Many professionals and ‘Data’ enthusiasts often ask, “What’s the difference between Data Science, Machine Learning and Big Data?” This is a question frequently asked nowadays.
Data Science follows an interdisciplinary approach. It lies at the intersection of Maths, Statistics, Artificial Intelligence, Software Engineering and Design Thinking. Data Science deals with data collection, cleaning, analysis, visualisation, model creation, model validation, prediction, designing experiments, hypothesis testing and much more. The aim of all these steps is just to derive insights from data.
Digitisation is progressing at an exponential rate. Internet accessibility is improving at breakneck speed. More and more people are getting absorbed into the digital ecosystem. All these activities are generating a humongous amount of data. Companies are currently sitting on a data landmine. But data, by itself, is not of much use. This is where Data Science comes into the picture. It helps in mining this data and deriving insights from it; for taking meaningful action. Various Data Science tools can help us in the process of insight generation.
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In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.
When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.
Let us begin by defining the two terms.
Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.
Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.
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With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.
Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.
Now, addressing the main topic of interest – how are data analysis and data science different from each other.
As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
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We live in a world where billions of data points are generated every single day from different sources, such as banks, telecommunication companies, industries, tourism, the agriculture sector, educational institutions (primary, secondary, colleges, and universities), and mobile devices. Any organization can start using their data to make data-driven decision-making that is effective and supportive of their mission and vision.
Regardless of the size of the business you’re running, you need valuable data to provide you with business insights. The insights help you to know your target audience and their preferences, and as a result, your business will be able to anticipate their needs. You can use insights from big data to outperform your competition by capturing and innovating through big data.
Companies like Google and Alibaba are using it to discover flaws in their services and products, suppliers and buyers, and consumer intent and preferences so they can create newer, better ones.
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