Is Data science bigger than big data?

7 Min Read. |

Curious to know what is big-data? 

Then in the simples form, you can understand big data as the large volume of structured as well as unstructured data that overflows around the day-to-day channelization of a business. 

However, the amount of the data is not significant here, instead what businesses do with those data and insights to make better decisions and strategic business moves are more important.

Big Data is crucial in understanding how the world is now digitalized and revolves around data transformations. It is from the data that stems from regular communications & living. The data goes through various processes from being collated to sorted and analyzed. 

Here is where big data vs. data science comes into the picture. Where data science seamlessly integrates such data to extract the best of insights. Thus, chalking out ways for influencing the public at the mainstream starting from the grass-root level.

Social media and data science seem to go hand in hand. The difference between big data and data science is vast even though they find to be inter-connected & used for scenarios identical in nature. 

To understand the two is essential how technology has ornamented swathes of data due to the ease of availability and data sciences doing the role-play of swiping up the hype surrounding such data.

Understanding What is Big-Data

The standard definition of this suggests how data is assimilated and put together that is way larger to go through processing utilizing the database’s conventional systems. All of this is relating to a certain period. 

There are misconceptions regarding such data, where references are cited for cases where the data volume is in terabytes or exceeds.

Data can be varied & is often defined in concise forms. The biggest example of which could be the email attachments. Any size over and above 250 megabytes is considered bigger in context. It brings us to what is big-data and its characteristics. 

The primary ones are termed as the 5 V’s. The “V’s” respectively have multiple inferences owing to their handling. However, when presented as a whole, the challenges are even bigger. 

Characteristics of Big Data

Volume

The technological advancements and the data generated for every second span determine what such data concerning its size & volume is.

Velocity

The acceleration speed for such data is immense. Social media giants like YouTube garners about three hundred plus hours of video streaming and uploads, that too for every minute.

Variety

Delving deep into what is big-data gives information about the wide variability in its content. It is from audio, video streaming, written evidence in texts, or any other recording.

Veracity

Data needs to be accurate & authentic. It governs the accessibility of data in all its forms. What is big-data answering the question of why data is often discarded. It is solely due to the data being unreadable and redundant & does not meet the pre-requisites.

Value

It necessitates the need to offer values of some kind. 

Cloud Computing, Social Media Trends & Big Data

The past trends suggest how web search engine Google reported the terms of cloud computing & social media. Cloud computing came in a little early from 2007 when the bigger question revolved around such data. 

Whereas social media chartered its flight around 2009. Social media has registered a growing pattern ever since, while cloud computing seems to be in a trajectory of sorts. Seeing multiple instances of peaking and then slowing down before levelling.

The process of renting or storage across cloud systems is so generalized that it seems to have lost its sheen. That is not the case for social media. The difference between big data and data science is how the latter has outsized controlling data flows. 

A look back over most popular searches for the past year states social media marketing to be trending. 

It suggests how digital juggernauts direct attention catapulting the debate of big data vs data science. Social media, even though being as ubiquitous as it is, still finds resonance amongst the masses.

Such a form of data saw significant gain with the downfall of cloud computing. It is primarily due to reasons that replaced focus from hardware rentals. Thereby, it brings into the equation what it is and how it is enforced to evaluate massive data sets through computational power.

Arguably, data science took off to a good start in 2013 and accelerated all through 2014. 

Big Data & Data Science

The onset of 2019 saw the search volumes for data science surpassing big data. Big data vs data science gained more steam. It is more so a gradual phase that first saw cloud computing being replaced by big data. 

Now data science takes the lead. Gone are the days of storing mounds of data. The process of data segmentation is followed to discard data that is redundant. 

What is big-data only highlights how data is assimilated and that it is just raw data. Data science has a tedious job of extracting the best of insights and information from these datasets and transforms them into readable information. 

The voluminous data is converted into simpler language for better understanding. 

Big data vs data science is more evident for this process of data analysis. It foretells how businesses plan their new product launches to gain deeper info about customer interests and how they intend to improvise on operational performances. 

Data science acts as a fuel giving relevance to what is big-data in its truest form. It is working through datasets and obtaining only relevant information. 

One of the best examples of this is Netflix. The online streaming platform churns data to the tune of billions of bytes. Some data scientists work around the clock to scurry through the content and give it a structure. 

What is big-data is how every user generates a plethora of behavioural data while their usage of the platform. This data is further studied and analyzed to segregate to suit preferences. 

Netflix’s personalized streaming experiences are modelled across this, and display results only present shows or movies with maximum percentages match.   

Big Data & Data Science Differences

There are many differences between the two ranging from concept to applications, skillsets to job roles, and career opportunities. The differences are listed in brief as below:

a) Concept

The difference between big data and data science is that while the former examines raw data and aids in support of mechanisms or the business intelligentsia, data science is built around everything related to data. 

This is from generating data to its cleansing as well as mining and analytics. All in context for raw plus structured data in the case of data science. 

Whereas big data combines algorithmic processes to arrive at operational derivatives in the case of multifaceted businesses.

b) Applications

Data sciences find their applications in multiple fields, from digital advertisements, internet searches, and recommending systems. Google AdSense to Media.Net all depends on data sciences and machine learning algorithms for the personalization of ads. 

The feature of internet searches & recommending systems is no different. The normal mode browsing sees results being filtered based on the logged-in accounts or preferences. 

The difference between big data and data science stems here, where big data thrives on data generation. This is mostly the case in terms of online gaming as well as the travel & hospitality sector. 

The volume of big data can only be imagined when a server is connected to a single online gaming session that necessitates 100MB of data, to begin with. 

c) Skillsets

Big data vs. data science states how each though interchangeably used, requires different skill sets to begin with. The pre-requisites for data scientists mention one has to have skills related to analytics, data management, programming. Sound know-how about database systems as well as technical skills, is desired.

The difference between big data and data science is how eligibility for being a big data analytics expert varies from that of a data scientist. An analytics expert must possess skills for data wrangling, data visualization, as well as machine learning

d) Job Roles

Data sciences’ main aim is evaluating through big data. What big-data is and its various forms are explored, visualized, analyzed, and scrutinized, all combining the algorithm of data science and machine learning? 

Big data, on the other hand, is voluminous data that exceeds one terabyte. This data is mostly unstructured and is accumulated from varied sources. 

e) Career Opportunities

Data sciences offer career opportunities in architecture, from being a data or infrastructure to enterprise architects. They can also choose to be data analysts as well as engineers.

Big data brings with it an opportunity to become big data analysts to data engineers and business intelligence engineers. 

Summing Up!

To understand what is big-data and its importance, we need to focus on the data revolution. This is over the past decade, that has swiftly altered from cloud computing to more data sciences. 

This is how people and the processes are brought into the picture, wherein algorithms that govern data and its fragmentation to notch up real-time numbers find prevalence. 

What big-data is seeing a fair number of overlaps from growing to humungous scales and data science acting at their disposal? 

Cleansing, mining, analyzing through various tools & in the process unearthing anomalies. Thereby being termed as the vital element to register growth for organizations to businesses. Enrolling in a data science course will help you learn and master what is big-data and its usages.

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