Data is one of the biggest assets any company has in present time. This in fact was long predicted by Forbes in 2015 when it stated that “The total Data market is expected to nearly double in size, growing from $69.6B in revenue in 2015 to $132.3B in 2020.“
Now with the advent of 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 times people use these terms interchangeably but indeed there are huge differences among these concepts.
A similar kind of ambiguity exists in the terms big data, data science and data analytics. Aspirants often mistakenly opt a different job role which does not match with their skills. Therefore it is utmost important for you to know before moving ahead in a certain direction for better career.
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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 in the field of technology. While these terms are interlinked there is a huge fundamental difference between them.
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Big Data- Big data refers to the huge volumes of data of various types, i.e., structured, semi-structured, and unstructured. This data is generated though various digital channels like 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.
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- Data science deals with slicing and dicing of the big chunks of data as well as finding insightful patterns and trends using technology, mathematics and statistical techniques. The 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 heuristics algorithms and models that can be used in future for significant purposes. This amalgamation of technology and concepts make data science a potential field for lucrative career opportunities. McKinsey once predicted back in 2013 that there will be an acute shortage of data science professionals in the next decade.
Data analytics- Data analytics seeks to provide operational insights into the 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 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%.
Forbes quoted “big Data Analytics & Hadoop Market accounted for $8.48B in 2015 and is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 to 2022“.
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How are they impacting the economy?
Data is the baseline for almost all the activities performed today whether it is 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 the companies now-a-days making it essential for them to derive more and more information possible. This necessity gave rise to the need of certain experts who could bring meaningful insights to be utilized.
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Big data professionals, data scientists and data analysts are the similar kind of specialists who wrangle with the data to provide industry-ready information.
|Impact on various sectors|
|Big Data||Data Science||Data Analytics|
It is evident from this table that how these areas are impacting 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 allow the healthcare, banking, travelling and transport, energy management, etc., industries 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., dive deep into the granular information for difference purposes.
What salaries do these professionals get?
The work profiles of all the three are entirely different which makes their salaries to vary from one another. It is clearly depicted in the above table that-
- Data scientists perform the most challenging jobs among the three
- Data scientist is one of the most trending profile in the 21st century
- There is a considerable overlapping of roles between data analysts and big data professionals.
Data science is booming like anything and hence has been tagged as the sexist job of 21st century by Forbes. This makes data science to stand at the top when it comes to salary, i.e., around $123,000 per year. Next are the big data specialist who earn around $62,066 per year followed by the data analysts with an annual income of around $60,476 per year.
Skill set required for these professions
The skill set required to become a data scientist, data analysts and big data professional is different. Though there are some skills that are common in all the three profiles but 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 as well as business strategies. You should have good communication skills as a data scientist needs to distribute the information to various departments of the 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.
Now with all this information you can make your decision wisely taking your skills into consideration. Stay in touch with Intellipaat.com for more information on Big data and related trending technologies and training courses.
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