Data science vs big data

Organizations need big data to improve efficiencies, understand new markets, and enhance competitiveness whereas data science provides the methods or mechanisms to understand and utilize the potential of big data in a timely manner.

The Resurgence of Data Science

In most cases, the work of data scientists is simply not compatible with that of machine learning and statistical analysis. However, in some industries, it is in-line and already a prominent part of the business model. Twitter, for instance, and Netflix are two of the leading analytics companies that rely on big data to deliver predictive analytics capabilities in order to better understand their users. In most cases, machine learning and statistical analysis require massive amount of data to understand the behavior of an entity or a set of entities. In this digital era, organizations have started seeking tools that help them utilize data scientists.

Reasons for the Resurgence of Data Science

It has been observed that big data demands more capacity and expertise to manage the massive volumes of raw data that are being generated from a variety of sources. Thus, leading organizations are heavily investing in their data management to handle the volume of information. Large, well-organized data sets are the key to analytical insights that can significantly impact the business operations. This, in turn, has led to the emergence of data science as an analytical function. Data science is mainly based on statistical techniques and predictive models. In the process, it is being realized that some of these patterns can be found in the archives of newspapers and magazines. Hence, it is being used by organizations to help extract insights from the data sets they have.

Data Science is a Science, not a Tool

There is a misconception that data science is a tool, a platform to which we can apply machine learning and other kinds of artificial intelligence. This is far from the truth. While it is true that there is a robust body of work in machine learning that helps us structure our data into increasingly complex information systems, there is no set of methods and algorithms that are commonly used in practice. Organizations that do not have sufficient data to train sophisticated algorithms need to acquire new, specialized expertise to build their own AI tools.

Data Science as a Profession

Data Science as a career is the blending of data analytics with other fields including statistics, computer science, mathematics, and physics. Here is how this is described in a resume listing by TopCoder.com “Data science is a specialized branch of computer science where the main emphasis is on scientific modelling of data with a bias toward finding novel insights. The results of their research can be applied to a wide range of practical problems in a variety of fields, including biology, chemistry, materials science, physics, psychology, statistics, and data mining.” This explanation was in the Powerpoint presentation the three judges viewed during the competition.

Big Data and Data Science

Big Data is the high volume and high variety of data that can be collected to identify patterns, make predictions, and make decisions, while Data Science is the discipline of bringing together the data and making sense of it. Increasing popularity of Data Science Big Data is both the defining and frustrating challenge of our time. Big Data brings the world closer than ever before to being able to understand all that is going on around us. Yet, the challenges of implementing these big data projects into an enterprise environment mean that it is not yet widely used. IT Agility IT departments can’t effectively manage, operate, and modernize their current IT systems to take advantage of new emerging technologies if they don’t adopt agile practices.

Data Science is Important

The increasing prevalence of the large amount of data in the digital and technological era requires people to become data scientists as the demand for data scientists is growing. There are four main responsibilities for the data scientist: Discovery. Data scientists create the basis for the understanding of big data. They make use of novel analysis methods to collect, process, and structure data in a targeted manner. They design and analyze the models to predict trends in the data. An essential tool for the data scientist is machine learning. Empirical Analysis. Data scientists conduct the initial analysis of big data using appropriate statistical methods.

Conclusion

The concept of data science is changing the way organizations are approaching data from a much wider perspective, the big data is from social networks, smart devices, IoT and unstructured data. Organizations need to develop the ability to analyze and transform this vast amount of information to the benefit of the organization.

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