Some say that we live in a world that is driven by data. Digital data is the next big thing. Actually, it is currently the biggest thing in information. The digital universe is expanding at a very rapid pace and it is expected to reach around 180 zettabytes by 2025. And that is a lot. A LOT.
Owing to said rapid expansion, more and more jobs are coming up in this domain. So for those of you with just a basic understanding, here’s an article that would explain the three main fields when it comes to data – Data science, data analytics & big data.
Let’s take a look at each of them.
Big Data 101
Technically, big data refers to any data set that is too large for conventional data software to manage or deal with. While the term had been loosely used since nineties, it’s current definition is pretty much relevant only to the volume, velocity and variety. Collectively they are called the 3Vs that make up big data.
Today, big data can be defined as any holistic information management system that covers and integrates new types of data and its management.
Big data applications require cost-effective, and competitive ways of information processing. This calls for highly skilled professionals who are competent in the field of RDBMS and similar lines of work.
Big data analytics and architects are hot jobs in today’s job market. To be a skilled big data analyst, a very unique skill set is vital.
Data Science 101
In layman terms, data science is the use of methods to analyse huge amounts of data with the intention of extracting useful information from it. A data scientist as the name suggests is a person who does the job of analysing and extracting said information.
Most of the extraction happens using automated methods and thanks to our rapidly developing technology, many such methods have come to be in use. Domains such as human genomics, high-energy physics make use of data science to advance. Data science is expected to transform anything from media to healthcare.
Owing to its massive scope, the position of data scientist is fast becoming popular. To draw insight from a large set of data, it takes a certain level of understanding & in depth knowledge of automated methods to be a good data scientist.
Data Analytics 101
DA, as it is simply called, is the use of techniques on data that are both quantitative and qualitative to improve gain and efficiency of a business. It typically involves extraction and organizing data to identify behavior, patterns or other relevant information from a big chunk of data.
Data analytics is typically used to B-2-C applications. In most cases data is stored to be later analyzed and studied to mine out purchasing trends and the likes later. So to summarize, data analytics is extracting meaningful information from raw, unprocessed chunks of data using specialised methods and computer systems.
Differences Between The Three
While all three involve manipulating large chunks of data, Big Data Vs. Data Science Vs. Data Analytics, the primary differences lie in the skills required and individual methods required. Here’s a summary of the different requirements in each of the three.
|Big Data||Data Science||Data Analytics|
|Skills required –
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Got The Skills?
Like we mentioned before, the domain concerning digital data one of the fastest growing domains currently. Job opportunities are ample in this field and if you have the right set of skills, you can earn a handsome pay working for companies dealing with digital data.
If you are interested in this field, it’s time you get started!!