Clarifying data analytic concepts for Industrial Engineering

Clarifying data analytic concepts for Industrial Engineering

1. Data analytics wants to analyse raw data to discover hideen patterns and relationships among different variables.

   • Extract information from rough data and transfer to effective knowledge to improve processes and products and make better decisions.

2. Data mining

   • Extraction of unknown and potentially useful information from data
     • Construct numerical algoithms to extract patterns that can be use to predict future outcomes

3. Machine learning

   • Developed most of the algorithms to describe structural patterns in data
     • Represents technology instruments of data mining
       • Data mining and machine learning can be grouped in 2 sets
         • Supervised learning: predicting an outcome value based on the measures of other input variables
         • Unsupervised learning: description of associations and patterns among a set of input data. Find hidden patterns
   • Linear methods for regression: how the inputs afect the outputs. 2) Lineal method for classification: identify class or group 3) Decision tree based method:nodes test a attribute. 4)Neural networks, 5) Association rules, 6)Asociation rules (find joint values of the input variables), 7) cluster analysis, 8) Self-organizing maps,

4. Data analytics

   • Composed of statistical techniques, data mining , machine learning algorithms, statistical tests

5. AI

   • Perform actions; learning, communication and decision making to arrive to a solution imitating intelligent behavior of human expertise

6. Big data

   • Types of datasets, information characterized by high volume, variety and velocity to require specific Technology for its transformation into values