What is Data Analytics?

Analytics is the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.

Questions that can be answered by data analytics include:

Question 1. Who are the most profitable customers?

Answer 1.: Use database query to retrieve customers and sort customer spending by cumulative amounts

Question 2. Is there a difference between profitable customers and average customers?

Answer 2. Hypothesis testing. For example, the value of profitable customers is significantly different from that of an average customer, with a probability of less than 5 percent. 

The benefits of being analytical are broad. 

Better understanding of the dynamics of business environment.

Know what is working for the business and what is not.

Leverage from previous experiences to get more insights, faster execution, and derive more business value.

Cut costs and improve efficiency through optimization techniques. Predictive models can anticipate the future and thus enable businesses to make early adjustments.

Data Analytics can be applied to Education.

Some universities analyzed the quantitative and qualitative feedback of the students’ evaluation of their professors.

Classes improve by using shortlist keywords that characterize a good professor.

Technology utilized in data analytics includes text mining, sentiment analysis, machine learning, data mining, classification, etc. One of the popular methods is the decision tree. The decision tree is like a root node at the top of the tree, to interior nodes, to leaves representing class values.

Clustering is also a useful technique in data analytics. In clustering, samples are separated into two or more separate clusters by characteristics such as color, shape, size, etc. However, there are some issues with clustering. It is often difficult to understand what the clusters reveal.​​