Cortana Intelligence Suite Foundations + SQL DW - Paris, France

By for November 23, 2016

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3-Day workshop on CIS Foundations + SQL Data Warehousing Location: Issy-les-Moulineaux, France. December 21 - December 23, 2016
# About the Course Welcome to the Cortana Intelligence Suite workshop delivered by your Microsoft Data Science team. In this workshop, you’ll cover a series of modules that guide you from understanding an analytics workload, the Cortana Intelligence Suite Process, the foundations of data transfer and storage, data source documentation, storage and processing using various tools. You’ll also learn how to work through a real-world scenario using the Cortana Intelligence Suite tools, including the Microsoft Azure Portal, PowerShell, and Visual Studio, among others. This course is designed to take approximately two days with the final day focusing on SQL Data Warehousing. You’ll also cover a series of modules that guide you from a review of the R programming environment, the Cortana Intelligence Suite Process, the Cortana Intelligence Suite Platform, to the Microsoft R platforms including: Microsoft Open R, the Microsoft R Client, Microsoft R Server, SQL Server with R Services, R in Azure ML, and HDInsight with R. Final lab is an SQL Server R Services solution, but extrapolates to any Microsoft R platform. # Prerequisites There are a few things you will need in order to properly follow the course materials: * There are a few things you need prior to coming to class: * A subscription to Microsoft Azure (this may be provided through your company or as part of your invitation – you must have this enabled prior to class – you will be using Azure throughout the course, for all labs, work and exercises) * You can use your MSDN subscription – https://azure.microsoft.com/en-us/pricing/member-offers/msdn-benefits/ * Your employer may provide Azure resources to you, but make sure you check to see if you can deploy assets and that they know you’ll be using their subscription in the class. * Optionally, you may receive instructions in your class invitation. * We’ll be using the Data Science Virtual Machine in Azure for the course. It has all of the tools you will need to work with the materials. Make sure you’re able to use the Remote Desktop Protocol (RDP) from your system to be able to work through the labs. * If you would also like to work with some of the tools locally (you still need an Azure subscription for this class), you can optionally obtain: * A laptop that you can install software on * Visual Studio installed – the Community Edition (free) is acceptable – Version 2015 preferable ( https://www.visualstudio.com/en-us/products/visual-studio-community-vs.aspx ) * Azure SDK and Command-line Tools installed ( https://azure.microsoft.com/en-us/downloads/ ) * Azure Storage Explorer ( http://go.microsoft.com/fwlink/?linkid=698844&clcid=0x409 ) * Power BI Desktop Installed (https://powerbi.microsoft.com/en-us/desktop/ )A background in data technologies, such as working with Relational and Non-Relational data processing systems * Install the Microsoft R Client: http://aka.ms/rclient/download with the R tools for Visual Studio * It’s also a good idea to have a general level of predictive and classification Statistics, and a basic understanding of Machine Learning # Modules The course is divided into the following modules: 1. Process and Platform, Environment Configuration 2. Data Discovery and Ingestion 3. Data Preparation 4. Modeling for Machine Learning and Data Mining 5. Business Validation and Model Evaluation 6. Deploying and Accessing the Solution # Concepts Covered * The Data Science Process, CIS Platform components, Tools installation and overview * Data sourcing, Feature selection techniques, Data cataloging, Data Ingestion, Data Exploration * Data selection, including Features, Dimension reduction, Data processing, Data transformation and augmentation * Algorithm selection and application, Parameter selection and adjustment * Business validation of report and results, Model testing and cross-validation * Deploying the solution using Data Destinations, Deploying the solution using API's, Deploying the Solution using Queries and Reports * Mapping requirements to CIS solution elements, what to use when in CIS * The R Interactive Environment, Data Structures, Functions, Libraries (Packages) and Code Flow * The Microsoft R ecosystem * Working with Client Options * Planning, deploying, managing, and monitoring a Microsoft R platform * Walking through a complete 6-step solution - SQL Server R Services focused # Technologies Covered * CIS and SQL DW # Skills Taught At the end of the course you will havev acquired the following skills: * Understand the CIS Process (General level), Understand CIS Components (General Level), Set up and configure the development environment * Understand how to source and vet proper data, Understand feature selection, Understand Azure Storage Options, Use various methods to ingest data into Azure Storage, Examine data stored in Azure Storage, Use various tools to explore data * Understand ADF and its constructs, Implement an ADF Pipeline referencing Data Sources and with various Activities including on-demand HDInsight Clusters, Understand the HIVE language and how it is used * Understand how to use Azure ML and how experiments are created, Understand how MRS can be used to perform Machine Learning experiments, Use ADF to schedule Azure ML Activities * Understand how to evaluate the efficacy and performance of an Azure ML experiment, Understand how to evaluate the efficacy and performance of an MSR ML experiment, Access and show data from Azure Storage, Access, and Query Azure SQL DB * Understand how to publish an Azure ML API, Understand the access methods of Azure Storage and Intelligent Processing, Understand the options to send a HIVE query to an HDI system, Use Power BI to query the results of a solution and create reports in Power BI Desktop, Power BI Service, and Power BI in Microsoft Excel * Understand when to use each component within CIS * Basic R coding * Choose, install, configure and use the proper R environment for a given solution * Connect to a Microsoft R platform from various client tools, run code locally and operationalize on server * Understand how to plan, deploy, manage, tune and monitor a Microsoft R solution * Deploy code to a Microsoft R Server, including SQL Server