Data Analytics refers to the study of structured data to draw inferences from it. Data analytics has a close relation to topics like Bid Data and Data Science but these are not the same. While Data Analytics refers to the study of data, Data Science involves the methods used to cleanse the data and prepare it for analysis. Big Data, on the other hand, refers to the huge voluminous data that is being generated today.
For example, take your Fitbits, they are connected to the internet. These gadgets send data continuously. This data can be personal data like your heartbeat or general data like environment. Now consider this data being sent to a place from all over the world by millions who use these Internet Connected Devices. The data is too large. It is not your average 2TB hard disk data. That is where Big Data comes in.
Now take that data and process it. You need to eliminate data points called “outliers” from the data. These can severely affect the outcome as these do not coincide with your regular data points. You are now cleaning the data. That falls under Data Science.
Now take the cleaned data. Observe it. Draw Conclusions from it. For example, say from the data from Fitbits, it can be concluded that most people like to go out jogging in the time frame of 5am-7am. So the Fitbits in the future would have to be modified to be better functioning during that interval. You are now analyzing data, drawing inferences from it. And that falls under Data Analytics.
Data Analytics can be employed for a variety of other purposes like improving the product quality of a certain milk product based on patterns of buying. The main things required for this are programming skill, a bit of statistics and Machine Learning and you are set!