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May 2020
Predictive Analytics in a Big Data Context
What is the right format for my data science project? Will a local application suffice or does it have to be a big data solution? In order to be able to find answers to these questions on their own and to avoid costly mistakes at the very beginning of the project, participants in this training course learn about the advantages and disadvantages of the different technologies in a practical way. For this purpose, the scenarios from the basics and deployment…
Find out more »Text and Web Mining
The Text Analytics and Web Mining course lasts for two days and gives an introduction to the analysis of unstructured data like text documents or web pages. The course covers the entire processing chain from the automated collection of unstructured data and storage in optimized databases to the automatic analysis of the content and presentation of the results.
Find out more »June 2020
Grundlagen und fortgeschrittene Themen für Analysten (Basics 1 & 2)
This training is offered by an external company and taught by us. It consists of both Basics 1 and Basics 2 and will most likely be in German.
Find out more »July 2020
Basics 1 – Predictive Models and their Validation
In this practical training course, the basics necessary for carrying out a data science project will be taught. This includes a general overview of the entire subject area to allow participants to keep track. Participants will learn to classify a specific problem into basic classes of problems, prepare data accordingly, create, optimise and evaluate predictive models using machine learning and apply the models. In the training course, the free version of RapidMiner Studio will be used to give each participant…
Find out more »Basics 2 – Profiling and Complex Models
Building on the training course Basics 1, this practical training course teaches how to create data profiles for the training of predictive models and how to choose meaningful quality criteria depending on the problem at hand. The predictive models that are already familiar are then combined with technologies for feature selection in order to identify important influence factors in the profiles. Participants will learn to create and optimize complex analytical predictive models. In the training course, the free version of RapidMiner…
Find out more »Deployment – Predictive Analytics in Live Use
This course demonstrates what needs to be considered in the live use of data science and how the knowledge acquired in the basic training courses can be successfully integrated into existing business processes. All skills necessary for integrating data science technologies into fully automated business processes are taught and practised on RapidMiner Server. In order to monitor the fully automated deployment or, in a semi-automated scenario, to be able to provide an end user interface, participants learn about the possibilities…
Find out more »Time Series Analysis
In this training course, participants learn what needs to be considered in the analysis and harnessing of time series based data. Almost all data sources have a time reference which needs to be taken into account in the analysis. This time reference can provide valuable information but can also give a false indication of correlations and must therefore always be kept in mind when creating predictive models. The course provides participants with the technologies to deal with time-referenced data and…
Find out more »September 2020
Basics 1 – Predictive Models and their Validation
In this practical training course, the basics necessary for carrying out a data science project will be taught. This includes a general overview of the entire subject area to allow participants to keep track. Participants will learn to classify a specific problem into basic classes of problems, prepare data accordingly, create, optimise and evaluate predictive models using machine learning and apply the models. In the training course, the free version of RapidMiner Studio will be used to give each participant…
Find out more »Basics 2 – Profiling and Complex Models
Building on the training course Basics 1, this practical training course teaches how to create data profiles for the training of predictive models and how to choose meaningful quality criteria depending on the problem at hand. The predictive models that are already familiar are then combined with technologies for feature selection in order to identify important influence factors in the profiles. Participants will learn to create and optimize complex analytical predictive models. In the training course, the free version of RapidMiner…
Find out more »Predictive Analytics in a Big Data Context
What is the right format for my data science project? Will a local application suffice or does it have to be a big data solution? In order to be able to find answers to these questions on their own and to avoid costly mistakes at the very beginning of the project, participants in this training course learn about the advantages and disadvantages of the different technologies in a practical way. For this purpose, the scenarios from the basics and deployment…
Find out more »