Know More About Data Science And Big Data Analytics

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Big Data Analytics

Big Data Analytics

‘Data has become important in our society today’ is an understatement. The importance of data can be seen across multiple sectors with many companies and organizations dedicating time and energy trying to sort and understand this data.

Data can be thought of as our past and in order for us to not repeat it, we must understand it. This is where data analysis comes in. When we talk about data, there is no way we cannot discuss Big Data and Data Science. Across the world, people have become hyped about data and finding ways to organize, understand, and analyze big data.

Data is already growing at a rapid rate, faster than we can keep track of it. According to Forbes, by year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. So, it’s become important to understand all aspects of data.

There are two words that are commonly associated with Data – Big Data and Data Science. Many use these words synonymously, which is incorrect. While both do share the main theme – data, they differ from each other on a lot of levels. Let’s take a look at both the terms.

Big Data
The term Big Data is used for data sets that are large and complex and require special software for processing. This data can be structured or unstructured, and is often used for making better decisions or strategic business moves. Gartner defines Big Data using 3 Vs as – “…high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

The data accumulated from computers around the world, shows a pattern of user behavior when analyzed. This allows companies to understand the current trend of the market before they make their decisions. Big Data is used extensively in sectors such as marketing, business, government, scientists, etc.

Data is growing larger at an exceptional rate because they are now gathered by cheap and numerous information-sensing IoT devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.

Data handling, processing and analysis is often conducted in by software such as Hadoop, Cloudtera, R Tableau and so on.

Data Science
On the other hand, Data Science is different. This term refers the process of extracting insights, or knowledge, from structured or unstructured forms of data. They are required to ask questions, formulate hypothesis and look for unknown solutions that will in turn drive sales. They are also required implementing various algorithms/tools, depending on the requirement of the type of analysis.

Some experts define data science as an umbrella term that encompasses Big Data, while other suggest that the process of extracting detailed information and manipulating data is different from Big Data. Data Science includes techniques and theories from other fields such as mathematics, statistics, information science, and computer science, machine learning, classification, cluster analysis, data mining, databases, and visualization.

The term ‘data science’ can be dated back to Peter Naur, who ended up using the term as a substitute for computer science in 1960, while ‘data scientist’ did not pop up until 1997, when C.F. Jeff Wu used the term in his lecture entitled “Statistics = Data Science?”. He initially wanted to use the term to replace statisticians, suggesting that all statisticians are data scientists.

To analyze and manipulate data, data scientists must be familiar with concepts such as Regression Tools, Clustering, Decision Tress, etc. and they rely on tools such R and RStudio.

Big Data and Data Science: What they have in common?
Big data and data science overlap on many aspects, as both of them majorly deal with Big Data, they require understanding the process of sorting, organization and analyzing data in order to make sense of it. However, while Big Data does a general analysis, Data Science delves deeper and requires manipulating data to understand more hardcore trends that can be understood from the data.

Both, Big Data and Data Science are commonly used for predictive analysis, where data is used to ascertain the particular trend of an industry such as Twitter analytics, Google analytics, Sales Predictions, etc.

Big Data or Data Science?
It isn’t a competition between the two, if you look at both of them as a career option. If you join any stream, you will dabble in both forms, where you will learn not only how to sort or organize data, but also analyze it to a certain extent.

Big Data requires more of mastering particular tools such as Hadoop or MapReduce, while Data Science combines the job of a statistician and mathematician to gouge trends from the data already present. If you are interested in mathematics and data mining, then Data Science is a great stream for you.

Today, both of these fields are an amazing career option as Data plays a huge part in our society. As data continues to grow, the importance of big data and data science will also increase.

3 COMMENTS

  1. I really like how you describe data as our past, and in order for us to not repeat it, we must understand it. I think this is especially important for businesses so they can understand trends in their sales and customers to make more informed marketing decisions. If Forbes is right in their prediction, then there will be a lot of data to process in the near future! Thanks for the enlightening article!

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