Clarifying Data Analytics Concepts for Industrial Engineering

Clarifying Data Analytics Concepts for Industrial Engineering

1. Main concepts:

   • Data mining is the extraction of implicit, previously unknown and potentially useful information from data.
   • Data analytics is composed both of data mining (and its technical algorithmic counterpart in machine learning) and statistical models to clean data at the beginning and to validate rules at the end.
   • Machine learning is the field that developed most of the algorithms to find and describe structural patterns in data, it represents the technology instruments of data mining.
   • AI systems can perform actions such as perception, interpretation, reasoning, learning, communication and decision making to arrive to a solution for a given problem, imitating the intelligent behaviour of human expertise.
   • Big Data is a type of datasets, in which variables are different in type and nature, they compel to be analysed with a number of specific techniques, models and algorithms.

2. There are some interesting research statements arise and need to be carefully detailed:

   • To understand how to capture data.
   • To understand how to analyse data.
   • To understand how to share information.

3. It is very impotant to send the right information to the right person in the right moment, which is boosted by the digitization trends that are permeating the manufacturing industry context.

4. The main aim of data analysis is to extract useful information from rough data and transfer it to effective knowledge to improve product and process understanding and to support decisions.

5. There are some machine learning techniques:

   • Linear Method for Classification
     • The linear classifier uses the variable’s characteristics to identify which class (or group) the variable belongs to.
     • The success of the classification learning can be tested on a new independent dataset, for which the true classification is known, but not made available to the machine.
   • Linear Methods for Regression
     • They are simple and often provide an adequate and interpretable description of how the inputs affect the output.
     • There are many applications of linear model regression, such as process monitoring, process control and quality prediction.
   • Decision Tree Based Method
     • Nodes in a decision tree involve testing a particular attribute.
   • Neural Networks
     • The central idea is to extract linear combinations of the inputs as new features, and then model the target output as a nonlinear function of these features.