Professional training – Data Science and Classical Machine Learning

Classification Methods

Hands-on Classification Methods course: you work on Logistic regression, Decision trees and Random forest and take away knowledge you can apply right away.

Duration
6 hours, one session
Level
Intermediate
Format
Online or on-site
Language
German or English
Certificate
Certificate of attendance

Overview

In “Classification Methods” we combine fundamentals and practice: you get to know Logistic regression, Decision trees and Random forest, apply what you learn in exercises and receive templates and checklists for your own use.

Data science creates value when data is prepared cleanly, models are chosen sensibly and results are communicated transparently. Our courses cover the whole path: from data preparation and exploratory analysis through classical methods such as classification, regression and clustering to model evaluation and MLOps basics. Classification Methods is a one-day workshop with compact input and exercises on realistic datasets. You work with Python, SQL and established platforms and take home notebooks, templates and checklists for your own projects.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Focus areas of the course

Logistic regressionDecision treesRandom forestGradient boostingClass imbalanceThresholds and costs

Who is this course for?

  • Data analysts and aspiring data scientists
  • Developers and data engineers
  • Domain experts with data responsibility
  • Team leads and project owners for data projects

What you will be able to do

  • You know the key topics of Classification Methods – Logistic regression, Decision trees, Random forest, Gradient boosting, Class imbalance and Thresholds and costs – and can apply them in practice.
  • You apply Classification Methods confidently in your own data projects.
  • You choose methods that fit the question and the data.
  • You evaluate models robustly and recognize typical sources of error.
  • You communicate results clearly to business units and decision-makers.

Course outline

Module 1 Getting started and problem framing for Classification Methods
  • Where Classification Methods fits in the data science process
  • Translating a business question into a data task
  • Working environment, notebooks and data access
  • Sample project and target variable
Module 2 Focus areas: Classification Methods
  • Logistic regression
  • Decision trees
  • Random forest
  • Gradient boosting
  • Class imbalance
  • Thresholds and costs
Module 3 Methods and tools in Classification Methods
  • Key methods and their assumptions
  • Implementation with suitable libraries or platforms
  • Spotting data quality issues, leakage and bias
  • Typical mistakes and countermeasures
Module 4 Hands-on exercises for Classification Methods
  • Guided exercises with realistic data
  • Training, comparing and evaluating models
  • Visualizing and interpreting results
  • Group review of the approaches
Module 5 Moving into production
  • Reproducibility and versioning
  • Documenting and communicating results
  • Monitoring and maintenance overview
  • Next steps for your data project
Module 6 Transfer to your daily work
  • Exercise based on a realistic case
  • Typical mistakes and how to avoid them
  • Checklist and templates to take away
  • Open questions, outlook and resources for further learning

Prerequisites

Depending on the level, basic Python or statistics knowledge is enough. Advanced courses assume solid programming and methodological skills. Basic knowledge of the topic is helpful.

Technical requirements

Your own laptop with internet access; development environment, notebooks and datasets are provided in advance.

Dates & locations

For every location we suggest a date roughly four weeks ahead. Your preferred date is always possible.

  • Munich Thu, 10 December 2026 6 hrs Request
  • Salzburg Wed, 16 December 2026 6 hrs Request
  • Berlin Tue, 15 December 2026 6 hrs Request
  • Augsburg Fri, 11 December 2026 6 hrs Request
  • Hanover Thu, 10 December 2026 6 hrs Request
  • Bremen Wed, 16 December 2026 6 hrs Request
  • Stuttgart Fri, 11 December 2026 6 hrs Request
  • Kaiserslautern Thu, 10 December 2026 6 hrs Request
  • Online Wed, 16 December 2026 6 hrs Request

Frequently asked questions

What prior knowledge do I need for Classification Methods?

That depends on the course level. Beginner courses need basic Python or statistics knowledge, advanced courses solid programming practice. We clarify the right starting point with you in advance and adjust pace and examples to the level of your group.

Can we work with our own data in Classification Methods?

Yes, gladly for in-house sessions, provided the data is anonymized or approved. We clarify access and data protection requirements beforehand. Alternatively we work with realistic sample datasets that cover all exercises.

How much does “Classification Methods” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 6 hours of live training with a trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. For every location we suggest a date roughly four weeks ahead. In the request form you can always describe your preferred date or period in free text – we check availability and get back to you within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run the course exclusively for your team.

Can the course be tailored to our team?

Yes. Tailor-made courses cost approx. €4,500 more, depending on the topic and by arrangement. We agree content, examples and exercises with you in advance.

More courses in Data Science and Classical Machine Learning

All 26 courses in Data Science and Classical Machine Learning