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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  • rstudio::global 2021 - Review of 24 hours around Data Science and R

    Reading time: approx. 4 min. 24 hours, +17,000 participants, 50 speakers: That was rstudio::global 2021. We take a look back and give you an insight into a selection of talks we think are most exciting.


  • Schriftzug Data Science Trends über einer Straße mit Sonnenuntergang

    Data Science Trends 2021

    Technologies are constantly evolving and getting better with time. What topics will your company be facing in 2021? We have identified 5 top topics for you.


  • Data Science Framework - YUNA elements now available for download!

    reading time: approx. 1min. Our new data science application is now available as a direct download! Test it now 30 days free of charge – no strings attached!


  • Reasons why data science projects are not always successful - Part 1

    Reading time: approx. 3min. Why does it happen that data science projects do not bring the success that one expects after long planning? We will shed light on some factors!


  • RStudio Team Admin Training

    RStudio Team Admin Training - Remotely

    Reading time: approx. 2 min. We train you in RStudio Server Pro, RStudio Connect and RStudio Package Manager – remotely. Register now for our online training in June 2020.


  • Online R trainings: Learning data science - live and interactive

    Reading time: approx. 2 min. In our two most popular courses "Introduction to R" and "Machine learning with R" we provide you with the knowledge you need for the productive use of R - remotely. Register now.


  • Shiny: Performance Tuning with future/promises

    Reading time: approx. 4 min.In our practical blog post we show with the help of an example app how future/promises apps can be kept reactive despite complex tasks. Try it yourself with the code we provide and put theory into practice!


  • Webinar-Data-Science-mit-R

    Online R trainings: Learning data science - live and interactive

    Reading time: approx. 2 min. Online concerts, online fitness courses, online yoga - and we also bring our popular R trainings on the internet! Use our online trainings and become an R expert!


  • 10 Reasons why YUNA

    Reading time approx. 3 min. What are the benefits of YUNA and how can it help you to get started with data analysis? Find out here!


  • Shiny: Performance tuning with future & promises - Theory

    Reading time: approx. 3 min. As analysis apps become more and more complex, the demands on developers to maintain the status quo for performance and availability are also increasing. We take a look at the architecture of the Shiny apps and the packages future & promises!


  • Dear Data Scientists – ease your job!

    Reading time: approx. 3 min. You have on the most exciting jobs ever! In the same time you have to meet tremendously high expectations – learn how a data science platform can ease your job!


  • How Shiny and YUNA complement each other

    Reading time: approx. 4 min. Shiny® apps have many advantages. In combination with YUNA the advantages are even more enhanced. Learn more!


  • Shiny: Load testing and horizontal scaling

    Reading time: approx. 4 min. Learn how detailed load testing can be an important tool to identify and eliminate potential vulnerabilities early enough before the application goes live.


  • Schön‘ juten Tach & Moin Moin: R trainings in Berlin & Hamburg

    Reading time: approx. 2 min Data science courses in Berlin and Hamburg - In April and October 2020 we will bring our popular R trainings to major German cities. Become a data science expert with R!


  • Data Science Anbieter Unternehmenserfolg

    How to build data science 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!


  • Data Science Anbieter Unternehmenserfolg

    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.


  • Data Science Anbieter Unternehmenserfolg

    How to build data science 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?


  • Data Science Anbieter Unternehmenserfolg

    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?


  • Data Science Anbieter Unternehmenserfolg

    How to build data science platforms - Part 1: UI & Teams

    Reading time: approx. 2min. Which factors must be met by modern data science platforms?


  • Version management - The uncomplicated work on a common 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!