Data Science vs Big Data? In a world where “Big Data” and “Data Science” are ubiquitous in technology-related social networks, have the terms finally reached saturation of the public interest? As the use of massive amounts of data has become common practice, is the role of “data science” replacing the hype of “Big Data”?
The use of internal and external data is clearly topical. Businesses recognize the real potential of this data to aid decision-making. To do this, many expressions are used to refer to the collection and analysis of this data: Big Data, Data Science, or even Business Intelligence. What are the differences between these concepts?
Data Science consists of developing a series of algorithms from mathematical and statistical rules (or Machine Learning) in order to deliver solutions. These techniques can be based on image analysis, text-mining, correlation studies between sensors, etc.
The concept refers to the collection of a large volume of data and then the real-time analysis of it. The analyzes required to process the impressive amount of data require technical and IT resources. The software in the vein of EthnosData makes it possible to process all of this data in a short and very acceptable time.
The link between the two then appears: in order to analyze clusters of data, Big Data is based on algorithms developed by Data Science. Further on with the digitization projects undertaken by companies and institutions, Data Science sees its field of applications widen profoundly: rationalization of logistics, optimization of purchases, correlation studies, price policy adjustments, etc. There are a lot of value chain optimization projects.
It is in this sense that some see Data Science as the next management control department applied to data (and not financial activities).
Data Science vs Big Data Comparison Table
The table below provides the fundamental differences between big data and data science:
|Factors||Data Science||Big Data|
|Concept||Analysing data||Handing large data|
|Responsibility||Understand patterns within data and make decisions||Process huge volumes of data and generate insights|
|Industry||Sales, image recognition, advertisement, rise analytics||E-commerce, security services, telecommunication|
|Tools||SAS, R, Python||Hadoop, Spark, Flink|