The data science blog by eoda
The data analysis blog from the data science specialist: We tell you stories about the data science languages R and Python, data science in a business context and much more.
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How to build data science 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.
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?
How to Build Data Science 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 data science platforms?
How to build analytics platforms - Part 1: UI & Teams
Reading time: approx. 2min. Which factors must be met by modern data science platforms?
Version control – The uncomplicated work on a joint project
Reading time: approx. 4min. Version management is a central tool for project management. Learn more about how Git can help you work more efficiently on joint projects!
Package management: Using repositories in production systems
Reading time: approx. 4min. A good package management in production systems and a fully functional infrastructure are the basis for a complication-free development environment. Learn how to work properly with repositories here!
Ansible: Infrastructure as code (IaC)
Reading time: approx. 3min By using Ansible, IT infrastructures can be automated so that manual intervention is no longer necessary. In our article we show you selected examples of how an automated infrastructure configuration can be implemented.
Kubernetes: Horizontal scaling of data science applications in the cloud
Reading time: approx. 2 min Due to the constant growth of data and the ever more complex requirements of an IT infrastructure, there are always new challenges for data scientists and data engineers. One possible solution is to combine the RStudio Job Launcher with a Kubernetes cluster: Read our blog article to find out about the advantages!
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!