Data Analytics is the scientific process of refining raw data in order to get meaningful information.
There are algorithms which can be used to analyse the data in a scientific manner and some mechanical processes also can be used.
The information extracted from the refining process can be used to improve overall efficiency of a business system, and thereby its overall revenue
What is Data Analytics
Data Analytics is a broad term that refers various types of data analysis. For example manufacturing machines can use these data analyzing methods to sort out the different attributes should be used at which level or quantity in order to get the maximum outcome.
Content companies use majority of data to keep you clicking, watching content to get another view or click as well.
There are several steps involved in data analytics
 The primary step determines how the data is to be grouped. They may be grouped according to the age, demographic, wealth or gender. Data values can be numerical for easy calculation
 As second step collect the data for analysis. This can be done through computers, online resources or through direct approach
 Once the data is collected, it must be organised so that it can be analysed. Organisation may take place on a spreadsheet or other form of software which take statistical data as input.
 Next process is the cleaning of the raw data before analysis. It is scrubbed and checked to ensure there is no duplication or error and is complete.
Why Data Analytics become important
Data analytics become important because it helps businesses optimize their performances.
With the help of analysis, a business model can be implemented in companies so that their production cost can be reduced considerably by identifying more efficient ways of doing their day to day activities and by storing large volume of data.
You would get the proper idea why the analytics is important through this link given below
They can also use data analytics to make better business decisions and in helping customer trends and behaviors as and when required, or to improve their product’s quality whenever a new product is launched.
Types of Data Analytics
Data analytics is broken down into four basic types.

Descriptive Data analytics
Descriptive Data analytics uses historical data to find what happened in an organisation for a specific past time. What about the number of views and sales for a particular period say one month and is usually used to make a comparative study of sales at specific periods.
2.Diagnostic Data analytics
It focuses on some incidents taking place in the organisation rather than their product’s behaviour for a period of time. This involves more diverse inputs and hypothesizing. Ask some questions related to some specific events and try to find the answer is in this category of analysis. In the discovery process, analysts identify the data sources that will help them interpret the results. Drilling down involves focusing on a certain facet of the data or particular widget.

Predictive Data analytics
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.
In business, predictive models exploit patterns found in historical and transnational data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decisionmaking for candidate transactions.
4.Prescriptive Data analytics
It suggests a course of action. Prescriptive analytics makes use of machine learning to help businesses decide a course of action based on a computer program’s predictions. Prescriptive analytics works with predictive analytics, which uses data to determine nearterm outcomes.
Data analytics implements many quality control systems in the financial world, including the everpopular Six Sigma program. If you are not properly measuring something—whether it’s your weight or the number of defects per million in a production line(probability) —it is nearly impossible to optimize it.
You would get more information on all these 4 types of data analytics
https://studyonline.unsw.edu.au/blog/descriptivepredictiveprescriptiveanalytics
Use Case
Here Data analytics is used for diagnosis to find the disease by analyzing symptoms in patients
A sample space of 10 patients is given here for the easiness of calculation
Sore Throat  Fever  Swollen Gland  Congestion  Headache  Diagnosis  
P1  yes  yes  yes  yes  yes  Strep Throat 
P2  No  No  No  yes  No  Allergy 
P3  yes  yes  No  yes  No  Cold 
P4  yes  No  Yes  No  No  Strep Throat 
P5  No  Yes  No  Yes  No  Cold 
P6  No  No  No  Yes  No  Allergy 
P7  No  No  Yes  No  No  Strep Throat 
P8  Yes  No  No  yes  yes  Allergy 
P9  No  Yes  No  yes  yes  Cold 
P10  Yes  No  No  yes  yes  Cold 
Here is the formula to calculate information gain
Then we should calculate the Entropy of sample space I(P,n)
Our sample space consists of 10 data for diagnosis (ST (3),A(3),C(4)
Now find the entropy of each attribute. To start first attribute is the Sore Throat. Take the value of yes in sore throat
attribute and count corresponding Allergy and Cold diagnosis against yes value.Repeat the same for No value as well.
ST  A  C  
Y  2  1  2 
N  1  2  2 
Now we have to find the entropy (yes) as well as entropy(no) for sore throat attribute using the above formula as shown below.
We get the result as 1.51 as the approximate value for entropy of sore throat (yes) attribute.
Now calculate entropy of sore throat (no) attribute.
Entropy of Sore throat is given by
E(ST)=prob*E(y)+prob*E(y)
=0.5*1.52+0.5*1.52=1.52 approx
Then Information gain of Sore throat
I(ST)=E(sample space)E(attribute)=1.561.52=0.05
Using this formula find the Information gain of all attributes
ST  0.05 
Fever  0.72 
swollen Gland  0.88 
congestion  0.45 
headache  0.05 
Now to draw the decision tree find the root node. The attribute which have highest information gain is the first root node. Here swollen node is selected as root node
In the case of yes value diagnosis is steep throat. For no value more than one attribute plays the role of diagnosis. There fore we have to select the next root node that is fever here because which have next highest gain value.For no value of fever allergy is the diagnosis and for yes value cold is the diagnosis.
In this way we can construct decision trees for any sample space using leaning algorithms
Summary
Data analytics is a very fantastic area of study as well as career path for young professionals. Using this decision tree algorithms, we can make useful predictions in various areas in the new world. The jobs in this area promises descent salary when compared to other professionals.
If you want to study more on data analytics please go through following links
To become a freelance data scientist