Data Science: Its Significance & Why do we need it?

Data Science Lifecycle has several stages where data works differently and used differently. Let me first tell you what all happens with data in data science. A huge amount of raw data is first sent for examination, then comes the analysis of the data. Where data analysis is critical, and thirdly extraction of useful information from it. And lastly, structuring of the data makes it easy.  

To make you understand on the whole, below is the discussion of these steps in brief. 

The data science process takes place to solve the problem of an organization. The problems that are restricting them to attain a certain achievement and make use of their brand effectively. And a data scientist is the one who helps the business or organization to overcome this situation. Data Scientists help businesses in crisis and understand their needs. As soon as the problem flashes, the entire work is to get the solution. Let’s see what steps a data scientist takes to find the solution. 

• Obtaining the data: Data can be present in any way. It can be pre-existing data, newly generated, and even downloaded from a certain source from the internet. A data scientist must derive the data from any external or internal databases, social media, web server logs, and the company’s CRM Software, and can even buy it from any third-party source. 

• Scrubbing the Data: Scrubbing the data actually means cleaning the data and to clean the data, a data scientist first must know how to separate or break down the data to make it look clear and then find out the missing data, or errors in it, and even if there are unwanted data present in it. 

More detailed version

Once data is analyzed thoroughly, second comes the shifting of the values of the data from advanced to standard. So that it becomes easy to go through it. Thirdly is checking out the errors such as spelling mistakes, or spacing errors. These need to be fixed in the second stage itself. And lastly, finding out if there is any numerical or mathematical rectification needed. 

• Exploring the Data: Exploring data is done with a purpose and that is to find suitable planning for the organization and make strategies based on the analysis of the data. Data scientists while cleaning the raw data get to know a lot about the data in the first step, but after the analysis here in the third stage, a data scientist can even claim to have a solution for the organization’s problem. The structures and patterns are studied in addition to presenting it in a manageable manner. 

• Data Modeling: Here comes two more terms that are equally important in dealing with data. Software and Machine Learning algorithms are used here to get a deeper knowledge of the problem and about the solution. The data model is prepared after using machine learning techniques such as association, classification, and clustering which are also used in training the data sets. The model is then tested against the data to find the accuracy of the result. This data model can then be worked on multiple times to bring changes in the results according to the changing algorithms. 

• Interpreting the data: Lastly comes the interpretation of the data where data scientists, data analysts, and non-technical business associates work together to bring the obtained information and models into use. This is the final stage where data is implemented practically into the action to see future reports. The data here is presented in a pictorial form such as diagrams, charts, and graphs to let and make it easy to understand for the ones who are unaware of working with such raw data.

These are the steps that are crucial while executing the task in the data science industry. Let us now get straight to the main topic and that is the importance of data science.