The training course is aimed at executives coming into contact with data science for the first time or who have some initial experience and want to expand their basic knowledge to make strategic decisions in terms of data science. The training course covers the basic concepts of data science as well as the topics of IT infrastructure and communication. Knowledge will also be shared regarding the social aspects in data science projects and their influence on project success.
In this training course, the end user is familiarized with the mindset behind machine learning and a common basic vocabulary is established that is necessary for successful communication between experts and data scientists.
Building on this basis of communication, end users learn to assess whether their challenges can be efficiently solved with machine learning. They also learn to formulate use cases in such a way that the data science department or external experts can quickly understand the problems and make the end user’s daily work easier by providing solutions. The communication between end users and data scientists enables data science solutions to be successfully integrated into business processes.
In this practical training course, the basics necessary for carrying out a data science project are taught. This includes a general overview of the entire subject area to allow participants to keep track.
Participants learn to classify a specific problem into basic classes of problems, prepare data accordingly, create, optimize 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 the opportunity to try things out without any programming knowledge.
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 Studio will be used to give each participant the opportunity to try things out without any programming knowledge.
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 of creating a web app with the standard tools of RapidMiner Server.
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 answer these questions on their own and avoid costly mistakes at the very beginning of the project, participants will learn about the advantages and disadvantages of the different technologies in a practical way in this training course. For this purpose, the scenarios from the basics and deployment training courses are continued with the participants and scaled to big data. The course also covers methods necessary for the processing of large quantities of data, with a focus on batch-oriented processing.
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 use it for different analysis purposes. Participants are shown how time series forecasts can be created, i.e. future values predicted. Furthermore, the classification of entire time series is looked at as well as the segmentation of time series into sections.
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.
