Learn how to use a growing library of R functions and advanced techniques for deeper statistical and predictive analysis.
Join Jared Lander, the man behind "R for Everyone: Advanced Analytics and Graphics", and Evan Castle from Sisense as they illustrate how R can be used to solve real life business problems.
In this webinar, you'll learn how to:
Predict critical business outcomes
Easily build statistical models in Sisense
Create interactive dashboards using practical statistics
Watch this on-demand webinar to learn how to get started with large-scale distributed data science.
Do your data science teams want to use R with Spark to analyze large data sets? How do you provide the flexibility, scalability, and elasticity that they need – from prototyping to production?
In this webinar, we discussed how to:
-Evaluate compute choices for running R with Spark (e.g., SparkR or RStudio Server with sparklyr)
-Provide access to data from different sources (e.g., Amazon S3, HDFS) to run with R and Spark
-Create on-demand environments using Docker containers, either on-premises or in the cloud
-Improve agility and flexibility while ensuring enterprise-grade security, monitoring, and scalability
Find out how to deliver a scalable and elastic platform for data science with Spark and R.
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.
With ever increasing adoption by vendors and enterprises, Spark is fast becoming the de facto big data platform.
As a general purpose data processing engine, Spark can be used in both R and Python programmes.
In this webinar, we'll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
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.
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
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.
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.
George Kinder's signature EVOKE(R) model-- a methodology that takes clients through a five-stage interview process leading to a comprehensive and highly tailored plan of action. Exploration - Vision - Obstacles - Knowledge - Execution
The interview process can be accomplished in as little as an hour or, for sophisticated clients with more complex needs, in a series of longer meetings. The method has been shown to deliver dramatic positive results consistently.
- Build a trusting relationship from the first minutes of the first meeting
- Help clients uncover & reveal inspirational visions for their lives that bring vitality & vigor to their finances
- Address key obstacles to attainment of long-desired goals
- Integrate the life vision with practical financial strategies
- Overcome resistance to plan execution
- Build deeper longer-lasting relationships with clients
- Enjoy a more stable flow of income
- Attract a larger share of clients' assets
- Receive stronger, warmer referrals
- Create a richer, more satisfying life for yourself