Data Analytics- Introduction

What is it ?

“Data analytics is all about gaining insights from data and using it to solve a problem statement.”

It has become a part and parcel of various domains such as biological research, cosmic exploration, stock market analysis, financial management, travel planning, advertisement industry, manufacturing industry etc.

Types of data analysis:

a) Descriptive Data Analysis (What?): The amount of data under study can be humongous and to understand the aspects of it can become overwhelming. Descriptive statistics helps us in creating simple summaries which further combined with graphs and charts can be used to construct a coherent representation of data. For example, it can be used to represent current sales situation of a company to its board members.

b)Diagnostic Analysis (Why?): It is an analysis of data which helps us to understand causal relations. It gives us insights to determine which features of data are responsible for a given outcome or an event. It is a deeper look at data than descriptive analysis. For example: it can be use to determine the cause of a failure at manufacturing pipe line or cause of a man made disaster.

c)Predictive Analysis (what may ?): It is about understanding the data and designing models to forecast the probability of an event or extrapolate the data in hand. For example, a technical support assistance model that predicts urgency of a user queries can help the support team to deal the customers accordingly. Furthermore predictive analysis methods can help a manufacturer to forecast the demand and supply of a material.

d)Prescriptive Analysis (How to?): It includes running simulations based on different approaches to solve a problem statement. It can be thought of as optimization algorithm seeking to continuously learn from the events and what causes them. For example, prescriptive models used in logistics industries help to find best possible way to deliver a package taking various variables into account like traffic, blockade, time, distance etc.

Various tools available to perform data analysis are MS Excel, WPS Office, R, Python, Tableau Public, SAS, KNIME etc.

Conclusion:- Data Analytics is a field which has it roots everywhere. The type of analysis a person should choose for his use-case is dependent upon the data available and the problem statement at hand.

Instead of just being career option it has become a necessary skill a person should have.

That’s all.