Introduction to Data Science and Its Growing Importance
Data Science and Its Growing Importance – An interdisciplinary field, data science deals with processes and systems used to extract knowledge or insights from large amounts of data.
Data extracted can be either structured or unstructured. Data science is a continuation of data analysis fields like data mining, statistics, predictive analysis.
A vast field, it uses many theories and techniques that are a part of other fields like information science, mathematics, statics, chemometrics and computer science.
Some of the data science methods include probability models, machine learning, signal processing, data mining, statistical learning, database, data engineering, visualization, pattern recognition and learning, uncertainty modelling, and computer programming.
With so much data, many aspects of data science are gaining immense importance, massive data.
It is not restricted to big data, which is a big field because big data solutions focus on organizing and pre-processing the data rather than analyzing the data.
Also, machine learning has enhanced data science’s growth and importance in the last few years.
What is the origin of Data Science?
Over the years, it has become an integral part of many industries like agriculture, marketing optimization, risk management, fraud detection, marketing analytics and public policy.
Using data preparation, statistics, predictive modelling and machine learning, it tries to resolve many issues within individual sectors and the economy at large.
It emphasizes the use of general methods without changing its application, irrespective of the domain. This approach is different from traditional statistics tend to focus on providing solutions specific to particular sectors or domains.
The traditional methods depend on providing solutions tailored to each problem rather than applying the standard solution.
Today, it has far-reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, digital economy on the one hand and fields like healthcare, social science, medical informatics on the other hand.
It affects the growth and development of a brand by providing a lot of intelligence about consumers and campaigns through data mining and data analysis techniques.
The history of this can be traced to over fifty years back and was used as a substitute for computer science in 1960 by Peter Naur.
In 1974, Peter published Concise Survey of Computer Methods, where he used the term data science in its survey of the contemporary data processing methods.
These methods were then used in several applications. Almost twenty-two years later, in 1996, the International Federation of Classification Societies met Kobe for their biennial conference, where the term data science was used for the first time, in the title of the conference, which was called Data Science, classification and related methods. C.F. Jeff Wu in 1997 gave an inaugural lecture on the topic where he spoke about statistics being a form of data science.
Later in 2001, William S. Cleveland introduced data science as an independent discipline. In his article, Data Science: An Action Plan for Expanding the Technical Areas of Statistics, he incorporated advances in computing with data published in the International Statistical Review in April 2001.
In his report, William mentions six areas that he thought formed the base of data science: multidisciplinary investigations, models and methods for data, pedagogy, computing with data, theory and tool evaluation.
In the next year, in 2002, the International Council for Science: Committee on Data for Science and Technology started the publication of Data Science Journal, which focuses on issues related to data science like a description of data systems, their publication on the internet, application and legal issues.
In January 2003, Columbia University also began the publication of the Journal of Data Science, a platform for data workers to share their opinions and exchange ideas about the use and benefits of data science.
A journal that was devoted to applying statistical methods and qualitative research, this journal was a platform that provided data workers with a voice of their own in the field of data science.
In 2005, the National Science Board published long-lived Digital Data Collections: Enabling Research and Education in the 21st century.
This article defined data scientist as the information and computer scientists, database and software programmers, disciplinary experts, curators and expert annotators, librarians who are extremely important for the successful management of digital data collection.
Their primary activity is to conduct creative inquiry and analysis so that data can be utilized properly and effectively by organisations across all sectors.
The growing importance of data science has, in turn, led to the growth and importance of data scientists. These data scientists professionals are now integral parts of brands, businesses, public agencies and non-profit organisations.
These data scientists work tirelessly to make sense of a large amount of data and discover relevant patterns and designs to be effectively utilized to realize future goals and objectives.
This means that data scientists are gaining prime importance, and understanding data properly is reflected in their rising salaries.
According to a recent study by McKinsey Global Institute, there is a shortage of analytical and managerial talent, especially as they need to make sense of a large amount of data available in the world.
This is one of the most pressing challenges in current times. Further, this report estimates that by 2018, there will be a requirement of four to five million data analysts.
There is also a need for close to one million managers and analysts who can help consume big data results to help organisations reach their goals in a manner that utilizes resources strategically and helpfully.
Why is data science so important?
It has, over the past few years, come a really long way. That is why they are an integral part of understanding many industries’ working, however complex and intricate.
Here are ten reasons why it will always remain an integral part of the culture and economy of the global world:
- It helps brands to understand their customers in a much enhanced and empowered manner. Customers are the soul and base of any brand and have a great role in their success and failure. With data science, brands can connect with their customers in a personalized manner, thereby ensuring better brand power and engagement.
- One of the reasons why it is gaining so much attention is because it allows brands to communicate their story in such an engaging and powerful manner. When brands and companies comprehensively utilize this data, they can share their story with their target audience, thereby creating better brand connect. After all, nothing connects with consumers like an effective and powerful story that can inculcate all human emotions.
- Big Data is a new field that is constantly growing and evolving. With so many tools being developed, big data is almost regularly helping brands and organisations solve complex problems in IT, human resource, and resource management effectively and strategically. This means effective use of resources, both material and non-material.
- One of the most important aspects of data science is that its findings and results can be applied to almost any sector like travel, healthcare, and education. Understanding the implications of data science can go a long way in helping sectors analyse their challenges and address them effectively.
- It is accessible to almost all sectors. There is a large amount of data available today, and utilising them properly can spell success and failure for brands and organisations. Properly utilizing data will hold the key to achieving goals for brands, especially in the coming times.
That being said, it is taking on a big and prime role in the functioning and growth process of brands. Being a data scientist is a prime position for any person as they have the big task of managing data and providing solutions for their problems, both within and outside the organisation.
Today, data scientists are opening new grounds in terms of experimentation and research. They are experimenting with intelligence gathering technologies and developing sophisticated models and algorithms to help brands answer some of the biggest challenges they face. A data scientist will perform major functions and roles; some of them include the following:
- Link new and different data to offer products that meet the aspirations and goals of their target customers
- Use señor data to detect weather conditions and reroute supply chains.
- Uncover frauds and anomalies in the market
- Advance the speed at which data sets can be accessed and integrated
- Identify the best and innovative way to use the internet so that brands can comprehensively use opportunities.
While retail is one area where it can have huge implications, take, for example, the case where the older generation recall having an amazing interaction with the local shopkeeper.
This shopkeeper was able to meet all the needs of the customer in a personalized manner. With time, however, this personalized attention got lost in the emergence and growth of supermarkets.
However, data analytics can help brands to create this personal connection with their customers. Using this, brands will have to develop a better and deep understanding of how customers use their products.
This means competitive retailers will have to understand better how customers use their products. Efficiency means that retailers will have to match the right product to the right customer, even though both these objects are constantly evolving.
What is the future of data science and a data scientist?
So while retail is a tangible field where the effects of data science are clearly visible, it can have far-reaching implications in other fields. These include healthcare, energy and education.
Because these fields are constantly evolving, the importance of it is also rapidly increasing.
In healthcare, new drugs are being constantly discovered on the one hand, and there is a need to create better care for patients on the other hand.
With its use of methods and techniques, it can help the healthcare sector find solutions that help take patient care and satisfaction to the next level.
The healthcare industry is constantly evolving, and it can help them create better care for patients at all stages. Another field that can truly benefit from data science is education.
With technology like smartphones and laptops becoming an integral part of the education system, it can help create better opportunities to help students learn and enhance their knowledge in a constructive manner.
Another example of how it can help society is through its application and use in energy. The energy sector is today on the cusp of radical change and transformation. From oil to gas to renewable energy, we need to find new and innovative ways to use energy.
It can help us meet the increasing demand and sustainable future challenges while ensuring the best solutions. This means that data scientists will have to develop a wide range of solutions to meet challenges across all sectors.
This is not an easy task, so they need the resources and systems that will help them achieve this goal. Data scientists will have to become creative thinkers who use high-end tools to create solutions that can be adopted across all verticals across sectors and economies.
All in all, data scientists are the future of the world today. They will soon become an integral part of the organisation and help the world address major global challenges, which can have far-reaching impacts across countries.
That is why the need of the hour is to develop the skill and creativity of data scientists worldwide so that they can help people experience life, products, and services in a brand new manner.
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This has been a guide to Data Science Its Growing Importance. Here we have discussed the basic concept, origin, importance along with the future demand of data science and a data scientist. You may look at the following articles to learn more –
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