Clarifying data analityic concepts for Industrial Engineering

Clarifying data analityic concepts for Industrial Engineering

1. Big Data

   • Types of datasets
     • Information charactarized by high volume, variety and velocity to require specific technology for transformation into values

2. Machine learning

   • Developed most of the algorithms to describe structural patterns
     • Represents technologyinstruments of data mining
       • Data mining and machine learning can be grouped in 2 sets
         • Supervised learning (predict an outcome based on the measures of the other variables
         • Unsupervised learning (description of asociations and patterns among a set of input data,
   • Linear regressions: how the inputs afect the outputs
     • Linear method for classification: class or group
     • Decision tree based on nodes test an attributes
     • Neural networjs
     • Association rules
     • Joint values of the input variable
     • Cluster analysis
     • Self-organizing maps

3. Al

   • Performs actions
     • Learning, communication and decision making to arrive to a a solution (simulating human behavior)

4. Data analytics

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

5. Data mining

   • Extraction of unknown and potentially usefull information
     • Contrsuct numerical algorithms to extract patterns to predict future outcomes

6. New data 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.