Data mining in practice

Data mining in practice

Cour­se objec­ti­ve:
The aim of this methods cour­se is to learn the prac­ti­cal appli­ca­ti­on of data mining con­cepts and methods. The cour­se is desi­gned to be soft­ware-inde­pen­dent and cross-indus­try and is 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 data mining.

Cour­se con­tent:
The cour­se is the prac­ti­cal con­ti­nua­tion of the methods cour­se “Methods for Sta­tis­ti­cal Data Mining” (MDM). Using con­cre­te examp­les, par­ti­ci­pan­ts will put the theo­re­ti­cal know­ledge they have acqui­red into prac­ti­ce with modern data mining soft­ware.

The topics:

• Explo­ra­ti­on / unsu­per­vi­sed lear­ning
  • Which data sets show simi­la­ri­ties?
  • Which data sets form natu­ral groups and struc­tures?
• Clas­si­fi­ca­ti­on / super­vi­sed lear­ning
  • Which influen­cing varia­bles deter­mi­ne the affi­lia­ti­on?
  • What con­clu­si­ons can be drawn from this?
  • Which of the gene­ra­ted pre­dic­tion models should be pre­fer­red?

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. Par­ti­ci­pan­ts will recei­ve a prin­ted manu­al in Eng­lish with all cour­se slides. On request, a test licen­se for Sta­tis­ti­ca Data Miner will be made available free of char­ge for 30 days so that par­ti­ci­pan­ts can try out what they have lear­ned in their own com­pa­ny.

Pre­re­qui­si­tes:
This cour­se assu­mes know­ledge of the methods cour­se “Methods for Sta­tis­ti­cal Data Mining” (MDM). No know­ledge of the Sta­tis­ti­ca Data Miner soft­ware is requi­red, as only its self-expl­ana­to­ry, intui­ti­ve pro­ject inter­face is used.