Data is one of the biggest assets any company has in the present time. This in fact was long predicted by Forbes when it stated: ‘The total data market is expected to nearly double in size, growing from US$69.6 billion in revenue in 2015 to US$132.3 billion in 2020.’
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Now with the advent of the digital economy, varied avenues have opened up in the Big Data landscape. Data Science, Data Analytics, Data Mining, Data Engineering, etc., all work together on a single platform but perform very diverse and significant jobs. Most of the time, people use these terms interchangeably, but indeed there are huge differences among these concepts.
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A similar kind of ambiguity exists with the terms Big Data, Data Science, and Data Analytics. Aspirants often mistakenly opt for a different job role which does not match with their skills. Therefore, it is of utmost importance for you to know the differences among them before moving ahead in a certain direction for a better career and hence, in this blog, we would be discussing on Data Science vs Data Analytics vs Big Data.
Big Data, Data Science, and Data Analytics: What are they?
Big Data, Data Science, and Data Analytics are not just some technical jargons but are significant concepts contributing to the field of technology. While these terms are interlinked, there is a huge fundamental difference between them.
Big Data refers to a huge volume of data of various types, i.e., structured, semistructured, and unstructured. This data is generated through various digital channels such as mobile, Internet, social media, e-commerce websites, etc. Big Data has proven to be of great use since its inception, as companies started realizing its importance for various business purposes. Now that the companies have started deciphering this data, they have witnessed exponential growth over the years.
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6 million developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today – Forbes
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Data Science deals with the slicing and dicing of the big chunks of data, as well as finding insightful patterns and trends from them using technology, mathematics, and statistical techniques. Data Scientists are responsible for uncovering the facts hidden in the complex web of unstructured data so as to be used in making business decisions. Data Scientists perform the aforementioned job by developing heuristic algorithms and models that can be used in the future for significant purposes. This amalgamation of technology and concepts makes Data Science a potential field for lucrative career opportunities. McKinsey once predicted that there will be an acute shortage of Data Science Professionals in the next decade.Click Here
Data Analytics seeks to provide operational insights into complex business situations. Looking into the historical data from a modern perspective, finding new and challenging business scenarios and applying methodologies to find a better solution are the prime concerns of a Data Analyst. Not only this, but a Data Analyst also predicts the upcoming opportunities which the company can exploit. Data Analytics has shown such a tremendous growth across the globe that soon the Big Data market revenue is expected grow by 50 percent.
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Forbes quoted, ‘Big Data Analytics and Hadoop Market accounted for US$8.48 billion in 2015 and is expected to reach US$99.31 billion by 2022, growing at a CAGR of 42.1 percent.’
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How are they impacting the economy?
Data is the baseline for almost all activities performed today, whether it is in the field of education, research, healthcare, technology, retail, or any other industry. The orientation of businesses has changed from being product-focused to data-focused. Even a small piece of information is valuable for companies nowadays, making it essential for them to derive more and more information possible. This necessity gave rise to the need for experts who could bring meaningful insights.
Big Data Engineers, Data Scientists, and Data Analysts are similar kind of specialists who wrangle with data to provide industry-ready information.
|Impact on Various Sectors|
|Big Data||Data Science||Data Analytics|
It is evident from this table how these areas impact our economy. Actually, technologies are helping diverse sectors in a great way, allowing them to put each and every piece of insight into use. While Big Data is helping the retail, banking, and other industries by providing some of the important technologies such as fraud-detection systems, operational analysis systems, etc., Data Analytics allows the industries of healthcare, banking, travelling and transport, energy management, etc. to come up with new advancements using the historical trends. On the other hand, Data Science is letting the companies get into web development, digital advertisements, e-commerce, etc., and dive deep into the granular information for different purposes.
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What salaries do these professionals get?
The work profiles of all the three are entirely different, which makes their salaries vary from one another. As per the above-given table:
- Data Scientists perform the most challenging jobs among the three.
- Data Scientist is one of the most trending profiles in the 21st century.
- There is considerable overlapping of roles between Data Analysts and Big Data Professionals.
Data Science is booming like anything and hence has been tagged as the sexiest job of the 21st century by Forbes. This makes Data Scientists be at the top when it comes to salary, i.e., around US$123,000 per year. Next are Big Data Specialists who earn around US$62,066 per year, followed by Data Analysts with an annual income of around US$60,476 per year.
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Skill Sets Required for These Profiles
Skill sets required to become Data Scientists, Data Analysts, and Big Data Professionals are different. Though there are some skills that are common in all the three profiles, the level of proficiency varies as per the job roles. Therefore, you should clearly know what you want to become and what skills you need to have for that.
In order to become a Data Scientist, you need to be proficient in mathematics, statistics, programming, and business strategies. You should have good communication skills, as a Data Scientist needs to distribute the information to various departments of an organization. Similarly, a Big Data Professional would require to have a good grasp of technology (such as Hadoop and Java), mathematics, and statistics, as well as analytics. However, a Data Analyst needs to be good in programming, Artificial Intelligence, and data wrangling.
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Now, with all this information, you can make your decision wisely while enhancing your skills.
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