Introduction:
In layman’s terms, Data Science, Data Analytics and Business Intelligence are used interchangeably. However, there are a plethora of differences between these three if seen from an expert’s eyes.
Data Science is a wider term that contains within its ambit everything that involves mining large data sets. Data Science is not merely related to statistical or algorithmic aspects of data mining but it is actually a melange of programming, statistics, and big data analytics. Business Analytics, on the other hand, can be deemed to be an end-product of data science. It is further divided into two categories, they are, Statistical Analysis and Business Intelligence.
Difference between Data Science and Business Analytics:
The primary difference between Data Science and Business Analytics is related to the problems addressed by both. Data science is related to the utilization algorithms and technology for the purpose of analyzing data. It is used mainly for the purpose of both structured and unstructured data.
On the other hand Business Analytics involves the study only of well structured business data.
Difference between Data Science and Data Analytics:
Data Science uses concepts such as data mining, data inference, predictive modeling, and Machine Learning algorithm development, to transform complicated data sets into actionable business strategies. Their responsibility is to clean, process and validate the data.
Data Analytics, on the other hand, mainly employs Statistics, Mathematics, and Statistical Analysis to uncover the basic specifics of insights extracted using Data Science techniques. Their responsibility is to collect the data and interpret it.
Difference between Data Analytics and Business Analytics
The role of a Data analyst entails the analyzing of data to unearth the recent trends. These trends are then used for the purpose of making informed organizational decisions.
On the other hand, in Business Analytics the analysis is performed for a specific type of information which helps a business in making practical and data-driven decisions. This also facilitates the implementation of changes based on those decisions, the identification of problems and finding the solutions.
Conclusion:
The difference between these there is negligible for someone who is just venturing into the studies of Data Science. Hope this blog cleared all the questions you had regarding the topic.
