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    • RIDE Containerized Data Science IDE server For Enterprise
      RIDE Containerized Data Science IDE server For Enterprise Ali Marami, Data Science Advisor at R-Brain Recorded: Dec 14 2017 4:00 pm UTC 45 mins
    • RIDE is an all-in-one, multi-user, multi-tenant, secure and scalable platform for developing and sharing Data Science and Analytics, Machine Learning (ML) and Artificial Intelligence (AI) solutions in R, Python and SQL.

      RIDE supports developing in notebooks, editor, RMarkdown, shiny app, Bokeh and other frameworks. Supported by R-Brain’s optimized kernels, R and Python 3 have full language support, IntelliSense, debugger and data view. Autocomplete and content assistant are available for SQL and Python 2 kernels. Spark (standalone) and Tesnsorflow images are also provided.

      Using Docker in managing workspaces, this platform provides an enhanced secure and stable development environment for users with a powerful admin control for controlling resources and level of access including memory usage, CPU usage, and Idle time.

      The latest stable version of IDE is always available for all users without any need of upgrading or additional DevOps work. R-Brain also delivers customized development environment for organizations who are able to set up their own Docker registry to use their customized images.

      The RIDE Platform is a turnkey solution that increases efficiency in your data science projects by enabling data science teams to work collaboratively without a need to switch between tools. Explore and visualize data, share analyses, all in one IDE with root access, connection to git repositories and databases.

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    • Moving Beyond Business Intelligence - Using R to Prepare Data for Analytics
      Moving Beyond Business Intelligence - Using R to Prepare Data for Analytics Lillian Pierson, Author of Data Science for Dummies & Chief Data Scientist, Data-Mania Recorded: Oct 8 2015 3:00 pm UTC 43 mins
    • Traditional business intelligence professionals often find it challenging to identify how this new “analytics” craze is any different from what they’ve been doing all along. Other data professionals don’t think much about the difference between BI and data science, because they’re too busy getting amazing results from data science applications like Watson Analytics and Tableau.

      It’s easy to become satisfied with “business as usual”, but what happens when the game changes and more powerful methods become available to derive more value from the same old data? If you want to cut out all the process bulk and really laser target the data and data operations you need… if you want extremely fast results… and if you want to generate data insights that’ll keep you the A-player at your workplace, then you’ll be happy to know that there’s a free (and relatively) easy way to achieve these results by using R programming language.

      In this webinar, you’ll get an introduction to R and the core benefits it offers you. You’ll see the lifecycle of an analytics project, noting how analytics is definitely distinct from traditional business intelligence. And lastly, you’ll get a live demo and tutorial on how you can begin using R to prepare your data for analytics.

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    • Binomial and Multinomial Logistic Regressions in R
      Binomial and Multinomial Logistic Regressions in R Ali Marami Chief Data Scientist Recorded: Jun 29 2017 5:00 pm UTC 49 mins
    • 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.

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    • Statistical Computing-R & Visual Analytics: Data Science at the Speed of Thought
      Statistical Computing-R & Visual Analytics: Data Science at the Speed of Thought Bora Beran, PhD, Program Manager, Tableau Recorded: May 28 2015 4:00 pm UTC 45 mins
    • Do you spend days on data science projects, only to struggle building them into presentations that management can understand? Do you spend more time exploring and understanding your data, before even beginning to write one line of a model? Do you have a hard time working through other departments to get to your data in the first place?

      Tableau is a visual reporting application that connects directly to R. It’s designed for you, the domain expert who understands the data. Its drag-and-drop interface allows you effortlessly connect to libraries and packages, import saved models, or write new ones directly into calculations, visualizing them in seconds.

      This webinar will show you how to:
      - Effortlessly connect your R scripts to a wide variety of data files and databases
      - Build interactive slideshows and presentations of your data in minutes
      - Use dashboards as a front end for R code, allowing viewers to intuitively interact with R models

      Join us to see how you can use drag and drop data visualization alongside R to speed up your data science projects and get them in front of more eyes, leading to smarter, data-driven business decisions.

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    • More Than Just a Query: Using R/Python to Get More Out of SQL Outputs
      More Than Just a Query: Using R/Python to Get More Out of SQL Outputs Neha Kumar, Customer Solutions Engineer at Periscope Data Upcoming: Oct 17 2018 6:00 pm UTC 60 mins
    • As companies become more data driven, it’s not enough to deliver the same standard SQL queries. Instead, data teams today are transforming SQL results into models to deliver more value to their businesses. But making that jump is tough, it’s hard to determine where to get started in the complex field of modeling and regression analysis.

      The Periscope Data community has been buzzing with requests to demystify Python and R modeling so data professionals can boost their skill set. Get started by creating a linear regression model with Neha Kumar, a customer solutions engineer at Periscope Data, and start bringing new analytics value to your business immediately.

      Join Neha on Wednesday, October 17th, 2018 at 11 a.m. PDT as she walks through:
      - What a linear regression is and when to use it
      - Setting up the framework for a linear regression
      - Step-by-step creation of a linear regression model in Python and R
      - Contextualizing a model for other business teams

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