Importance of Data Science

Category: Data Science Posted:Apr 12, 2019 By: Ashley Morrison

Importance-of-Data-Science

Data is one of the important features of every organization because it helps business leaders to make decisions based on facts, statistical numbers and trends. Due to this growing scope of data, data science came into picture which is a multidisciplinary field. It uses scientific approaches, procedure, algorithms, and framework to extract the knowledge and insight from a huge amount of data. The extracted data can be either structured or unstructured. Data science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data. Data science is an extension of various data analysis fields such as data mining, statistics, predictive analysis and many more. Data Science is a huge field that uses a lot of methods and concepts which belongs to other fields like information science, statistics, mathematics, and computer science. Some of the techniques utilized in Data Science encompasses machine learning, visualization, pattern recognition, probability model, data engineering, signal processing, etc.

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The developments of plenty of data have given enormous importance to many features of Data science particularly big data. But data science is not limited to big data alone as big data solutions concentrated more on organizing and pre-processing the data instead of analyzing them. Also, due to Machine Learning, the importance and growth of data science has been improved.

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Let’ first know about from where the Data Science is originated. From the past few years, Data Science is utilized in many industries such as farming, risk management, fraud recognition, marketing optimization, and public policy, etc. With the help of machine learning, statistics, data preparation and predictive analysis, data science attempts to resolve many problems within individual sectors and the budget.  Data science focuses on the utilization of general strategies while not ever changing it application, regardless of the domain.  This method is entirely different from traditional statistics which incline to concentrate on giving solutions that are specific to explicit sectors or domains. The conventional approaches rely on giving solutions which are tailored to each problem instead of applying the quality solution. Today, Data science has extensive ramifications in numerous fields, i.e., in theoretical and applied research areas such as machine interpretation, speech recognition, advanced economy and also in the fields like healthcare, social science, medical informatics. Data Science influences the growth and improvements of the product by providing a lot of intelligence about customers and operations, by using methods such as data mining and data analysis.

History of Data Science: The Data Science history can be followed to more than fifty years back and was utilized as auxiliary for computer science in 1960 by Peter Naur. After this in the year 1974, Peter published a summarized review of computer techniques. In this review, he has included the term Data Science in its study of the modern data processing methods. Later these techniques were utilized in numerous applications. After twenty years, the term Data Science was used for the first time in the International Federation of Classification Societies in the title of conference Data Science, classification and related methods. In the year 1997, an introductory lecture was given by C.F. Jeff Wu on the Data Science topic where he addressed about statistics being a form of data science.

In the year 2001, William S. Cleveland in one of his articles introduced Data Science as an self-governing field. In his article, he involved developments in computing with data. The article was then published in the International Statistical Review. In one of his report, he indicated six regions which he assumed that they are the foundation of Data Science; these areas are multidisciplinary study, method and models for data, instruction, computing with data, theory and tool assessment. In 2002, the International Council of Science, Committee on Data for Science and Technology begins the publication of Data Science Journal that concentrates on problems associated with the Data Science such as explanation of data systems, their publication on the web, application, etc. In 2003, Columbia University also started the book of Data Science Journal which was a podium for the data staff. The employees used this platform for sharing their thoughts and concepts related to usage and advantages of Data Science. The National Science Board published an existed digital data collection in the year 2005. This is all about the history of Data Science. Now let see who a Data scientist is.

Due to the growing importance of data, the demand for a Data scientist is also rising.  They are now becoming essential parts of products, trades, public agencies, and non-profit organizations. A Data Scientist can be defined as the information and computer scientist, database and software programmers, curators and expert annotator. All of them are very important for the successful management of digital data collection. A Data Scientist is responsible for conducting original analysis and reviews which enables organizations to utilize the data appropriately and effectively in all the domains. The data scientist works very hard to extract meaningful information from a huge stack of data and determine applicable patterns and designs, so that they can be used to identify future goals and objectives. This shows that Data Scientists are gaining popularity and importance. According to a study, it is found that there is a scarcity of talented professionals with Data science skills. Currently, this is one of the biggest challenges. Also, it is estimated that, there will be a massive requirement of data analysts in the future.

Importance of Data Science: Now, let us see some of the reasons which focus on the increasing importance of Data Science. From the past few years, Data Science comes a long way and therefore it becomes the important part of understanding the working of various industries. Below are some reasons which show that data science will always be a significant part of the economy of the global world.

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1. With the help of Data Science, the companies will be able to recognize their client in a more improved and enhanced way. Clients are the foundation of any product and play an essential role in their success and failure. Data Science enables companies to connect with their customers in a modified manner and thus confirms the better quality and power of the product.

2. Data Science allows products to tell their story powerfully and engagingly. This is one of the reasons which makes it popular. When product and companies use this data inclusively, they can share their story with their viewers and thus creating better product connections.

3. One of the important features of Data Science is that its results can be applied to almost all types of industries such as travel, healthcare and education. With the help of Data Science, the industries can analyze their challenges easily and can also address them effectively.

4. Presently, data science is available in almost all the fields and there is a vast amount of data present in the world today and if it is used properly it can lead the product to the success or failure. If data is used correctly it will hold the importance for achieving goals for the product in the future.

5. Big data is continuously emerging and growing. Using various tools which are developed regularly, big data helps the organization to resolve complex issues related to IT, human resource and resource management efficiently and successfully.

6. Data science is gaining popularity in every industry and thus playing a significant role in functioning and growth of any product. Therefore, the requirement of data scientist is also increased as they have to perform an important task of handling data and delivering solutions for the specific problems.

7. Data science also influenced the retail industries. Let’s take an example to understand this, the older people were having a fantastic interaction with the local seller. The seller was also capable of fulfilling the requirements of the clients in a personalized way. But now due to the emergence and increase of supermarkets, this attention got lost. But with the help of data analytics, it is possible for the sellers to connect with their clients.

8. Data Science helps organizations to build this connection with the clients. With the help of data science, organizations and their products will be able to create a better and deep understanding of how customers can utilize their products.

Future of Data Science: As most of the fields are emerging continuously, the importance of data science is also increasing rapidly. Data science has influenced various areas. Its effect can be observed in multiple sectors such as the retail industry, healthcare, and education. In the healthcare industry, new medicines and techniques are being discovered continuously and there is a requirement of better care for patients. With the help of data science techniques, the healthcare sector can find a solution that help to take care the patients. Education is another field where the benefits of data science can be seen clearly. The latest technologies such as smartphones and laptops have now become an important part of the education system. With the help of data science, better opportunities are created for the students which enable them to enhance their knowledge.

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Conclusion: Data science is one of the growing fields. It has become an important part of almost every sector. It provides the best solutions that help to fulfill the challenges of the ever-increasing demand and maintainable future.  As the importance of data science is increasing day by day, the need for a data scientist is also growing. Data scientist are the future of the world. Thus, a data scientist must be capable of providing great solutions which meets the challenges of all the fields. To perform this, they should have proper resources and systems which help them to achieve their goal.

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