplotlib, etc. Along with this, having communication skills that will help you present your findings is very much needed.

These are the major steps in your data science pipeline. Now you have your model in production. But your work does not stop here. You need to provide changes and updates to it periodically. As you receive more and more data, you need to have updates that often. With this pipeline, you can make sure that you have better integration and flexibility when you have new data sources and also new end users.

What are the applications of the data science pipeline?

There are many applications of pipelines in data science. Regardless of the industry, below are some of the applications:

·         Risk Analysis: It is mostly used by financial institutions to make the sense of enormous amounts of data. There could be potential risks that can come in the organization. So, they need to be aware of them before. With the use right data science pipeline, companies can make sure that they are well-equipped to face those issues.

·         Forecasting: These pipelines are very useful when you want to have forecasted data that will help you understand and estimate the impact of a series of events. These are used by mostly all industries. This helps in giving better solutions that will make them aware of what is needed to be done.

Characteristics of Pipeline in data science

So, now we are aware of the steps that are done in the pipeline of data science- sourcing the data, managing, analyzing, and then transforming that data to give insights to devise business models. When you are using a modern data science pipeline, you are going to have better accessibility and quicker results as well. So, some of the characteristics of the data science pipeline are:

1.       Continuous, extensible data processing: This model helps in getting access to various data sources very quickly.

2.       Cloud-enabled elasticity and agility: If you are looking to have more flexibility and agility, then going for a data science pipeline will be your way to go.

3.       Independent, isolated data processing resources: You are going to have an independent resource that will help you come up with a plan that will help you understand what is going on.

4.       Widespread data access and the ability to self-serve: The data science pipeline will help you have access to data and also give you an option to self-serve.

5.       High availability and disaster recovery: You can be aware of potential risks which will help you come up with a plan to deal with those issues in advance.

So, if you are working in the data science pipeline, then with the help of the above-mentioned characteristics, you can make sure that you are taking the best from the model made by your team of data scientists.

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Conclusion

So, in this article, we came across various segments of pipelines in data science. We learned what is a pipeline in data science and what are benefits a company can have when they are properly implementing various steps given in this process. As we are living in the world of data, there is a need for a process that will help in extracting, analyzing, and using those insights that will help organizations to come up with various plans to help their business. Data Science is one of the major areas where you can encompass various skills and learn various concepts. You are going to have various roles. With the help of pipelines and various concepts of machine learning, you can ensure that you are giving value to the project.

If you are looking to increase your knowledge of data science, you should definitely check out data science certification. With the best platform like StarAgile, you can ensure that you are having all the tools under your hat which will help you score your dream job in this market. The trainers are professionals and help you understand various terminologies and concepts that play a major role in your career as a data scientist. So, start now and give wings to your career.