Understanding data science
Blog posts, case studies and whitepapers: Learn more about data science with our expert information. How do I implement data science projects, what does the right IT infrastructure look like and how can I better understand consumer behavior? Find exactly the content you’re looking for and gain insight into best practices in data science across the enterprise.
New customer services with predictive maintenance
Development of a "health indicator" for Schenck Process Europe GmbHWould you like to know more?
Set-up of a analysis environment for REWE International AG
Implementation of a powerful IT infrastructure based on R.Would you like to know more?
Infrastructure consulting, implementation and support for fashion companies
eoda has implemented a high-performance and reliable IT infrastructure for the productive operation of R-analyses for a fashion company.Would you like to know more?
Knowledge transfer workshop: R Shiny apps – Deutsche Bahn Analytics
In a workshop, eoda enabled the DB Analytics team to make analysis results available quickly and attractively with R Shiny.Would you like to know more?
Development of a scoring algorithm for databyte®
eoda supports databyte® in identifying new customer potential.Would you like to know more?
Precise forecasting models – 50Hertz
eoda uses data mining to create reliable forecasting models to compensate for network losses.Would you like to know more?
Predictive maintenance with deep learning
We have developed a deep learning model for a machine builder to reliably predict machine failures.Would you like to know more?
Predictive Maintenance –TRUMPF Lasertechnik
eoda has helped TRUMPF Laser Technology to increase its "Industry 4.0 Maturity Level" based on data and algorithms.Would you like to know more?
Self-learning address database
With its self-learning address database addRess, eoda enables the digital account change service of fino digital.Would you like to know more?
Potentials and opportunities for new business models
An excerpt of a data blueprintWould you like to know more?
How to build analytical platforms - Part 5: Data visualization and reliable results
Reading time: approx. 2min. What must be considered when visualizing results and data? We take a look at it!Would you like to know more?
R, Python & Julia in data science: A comparison
Reading time: approx. 3min R, Python or Julia? Meanwhile there are many programming languages, but which one is suitable for your own needs is still unclear. We give you a recommendation!Would you like to know more?
How to build analytics platforms - Part 4: Database scalability and business models
Reading time: approx. 2min. This time: Connect various data sources and develop new use cases and entire business models.Would you like to know more?
Easy data access: The advantages of a unique database connection with ODBC and DBI
Reading time: approx. 2min Optimum data access is particularly important for the work of data scientists. It enables them to access the relevant data quickly and efficiently.Would you like to know more?
How to build analytics platforms - Part 3: Customizable workflows and dashboards
Reading time: approx. 2min What if the individual project steps were based on the company and individual dashboards could be reused?Would you like to know more?
How to build analytics platforms - Part 2: Intelligent user and role concept
Reading time: approx. 2min. Why are users- and role-permissions a factor not to be underestimated in analytics platforms?Would you like to know more?
How to build analytics platforms - Part 1: UI & Teams
Reading time: approx. 2min. Which factors must be met by modern analytical platforms?Would you like to know more?
Ei Gude! - Data science courses with R in Frankfurt
Data science courses in Frankfurt! In November it's time: We will bring our popular R-Trainings to Southern Hesse.Would you like to know more?