These three terms: Data Science, Big Data, and Data Analytics are highly related to each other and are often used together in several contexts. But they greatly differ in themselves. While data science widely refers to the technology of extracting insights from data, data analytics is the process that enables data science with the extraction of insights. And not to mention, big data is the fuel that powers data science. It is the big data that is analyzed and through which relevant outputs are extracted.
Though they all belong to the same industry, different tools are used to perform or handle each of these operations. Hadoop, NoSQL, Hive, etc. are used to handle and process Big Data, whereas R, Tableau, and Spark, are some of the best tools to perform data analytics.
Your job role will depend upon which technology you choose. For example, if you want to get into big data thing, then you can opt to become a big data engineer who earns around $154,020 on average salary per annum in the US. And if data science interests you, then you can go for the Data Scientist job role. They also earn huge i.e., around $156,919 per annum on average.
Sounds interesting, right? Learn more about them in detail in this infographic guide.
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