This year, SUS5 (Bocconi Velodromo building, 18-19 June, 2019) is organized by BIDSA and BUILT of Bocconi University in collaboration with Generali and SAS. Each team may use any programming language or software which is deemed appropriate to analyze the data.
This year, one member of each team is eligible to obtain a free license for the use of analytical and machine learning solution in SAS® Viya® and a free access to elearning course: Machine Learning Using SAS® Viya®
Moreover, a series of webinars, available to all the interested participants to SUS5
Viya Platform E2E process
Platform presentation, Analytical Lifecycle, Toolset presentation: SAS Visual Analytics, SAS Studio, SAS Visual Statistics, SAS VDMML.
Data Preprocessing with SAS Model Studio Nodes & Pipeline
Data Exploration and Data Preparation in SAS Model Studio.
Focus: SAS Model Studio & Model Comparison
SAS Model Studio: introduction, Pipeline development, Model comparison.
Scoring on New Data
Model Deployment, Export score code, Using score snippets.
The toolset available for the competition is composed by 4 main modules which are part of the SAS Viya Platform
SAS® Visual Data Mining and Machine Learning
Visual Data Mining & Machine Learning is the ultimate Enterprise Analytics Platform. Thanks to its visual interactive interfaces, it accelerates the comprehension of data & phenomena so as to extract useful business insights easily and quickly. Plus, it is a multi-purpose platform (creativity is the only limit) and has an open architecture.
SAS® Visual Analytics
Explore, represent and analyze your data thanks to one simple and intuitive visual interface. This is the tool that allows to interactively and maturely explore data and quickly understand patterns and trends. This component might be the key for accelerating data comprehension prior to any subsequent modeling stage.
SAS® Visual Statistics
Statistics made simple with SAS. This is the interface capable to quickly and interactively prototype statistical models. Thanks to this interface, business users get the chance to quickly get analytical feedback from data, such as: which is the best model that fits to data? Which are the regressors to be included? Etc...
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