Data Science and Its Growing Importance

Data Science and Its Growing Importance – An interdisciplinary field, data science deals with processes and systems, that are 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, data science uses a lot of theories and techniques that are a part of other fields like information science, mathematics, statics, chemometrics and computer science.

Some of the methods used in data science includes probability models, machine learning, signal processing, data mining, statistical learning, database, data engineering, visualization, pattern recognition and learning, uncertainty modeling, computer programming among others.

With advancements of so much of data, many aspects of data science are gaining immense importance, especially big data.

Data science is not restricted to big data, which in itself is a big field because big data solutions are more focused on organizing and pre-processing the data rather than analyzing the data.

In addition, machine learning has enhanced the growth and importance of data science in the last few years.

What is the origin for Data Science?

Over the years, data science has become an integral part of many industry like agriculture, marketing optimization, risk management, fraud detection, marketing analytics and public policy among others.

By using data preparation, statistics, predictive modeling and machine learning, data science tries to resolve many issues within individual sectors and the economy at large.

Data science 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 that are specific to particular sectors or domains.

The traditional methods depend on providing sectors with solutions that tailored to each problem rather than applying the standard solution.

Today, data science has far reaching implications in many fields, both academic and applied research domains like machine translation, speech recognition, digital economy on one hand and fields like healthcare, social science, medical informatics, on the other hand.

It effects the growth and development of brand by providing a lot of intelligence about consumers and campaigns, through techniques like data mining and data analysis.

The history of data science can be traced to over fifty years back and was used as substitute for computer science in 1960 by Peter Naur.

In the year 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 a number of applications. Almost twenty two years later in 1996, the members of 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 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, which was published in the the International Statistical Review in April, 2001.

Data Scientist Course 43 Online Courses | 170+ Hours | Verifiable Certificates | Lifetime Validity Watch The Course Preview

In his report, William mentions six areas which he thought formed the base of data science: these includes 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 description of data systems, their publication on the internet, application and legal issues.

Very soon, in January 2003, the Columbia University also began the publication of the Journal of Data Science which was 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 the application of 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 in a proper and effective manner, by organisations across all sectors.

Image source: pixabay.com

The growing importance of data science has in turn led to the growth and importance of data scientists. These data scientists professionals are now an integral parts of brands, businesses, public agencies and non-profit organisations.

These data scientists work tirelessly to make sense of large amount of data and discover relevant patterns and designs in them, so that they can be effectively utilized to realize future goals and objectives.

This means that data scientists are gaining prime importance and understanding data in a proper manner is reflected in their rising salaries as well.

According to a recent study by McKinsey Global Institute, there is a shortage of analytical and managerial talent, especially as they are need to make sense of the large amount of data available in the world.

This is one of the most pressing challenges in the 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 the results of big data in a manner, that can help organisations reach their goals in manner that utilizes resources in a strategic and helpful manner.

Why is data science so important?

Data science has over the past few years come a really long way. That is why they are integral part of understanding the working of many industries, however complex and intricate.

Here are ten reasons why data science will always remain an integral part of the culture and economy of the global world:

1. Data science 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 to play in their success and failure. With the use of data science, brands can connect with their customers in a personalized manner, thereby ensuring better brand power and engagement.
2. One of the reasons why data science is gaining so much of attention is because it allows brands to communicate their story in such a engaging and powerful manner. When brands and companies utilize this data in a comprehensive manner, 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.
3. Big Data is a new field that is constantly growing and evolving. With so many tools being developed, almost on a regular basis, big data is helping brands and organisations to solve complex problems in IT, human resource, and resource management in an effective and strategic manner. This means effective use of resources, both material and non-material.
4. One of the most important aspect of data science is that its findings and results can be applied to almost any sector like travel, healthcare and education among others. Understanding the implications of data science can go a long way in helping sectors to analyse their challenges and address them in an effective fashion.
5. Data science is accessible to almost all sectors. There is a large amount of data available in the world today and utilising them in an proper manner can spell success and failure for brands and organisations. Utilizing data in a proper manner will hold the key for achieving goals for brands, especially in the coming times.

That being said, data science is taking on a big and prime role in functioning and growth process of brands. Being a data scientist is therefore 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, in order to help brands answer some of the biggest challenges that they face. A data scientist will perform major functions and roles, some of them include the following:

1. Link new and different data to offer products that meet the aspirations and goals of their target customers
2. Use señor data to detect weather conditions and reroute supply chains
3. Uncover frauds and anomalies in the market
4. Advance the speed at which data sets can be accessed and integrated
5. Identify the best and innovative way to use the internet so that brands can make use opportunities in a comprehensive manner

Image source: pixabay.com

While retail is one area where data science 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 connect with their customers. Using data science, brands will have to develop a better and deep understanding of how customers use their products.

This means, retailers who are competitive will have to build a deeper understanding of how customers use their products. Efficiency means that retailers will have to match the right product to the right customer, despite the fact that both these objects are constantly evolving.

What is the future of data science and data scientist?

So while retail is a tangible field where the effects of data science is clearly visible, data science can have far reaching implications in other fields as well. These include healthcare, energy and education.

Because these fields are constantly evolving, the importance of data science is also rapidly increasing.

In the field of healthcare, new drugs are being constantly discovered one hand and there is a need to create better care for patients on the other hand.

Data science with its use of methods and techniques can help the healthcare sector to find solutions that help take patient care and satisfaction to the next level.

The healthcare industry is constantly evolving and data science 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, data science can help create better opportunities to help students learn and enhance their knowledge in a constructive manner.

Another example of how data science 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.

Data science can help us meet the challenges of the increasing demand and sustainable future while ensuring the best solutions. This means that data scientists will have to come up with a wide range of solutions to meet challenges across all sectors.

This is not an easy task and that is why they need the resources and systems that will help them achieve this goal. Across sectors and economies, data scientists will have to become creative thinkers who use high end tools to create solutions that can be adopted across all verticals.

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, that in turn 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 across the world so that they can help people to experience life, products and services in a brand new manner.