Business Insight

TIMi training

There are 2 different courses:

For the simple user

This course is open to any profile: Business, IT, dataminer.
Duration: 2 to 3 days

TIMi is really easy to use for anybody. For a day-to-day usage, the usage of TIMi is quite straight forward. The three videos available on this page covers most of the standard "basic" usage of TIMi.

However, a short course covering the different aspects of the modelization process might still be good idea to take:

  • What kind of problem can you solve with TIMi?

    Cross-selling models, propensity models, fraud models, risk models, segmentation models.
    The predictive models for "Strategic & Tactic decisions" are more complex and will be explained inside the "expert user course".
  • What methodological steps do I have to follow when working with TIMi ?

    Do I still need to check if the distributions of my variables are normal before starting the modelization?
  • How to avoid the traps linked to the time periods?

    How to define properly a target group, taking into account the time dimension?
  • This topic will be discussed very briefly and on request only: What are the algorithms in TIMi?

    What are the limitations of these algorithms? How to avoid these limitations? How does these algorithms relates to other algorithms available in other datamining packages? What are the theoretical mathematical properties of these algorithms that ensure that TIMi consistently delivers better models than all the other statistical softwares (like SAS, KXEN, ART)?
  • How to get the most out of the reports generated with TIMi ?

    How to give a business sense to the charts and tables inside the TIMi reports? (with a special session on models for optimal allocation of resources.)
  • What are the most important parameters of TIMi ?

    How do these parameters relate to the general theory of modeling? How to fine tune these parameters?
  • What to do when you have no or nearly no lift?

    How to boost the quality of your model?
  • How to handle different kind of Modelisation problems?

    What are the best variables to create or use inside your dataset in:
    • a Telco application?
    • a Cross-Selling Banking application?
    • a Credit-Risk Banking application?
    • a classical CRM application?
  • What are the traps and how to avoid them when...
    • ...creating continuous models?
    • ...combining multiple binary models into one N-ary model?
  • What strange behavior could appears out of the modelization process and how to avoid them?

For the expert user

This course is open to any profile that is interested in the more mathematical aspect behind TIMi : Business, IT, dataminer.
Duration: 1 to 2 days+simple user course

This course covers the following subjects:

  • Predictive models for "Strategic & Tactic decisions".
  • What are the different "advanced parameters" of TIMi ?
    How to use them to increase the accuracy of the predictive models created with TIMi ?
  • What are the different techniques available to construct models? What are the weakness of each modelization techniques? How to overcome these weakness?
  • What's overfitting? How to prevent it?
    • Pruning
    • Ridge regression
    • Backward Stepwise
    • Forward Stepwise
    • Cross-validation
  • What are the danger linked to sampling?
  • Combination of models: Boosting, Bagging, Feature Selection



Next: Stardust training

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