Data is one of the greatest holdings any business has in present time. Data Science, data analytics, data mining, data engineering, etc., all work together on a single platform but perform very diverse and significant jobs in different scenarios. Many times people use these terms interchangeably but indeed there are huge differences among these models.
A similar kind of uncertainty is there in the terms like- big data, data science and data analytics. Applicants often get confused and opt for different job role which does not match with their skill-sets. Therefore, it is utmost important for you to know before moving ahead in a certain direction for better career.
What is the Actuality?
Big data and data science are not just some technical terminologies but are considerable theories contributing in variety of ways. While these terms are interlinked there is a structural difference between them.
Big Data- Big data is when we deal with mass data and of data of various types, i.e., structured, semi-structured, and unstructured. This data is generated through various digital frequencies like- Internet, Cell phones, Desktops, websites, social media, telecom media, satellite etc. Big data has shown one of great aspect since its establishment, as companies started recognizing its value for various business objectives. Now that the companies have started interpreting this data, they have witnessed potential growth over the years since then. Big Data is one of the major parts in Data Science, which calls for software or algorithms like Hadoop, etc.
Data Science- Data science do the carving and cubing of the big chunks of data as well as finding perceptive outlines and tendencies using technology, mathematics, probability and statistical techniques. The data scientists are responsible for uncovering the facts hidden in the complex web of unstructured data, so that can be used as a decision-making point or considering points while take the chief verdicts related to the development of the organization. Data scientists perform the job by developing algorithms and models that can be used in future for significant purposes. In simple words, the science of tackling information in a way that can be presented in the form of report. Data Scientist refines the data and keep the useful information for future purposes by using different programming languages, algorithms, tools and techniques. This incorporation of technology and concepts make data science a potential field for profitable career opportunities. McKinsey once predicted back in 2013 that there will be an acute shortage of data science professionals in the next decade.
Conclusion-
Figures and numbers are the baseline for almost all the activities achieved, be it is education, research, non-technology, healthcare, technology, retail or any other industry. The alignment 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 compulsion gave growth to the need of certain experts who could bring meaningful insights to be utilized.
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