ECFR visualizes survey data with Tableau
Cross-national surveys are prepared in a sophisticated way
Data alone is only a collection of measurements and counts. Only their analysis creates meaningful findings.
In this fast-growing field, new methods for improving knowledge acquisition are being developed daily. If these methods are applied, a better understanding of one's own company often emerges. You profit from this in the long term.
Companies often have a rich data situation without knowing what added value can be achieved with it. This is where we as a team start with our "Vision Workshop" model.
We offer you a free workshop via video conference to discuss your data and its possibilities - completely free of charge.
Viewing relevant data and working out a
Target image and highlighting added value for the company
In this step we take care of the data preparation
the data sources relevant and underlying the project
Analysis of results using the latest methods from the fields of "Supervised and Unsupervised Learning," "Deep Learning," or Neural Networks.
Visual processing of the results and Productization in existing or new infrastructure
Sebastian Lorenz is head of the Data Science division at M2. Sebastian is an expert in the field of "Supervized and Unsupervized Learning" with specialized knowledge in the area of "Medical Data Science".
In his 10 years of professional experience he has been working in different industries such as aviation, service centers or construction and has acquired a wide range of knowledge on different management levels. As a project manager, he has successfully managed projects such as the prediction of inbound contacts in service centers, complex transaction analyses in the area of crypto currencies or the creation of simulation tools in the area of resource planning.
Besides Data Science, Sebastian Lorenz also deals with upstream and downstream processes such as data preparation and storage as well as visualization of results with Tableau.
Part Default Probability & Life Expectancy
A service provider for the automotive industry would like to use machine learning algorithms for the prediction and inspection of quality problems on the one hand, and on the other hand also use the possibilities of an interactive Data analysis. The interactive data analysis is intended to enable end users from the various departments to quickly and precisely analyze the data and verify the results of the ML algorithms.
In this project a machine learning application in Python was developed, which predicts the life expectancy of individual parts based on more than 100 variables. An interactive Tableau dashboard solution with over 20 individual dashboards, including complex user navigation, was developed to make the data visually consumable.