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Identify patterns and analyze data meaningfully

Data Mining Training

The Data Mining training follows the CRISP-DM process from data preparation to clustering, classification and prediction. Participants learn how to assess models and translate analytical results into practical decisions

Data Mining Training – Stemann Consulting
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Training overview

Discover patterns in data and use analytical models responsibly

The Data Mining training covers the full path from a well-defined analytical question and data preparation to exploration, segmentation, classification and prediction. Participants learn the CRISP-DM process, assess model quality and translate results into understandable recommendations

Prepare and validate data
Identify patterns in data
Structure analytical questions
Communicate findings clearly
Curriculum

Training content and agenda

  1. 01

    Data understanding and objective

  2. 02

    Preparation and exploration

  3. 03

    Patterns, segmentation and models

  4. 04

    Interpretation and business value

Target group

Who is this training for?

  • Data analysts and data engineers
  • Business functions with data-intensive questions
  • Quality and process owners
  • Project leaders for analytics and machine learning applications
Training calendar

Upcoming public dates

19Oct

Online

19.10.2026 – 21.10.2026

Online | 16 seats
Registration openRequest a place
22Feb

Berlin

22.02.2027 – 24.02.2027

On-site | 16 seats
Registration openRequest a place
23Aug

Stuttgart

23.08.2027 – 25.08.2027

On-site | 16 seats
Registration openRequest a place
13Dec

Online

13.12.2027 – 15.12.2027

Online | 16 seats
Registration openRequest a place
Quality & certificate

Quality assurance & certificate

Models are evaluated for explanatory power, stability and business usability. In addition to analysis, the training covers data quality, transparent model limitations and responsible communication of predictions

FAQ

Frequently asked questions

Is data mining only for data scientists?

No. The training is also useful for business functions that want to understand and evaluate data projects more confidently