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.
The 5 Vs of Big Data include
Volume: With the evolution of technology, most of the data created every second is tremendous in size and volume.
Velocity: The speed at which data is generated is beyond our scope of calculations. Did you know that an average of 300 hours of video content is streamed and uploaded on entertainment sites like YouTube every minute?
Variety: The beauty of data is that it is an umbrella term over a vast number of types of information, be it audio content, video streams, textual evidence or anything that can be recorded.
Veracity: It has to be clean and reliable. By clean, we mean that it must be accurate and accessible. Data in an unreadable format, redundant data is discarded is as it does not meet a benchmark.
Value: It should provide some benefit and not be gibberish.
The Confluence of the two!
When we talk about data, it is just a collection of raw facts. To extract crucial information out of it and to convert this Big Data into readable information, the role of Data Science comes into play. Its contribution cannot be negotiated with any other process. Fundamentally, its role is to analyse the voluminous data to obtain insights. These insights are useful to companies planning new products, looking for insights into customer’s interests, or improving the operational and other processes within the organisation.
Read: 3 Ultimate Big Data Project Ideas
Data Science, formally, is the study of any and every data available, including voluminous data. In other words, data is the fuel on which this section of science runs its engine to arrive at meaningful and relevant information. Netflix is a good example where both these terms go hand in hand.
Netflix produces billions of bytes of data every day. These ‘content’ would be meaningless to us as users if it were not structured by the Data Scientists working at Netflix. They study and understand user behaviour based on the enormous volume every user generates during their use of the entertainment website. After modelling this behavioural data, they create personalised streaming experiences and display which movie or show has the greatest percentage match with the users’ past history.
Difference between Big Data & Data Science
It is the umbrella term that encompasses most things related to data — from the generation of data to data cleansing, visualizing, mining to analytics and deals with both raw data and structured data (information). The science encompasses statistics, programming, mathematics, problem-solving, to name a few.
Analytics of Big Data is all about examining raw data to support decision making in the fields of business intelligence. Algorithmic processes, when applied will derive operational visions for multifaceted business solutions. In short, it needs to be inspected, transformed, cleansed and modelled into information.
Digital advertisement: You will notice that whenever you open any website supported by advertisements, the advertisements are related to browsing history! Data science algorithms and machine learning are used by every digital marketing domain like Google AdSense or Media.Net to personalise the ads you see.
Internet search: Sometimes when you search for a term or run a query in your browser in both the normal mode and incognito mode, it will surprise you how the search results are different in the two browser windows. That is because we live in a sort of filter bubble, where when we are logged into our accounts, based on the browsing history of that account, the search results are filtered.
Recommender systems: As we talked about Netflix, several other such websites are using and developing many algorithms to make powerful recommender systems. Such websites usually cater to the user’s preferences. .
Gaming sector: A single frame of your favourite online game can require 100mb of data to render. Imagine how much Big Data is generated from the server in a single gaming session online.
Healthcare sector: Hospitals and Healthcare service providers store big data to analyse in order to perform tasks like track and optimise patient influx, track the use of equipment and medicines in the facilities, organise patient information, etc.
Travel sector: Travel agencies generate big data from their customers to optimise their services and travel itineraries through various channels. Consumer preferences are studied to offer them vacation or experience options best suited to their interests – which is more than likely to optimise conversions.
3. Job Responsibilities
The major responsibility of data science can be captured in two words – exploratory analysis. As the term suggests, the science explores and analysis the data, with a combination of machine learning algorithms. The analysis can either predict an outcome – such as the US housing market crash of 2009 with the help anomalies and trends, both hidden and obvious.
Big Data is large is more than one terabyte and unstructured as it is captured from multiple sources. Future solutions are dependent on the data and the structure,
The behaviour and structure for future solutions and how they can be delivered by applying different technologies like Spark, Hadoop, etc. based on the requirements.
4. Skills Required
To become a Data Scientist, you should have excellent:
- analytical skills
- data management skills
- programming skills
- technical skills
- sound knowledge of database system
As an aspiring big data analytics professional, i is necessary to develop proficiency:
programming languages skills in statistics and mathematics are required.
While the two industries are the same, the difference is really vast and can be astounding. A Data Scientist in India earns a much higher salary than a Big Data Analyst because of the skills they have that can help organisations unearth the trends necessary to create marketing plans that help bring in profits.
5. Pay Scales
A Data Scientist can earn an average salary of about is ₹7,08,012 per annum.
An average Big Data Analytics professional can earn Rs. 7,24,280 per annum
6. Career Options
Data Scientists are fast becoming the backbone of the companies they work for, as it is their ability to read data that helps companies achieve success. Here are some of the career options that you can explore:
Data/Infrastructure/Enterprise Architects are tasked with building solutions for design analytics, tracking applications behaviour, and overseeing business systems.
Data Scientists are typically responsible for handling data which can include cleaning, mining, visualizing data to unearth hidden information in the form of trends.
Data Analysts/Engineers are responsible for flushing out and processing the data sets. It is important to identify the data sets that useful for the companies and then process them in real time.
Statisticians are the backbone of actuarial sciences and other industries as they interpret statistical information.
You have to start with junior positions such as junior data analyst or junior data scientist, before you can move on to a more meaningful role in your career.
With billions of bytes of data being produced across the world, it should come as no surprise that there are several career options available to Big Data Analysts. Some of the options you can explore are:
Big Data Engineers are responsible for building designs, followed by testing and maintaining the design along with solution analysts.
Big Data Analysts are well-versed in Hadoop and other technologies. They are responsible for finding information from the huge data sets which statisticians and scientists can use.
Business Intelligence Engineers are managers of the data warehouses. They create queries and are involved in solving complex issues.
So, what are the steps that you need to follow to become a renowned Big Data Analytics
You should focus on studying data analysis or applied statistics to develop skills for project and database management.
Remember, employment without experience is difficult and hence, you would be wise to search for internship offers that allow you to work with, or as, Big Data analytics professional. The experience you gain as an intern could be the first step towards a very successful career.
Begin as an assistant and then once you develop the confidence to work on your own, move to managerial or team leadership positions.
7. Basis of Formation
In the field of Data Science, scientific applications are used. These applications help the data scientist to extract information or unearth trends hidden in Big and other data.
The field is related to filtering data followed by preparing it for analysis.
Apps and tools are used to filter patterns and develop working models and solutions.
Big Data is usually captured by the high volume of Internet traffic.
Users behavioural patterns and preferences are captured via electronic devices, AV feeds, online forums, and other digital mediums.
Organisational data from emails and spreadsheets as well as system logs can be captured as Big Data.
The best way to succeed in a career is to get trained. Now training can be done with:
- Professional courses offered by upGrad
Additional classes offered by schools and colleges
- Training opportunities offered by the company you work for.
Not only will you develop the knowledge critical to be an analyst, but it could be the stepping stone to success.
Education is the key to success, and any advanced degree that you work for, will bring in more and better job opportunities.
Today, it is all about automation and technology. Hence, familiarising yourself with advanced and latest tools and technologies through degrees and diplomas in the field of data is important for success.
Also, educational websites offer certifications that amalgamate theory with practical knowledge and experiences. There is no need to put your career on hold to get certified. You can join online classes and get the certification you are looking for.
As is evident from the tables shared above, the two fields are quite similar to each other, with a fair amount of overlap.
Big Data is a humungous volume of data – minimum one terabyte of data is considered Big Data. But, with millions and trillions of data being captured across the world, the data sizes a Big Data analyses have increased to 1024 terabytes or petabytes or 1024 petabytes called exabytes.
The data sizes are growing, and according to Forbes magazine, data will be generated at a rate of 1.7 million MB per second in 2020. Only experts in the field of Big Data can manage the unstructured data to make it usable for others.
Data Science, on the other hand, takes care of cleaning, mining, preparing and analyzing data. The Data Scientist will use tools at their disposal to create graphs, read patterns and unearth anomalies that can shock and surprise organizations. Operations are planned around these analyses, making them a crucial element in the growth of a single unit or an industry. Not many people are aware that some financial analysts unearth the anomalies of the US Housing market and prepared for the crash, raking in millions of dollars.
The two may compete, but they are incomplete without each other. Data Science needs the data to function, and Big Data requires scientists and analysts to be relevant. Choosing one field over the other is a matter of personal preference and inclinations.
Both are the hot domains, and you could do well in either of them if you are equipped with the right knowledge and education while staying on top of industry trends. Of course, it has to be backed by the experience to build expertise. In the future, the option to shift from one to the other is always there.
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