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Radiant, a powerful open source Shiny application for business analytics

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

Radiant key features:

• Explore: Quickly and easily summarize, visualize, and analyze your data
• Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
• Cross-platform: It runs in a browser on Windows, Mac, and Linux
• Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
• Programming: Integrate Radiant's analysis functions with your own R-code
• Context: Data and examples focus on business applications

After this webinar you will learn:

• Data manipulation and running different models
• How to run advanced analytics in a browser on any device even in your tablet or iPad.


Presenter bio:

Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
Recorded Jul 11 2017 58 mins
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Ali Marami Chief Data Scientist
Presentation preview: Radiant, a powerful open source Shiny application for business analytics

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  • Radiant, a powerful open source Shiny application for business analytics Recorded: Jul 11 2017 58 mins
    Ali Marami Chief Data Scientist
    Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results.

    The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. In this webinar, we review the tools available in Radiant and explain how easily you can use this tool without any setup or installation on your system.

    Radiant key features:

    • Explore: Quickly and easily summarize, visualize, and analyze your data
    • Run different models: OLS, GLM, Neural Networks, Naïve Bayes and CART.
    • Cross-platform: It runs in a browser on Windows, Mac, and Linux
    • Reproducible: Recreate results and share work with others as a state-file or an Rmarkdown report
    • Programming: Integrate Radiant's analysis functions with your own R-code
    • Context: Data and examples focus on business applications

    After this webinar you will learn:

    • Data manipulation and running different models
    • How to run advanced analytics in a browser on any device even in your tablet or iPad.


    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Binomial and Multinomial Logistic Regressions in R Recorded: Jun 29 2017 49 mins
    Ali Marami Chief Data Scientist
    Logistic regressions are the basic of machine learning. In this webinar, we discuss binomial and multinomial logistic regressions, how we implement them in R and test their performance. We will also review few examples of their usage in industry. In addition, you will learn how to use R-Brain advanced IDE when implementing the model.

    - Logistic regressions fundamentals and how to interpret estimates
    - Binomial and Multinomial logistic regressions
    - Implement logistic regressions in R
    - Performance measurement in logistic regressions
    - Generating and understanding ROC curve
    - Building confusion metrics and understanding its elements
    - Examples of model application in industry
    - Learn about new advanced IDE

    Presenter bio:

    Ali has a Ph.D. in Finance from the University of Neuchatel in Switzerland and a BS in Electrical Engineering. He has extensive experience in financial modeling, quantitative modeling, and financial risk management in several US banks.
  • Jupyter is more than notebooks, JupyterLab and beyond! (Webinar) Recorded: Jun 1 2017 48 mins
    Ali Marami Chief Data Scientist
    Join us to learn about JupyterLab, the new open source computational environment for Jupyter. Increase the performance of your data science projects by working in an integrated environment for your notebooks, editor, terminal and console. We will also discuss R-Brain cloud platform and its new R Python Cloud IDE which is built on JupyterLab.
Live monthly webcasts for data science and predictive analytics
We will talk about the challenges of delivering data science solutions to business users or data science team. Either you are a data scientist working in an organization or a consultancy firm, or a client for data science solutions, efficiency and speed matters. We show you how you could be more agile and maximize efficiency in developing and delivering the work to your internal/external clients by introducing the best practices and new advanced technologies.

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  • Title: Radiant, a powerful open source Shiny application for business analytics
  • Live at: Jul 11 2017 5:00 pm
  • Presented by: Ali Marami Chief Data Scientist
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