evaluating data

How data science tasks are carried out ?

We of us have heard the term ‘data science’ but most of us have a vague idea about the learning and application of this discipline or how data science tasks are implemented. In this blog post, I am going to explain you briefly the process that takes place whenever you do a task in data science. Whatever meaningful insights you generate as a data scientist, they will have one thing in common and that is “Future Predictions”. A data science action or task follows this cycle at large –

1. Planning

This is all about defining the project and the possible outcomes. Depending upon your goals, you have to plan and prepare a draft or prototype of the project. This simply means what benefits the outcome of the project is going to serve us. For example you want to get the data about how many people have booked a movie ticket online and how many have booked offline? You must collect all the data you require for the purpose and while analyzing them you must use all of them. The outcome is the meaningful insight that is going to benefit businesses.

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2. Building a data model

By model we mean machine learning model. To build such a model you will use various open source libraries or in-database tools. These are nothing but various software applications that enable you to craft the perfect data structure according to your need. It is the data model that will decide what information to capture and how to store them. Depending upon business goals, you have to customize the model. Model building is the tedious and most important task of the process.

3. Evaluating a model

This is to check the accuracy of the model you have made. Though it is a time consuming process yet it is absolutely necessary so that it applies correctly to future unseen data. Because there is no chance of making mistakes, you have to check thoroughly before incorporating it into machine learning algorithm. Cautious evaluation helps you determine how well your chosen model will perform in the future.

4. Explaining model

In data science, a model in simple terms is a mathematical representation of a business problem. Since you have made a machine learning model, you must be able to explain it to businesses. Because it is not like literature that anybody can read and understand. You can use tools, techniques and your creativity to make people or businesses understand your model in simple words. You have to explain the internal mechanics of your model in a language that humans easily understand.

5. Launching model

This is to apply your model for prediction. You have to launch your model into the machine learning algorithm so that the machine is redirected. As your ultimate goal is to help your customer, you have to organize and present the model in such a way such that it becomes user-friendly. But this process requires effort and patience. There are four ways in data science you can launch a model.

6. Monitoring model

After successful installation, it is your duty now to monitor the model and make sure everything is working fine. The field of data science is dynamic and after a certain period of time, a model may become ineffective or no more relevant. So, you have to ensure that your model works properly from time to time. For example computer virus detection process is not the same every time. To tackle new viruses, you have to make on time changes to your data model.

By now you must have got a basic idea about how data science tasks are carried out in reality.

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