What is Data Mining? What Kinds of Data can be Mined?

Data mining is also known as Knowledge Discovery from Data, or KDD for short, which turns a large collection of data into knowledge. Data mining is a multidisciplinary field including machine learning, artificial intelligence, pattern recognition, knowledge-based systems, high-performance computing, database technology, and data visualization. 

• Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. 
• Data mining is the process of finding hidden information and patterns in a huge database. 
• Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.

 

Data, Information, and Knowledge
Data: Data are any recorded facts, numbers, or text that can be processed by a computer - scientific data, medical data, demographic data, financial data, and marking data.

Information: The patterns, associations, or relationships among all this data can provide information.

Knowledge: Information can be converted into knowledge about historical patterns and future trends.

 

What Kinds of Data can be Mined?
The most basic forms of data for mining are come from: 

1. Database Data 
2. Data Warehouses 
3. Transactional Data

Descriptive, Predictive, and Prescriptive analytics classification questions

 Please read about Descriptive, Predictive, and Prescriptive analytics here: https://triksbuddy.blogspot.com/2020/12/what-are-different-types-of-analytics.html

And try to answer the following questions and match with correct answer below questions:

1. A classification of our customers into four quantiles according to their profitability in the last four quarters. 

A. Descriptive
B. Predictive
C. Prescriptive

 

2.  A classification of our customers into four quantiles according to their expected profitability in the following four quarters.

A. Descriptive
B. Predictive
C. Prescriptive

 

3. A model that assigns a credit limit to each customer such that it optimizes our bank’s expected profits in the next four quarters.

A. Descriptive
B. Predictive
C. Prescriptive

 

4. A list of our best 10 customers on the basis of their sales growth in the last quarter.

A. Descriptive
B. Predictive
C. Prescriptive

 

5. A list of the 10 customers that are most likely to leave our company in the next two quarters.

A. Descriptive
B. Predictive
C. Prescriptive

 

6. A model that assigns to each credit card transaction a score that represents its probability of being a fraudulent transaction.

A. Descriptive
B. Predictive
C. Prescriptive

 

7. A model that outputs a preventive maintenance schedule of airplane engines such that it minimizes our airline’s annual maintenance and downtime expenditure.

A. Descriptive
B. Predictive
C. Prescriptive

 

8. A model that schedules the timing of the posting of an individual’s tweets so as to maximize the daily number of associated retweets.

A. Descriptive
B. Predictive
C. Prescriptive

 

9. A list of students that are in high risk of dropping out of our university in the next two semesters.

A. Descriptive
B. Predictive
C. Prescriptive

 

10. A model that suggests an individualized student degree completion path such that it minimizes the likelihood that the student will quit her studies before competing her degree.

A. Descriptive
B. Predictive
C. Prescriptive

 

 

 Answer: 

1. A 

2. B  

3. C  

4.  A

5. B

6. B

7. C

8. C

9. B

10. C

 

What are different types of analytics? Describe different types of analytics.

Analytics: 

The term analytics is used to characterize a vast array of methods that use data to help make better business decisions. And there are many ways to organize them into subcategories. The simplest way is to distinguish analytics into three large classes

• Descriptive Analytics
• Predictive Analytics, and 
• Prescriptive Analytics

Descriptive Analytics: The most basic form of analytics are descriptive analytics. The simplest way to define descriptive analytics is that it answers the question what has happened. Descriptive analytics typically condenses large amounts of historical or real-time data into smaller, more meaningful nuggets of information.

For example, in an internet marketing context, descriptive analytics could be used to summarize a large number of search, display and social media advertising campaigns into a smaller set of metrics that shows the average click-through rate, conversion rate, and the return on investment of each of these three advertising channels.

The main objective of descriptive analytics is to find out the reasons behind success or failure in the past. The vast majority of big data analytics used by organizations falls into the category of descriptive analytics.

Predictive Analytics: The next class of analytics, predictive analytics, uses data from the past to predict what will happen in the future.

For example, suppose we would like to predict the likelihood that a new prospective customer will respond to a promotional email campaign. By analyzing past data that includes situations where prospects responded and did not respond to similar campaigns in the past, analytics can help identify what distinguishes those prospects who responded from those who did not. On the basis of this data, a model can be built that can help assess the probability that the new prospect will respond to a future campaign. 

As we can see, predictive analytics is based on a solid understanding of what happened in the past. So most organizations are deploying them after they have mastered the art and science of descriptive analytics.

Prescriptive Analytics: Armed with models of the past and forecasts of the future, organizations can then venture to the more advanced level of analytics, prescriptive analytics.

This class of method is using optimization algorithms to determine the set of actions that optimize a desirable objective, given predictions of what is likely to happen. 

Referring to the previous example, assuming that we have built predictive models for a number of different campaigns, a prescriptive analytics model could be used to determine which campaign should be sent to which prospects in order to maximize our expected sales and stay within our marketing budget.

As organizations gain experience and skills with data-driven business decision-making, they typically progress from descriptive, to predictive, and finally to using prescriptive analytics to inform decisions and actions in a growing number of functions.