Importance of Data Science & why do we need it?
Tags: datascience, machinelearning, python, programming
Published: 2022-12-08 11:18:31

[Data Science](https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/) is an emerging technology adopted by various organizations to find ways to achieve a certain success point and take their brand to the heights using a huge amount of data generated every day online. Data Science has become the most complex yet interesting field requiring several skills to master the domain. Although this may be true that not everyone can master the data science industry even with many years of experience because only obtaining the relevant skills is not necessary, you must even have the passion, interest, and love to deal with data every day.
People generally hear about the data science industry and make their perceptions without even doing basic research from their end and end up with the disappointment that heads them nowhere. This happens because they did not have passion for the industry, yet they found it a fascinating career option. Indeed, it is a profession that can make you earn even 6 figures or more, and for that, you need to have curiosity, courage, and interest to work in the industry.
This blog is particularly for those who want to learn data science but know nothing about it, and not even what is it. Firstly, I’ll be letting you know about what data science is and then about its importance.  

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**[What is Data Science?](https://1stepgrow.com/data-science/data-science-its-significance-why-do-we-need-it/)**
Data science is the domain that is to study the different forms of huge or small data to obtain useful information from it. Data science contains several steps through which data is processed and is used for several purposes in each of the stages. The main idea of introducing data science was to help small, big, well-known, and local businesses to grow and achieve success with it.
In addition, the data obtained over the internet helps make future predictions to help businesses with how and what steps to be adopted. Let us now see the steps of data science that make a data science cycle. 

**Data Science Lifecycle:**
Data Science Lifecycle has several stages where data is treated differently and used differently. Let me first tell you what 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 is analyzed critically, and thirdly the useful information is extracted from it, and lastly, is given a structure to make it understandable for all. 
To make you understand on the whole, below is the discussion of these steps in brief. 

The data science process is held to solve the problem of an organization that is 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 of crisis and helps in understanding the business needs. As soon as the problem is recognized, then the entire work is to get the solution that which data scientist is an expert in doing. 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. 
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 checking out the errors such as spelling mistakes, or spacing errors. These need to be fixed in the second stage itself. And lastly, find 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 them in a manageable manner. 

**Data Modeling:** Here are two more terms equally important in dealing with data. Software and Machine Learning algorithms are used here to get a deeper knowledge of the problem and 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 the 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 performed 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. 



**Importance of Data Science**
The increase in demand for data scientists across the globe clearly shows the importance of data science. Let us now find the reasons why and how data science is important in today’s generation. Data science has spent years reaching where it is today and now has evidently become the industry where aspirants, students, and even professionals are inclining. Here are the reasons that make it important for us to exist. 

**Recognize the client**
Data science helps in recognizing and targeting the client profoundly. Clients are the ones that help you to establish a brand or name in the market. They are the reason for the success or even failure of your organization, brand, services, and products. Consider them the foundation of your work. Data science allows you to interact with your customers, or audience in an effective manner where you even get feedback on how well your products and services are doing in the market. Therefore, clients are the ones and last ones to confirm whether your product should continue to be used or not. 

**Act as a Storyteller**
Data science helps to present the story of your brand or product engagingly. In this case, data science acts as a storyteller that can play a major role in achieving the target set by the organization. Though, data science is an industry with very complicated raw data that cannot be easy to understand. Yet, when used efficiently, these raw data can make wonders in telling your journey in a story format. 

**Engages in several industries**
Data science is a multi-tasking domain that provides its services in various sectors such as travel, healthcare, education, finance, banking, E-Commerce, social media sites, and even more. And in any case, data science is used effectively to get the solution to the problems faced by any industry and at any level.  

**Data as a Key Power**
Undoubtedly, data is the most important part of data science that plays a major role in the success and failure of your business. An infinite amount of data is produced every day over the internet and this data carries a major part of the information useful for any organization to work with certainly. And if this data is used correctly, you probably hope for better results to come up, but if in case, any mishappening occurs with the data or mistakes happen while reading the information. It can simply affect your business harmfully. 

**Data Science Tools as a shield**
Many times, big raw data is difficult to process, and it becomes impossible to resolve the issue using that big data. Also, it is very complex to study such large data because they contain many technical words used in coding, etc. In this situation, data science tools come into the frame to help you with finding the huge raw data and breaking down it to study them keenly. Sectors such as Human Resources, IT, and Resources Management require data science tools to solve the complications. 
These were a few of the reasons stating how data science has so much importance in today's world. 

**Conclusion**
Data science helps every industry in several ways to attain a certain position in the respective markets. Besides, it also provides several useful solutions for each of their problems. As the demand is increasing with time, the importance will also increase accordingly. And with such a need for data science in the technology and science sectors, data scientists are also in high demand for multiple job roles. Thus, a data scientist is accepted to have all the relevant knowledge of the industry and relevant skills to overcome any dull phase or situation faced by the industry. Also, a data scientist should possess the quality of accepting the challenge to do better in the field.