An ultimate guide to Data analytics, techniques and Tools

Data analytics is the process of analyzing raw data in order to draw meaningful and useful insights, which are used by businesses to make informed and smart business decisions. It helps to predict future trends and behaviors instead of basing your strategies and decisions on guess work, thus making informed decisions according to what the data is telling you.

There are four key types of data analytics: predictive, descriptive, diagnostic, and prescriptive. Each of them helps the organization to make informed decisions and they tells us the following:
 Descriptive analytics tells us what happened in the past.
 Diagnostic analytics tells us why something happened.
 Predictive analytics tells us what will likely happen in the future.
 Prescriptive analytics tells us how to act and decisions that should happen.
The engineers who work with data explores these four areas using data analysis process, which includes identifying the question, collecting raw data, cleaning data, analyzing data, and interpreting the results.
Below are some of the data analytics techniques used by data analysts.
 Regression analysis which is used to estimate the relationship between a set of variables.
 Factor analysis helps the data analysts to identify the underlying variables that drives people’s behavior and the choices they make.
 Cluster analysis helps in identifying structures within a dataset.
 Time-series analysis helps in measuring the same variable at different points in time
Now lets us look at some of the tools which data analytics engineers might work with.
 Microsoft Excel
 Tableau
 SAS
 RapidMiner
 PowerBI