Methods for statistical data mining (MDM)

Cour­se objec­ti­ve:
The aim of this methods cour­se is to intro­du­ce the con­cepts, ter­mi­no­lo­gy and methods of data mining. Our methods cour­ses are desi­gned to be soft­ware-inde­pen­dent and are also sui­ta­ble for users who do not work with Sta­tis­ti­ca.

Tar­get group:
This cour­se is aimed at pro­ject mana­gers and qua­li­fied spe­cia­lists in ser­vice or indus­tri­al com­pa­nies who want to ana­ly­ze their data using modern data mining tech­ni­ques.

Cour­se con­tent:
The cour­se pro­vi­des an over­view of the idea and importance of data mining, the art and science of lear­ning from data. Indi­vi­du­al data mining tools are exami­ned in more detail and their signi­fi­can­ce for dif­fe­rent ana­ly­sis tasks is explai­ned. For all tech­ni­ques pre­sen­ted, the basic metho­do­lo­gy is explai­ned and illus­tra­ted with prac­ti­cal examp­les.

The topics:

• • Intro­duc­tion to data mining
  • Data pre-pro­ces­sing tech­ni­ques
  • Pos­si­bi­li­ties of visua­liza­ti­on
  • Deal­ing with mul­ti­va­ria­te ques­ti­ons
  • Over­view of unsu­per­vi­sed lear­ning methods
  • Over­view of super­vi­sed lear­ning methods
  • Vali­da­ti­on pro­ce­du­res
  • Out­look on cur­rent method deve­lo­p­ments

We offer this trai­ning in coope­ra­ti­on with our part­ner Sta­too Con­sul­ting. The trai­ner is Dr. Die­go Kuo­nen, PhD in sta­tis­tics, CStat PStat CSci, CEO and CAO of Sta­too Con­sul­ting, lec­tu­rer at the Uni­ver­si­ty of Gen­e­va and Pre­si­dent of the Swiss Sta­tis­ti­cal Socie­ty. The cour­se is desi­gned to be soft­ware-inde­pen­dent and cross-indus­try. Par­ti­ci­pan­ts will recei­ve a prin­ted manu­al in Eng­lish with all cour­se slides.

Pre­re­qui­si­tes:
The cour­se requi­res a basic know­ledge of sta­tis­tics and expe­ri­ence with mul­ti­ple line­ar regres­si­on.