# learnAnalytics-CreatingSolutionswiththeTeamDataScienceProcess **Repository Path**: mirrors_Azure/learnAnalytics-CreatingSolutionswiththeTeamDataScienceProcess ## Basic Information - **Project Name**: learnAnalytics-CreatingSolutionswiththeTeamDataScienceProcess - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-08 - **Last Updated**: 2026-03-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README Introduction ============================================================ In this hands-on workshop, you’ll cover a series of modules that guide you in understanding how to implement an analytics solution using the Team Data Science Process. You’ll learn how to work through a real-world scenario using Microsoft Azure Machine Learning Services along with other Microsoft technologies. You'll learn how to modify the solution we create in the class solution for implementations in your own scenarios. This course is designed to take approximately one day. Getting Started =============== You’ll need a laptop that you can install software on, a Microsoft Azure account, experience with Machine Learning and Programming in Python, along with several other pre-requisites. See the “README.md” file in the “Students” folder for a full list prior to taking this course. Important – These pre-requisites are essential to have \*before\* you attempt this class. Many concepts will not be covered that are essential to have as a background for this material. If these pre-requisites are new to you, there is a complete Learning Path in the “Instructions” folder called “Learning Path - Creating Solutions with the Team Data Science Process.md” that you can use to learn these technologies and processes. You should be able to complete all tasks in that Learning Path prior to attending this course. Course Modules -------------- ### 1 – Introduction to the Team Data Science Process (TDSP) In module one, we’ll cover an overview of the TDSP, with an explanation of each phase. You’ll also set up your environment for the rest of the course. By the end of the module, students should be familiar with the Team Data Science Process, the Microsoft Business Analytics and AI Platform and Azure DevOps for Data Science. *NOTE: Much of the setup must be accomplished prior to class. See the “README.md” file in the “Students” folder for these requirements.* ### 2 – Business Understanding At the end of this module, students should be able to determine questions from business requirements, locate and document data sources for Advanced Analytics, and use patterns to create solution frameworks. During the module, a business case is presented, and the instructor takes the students through the process of breaking a statement down into key words used to determine the question to be answered with data storage technologies and data processing technologies, ultimately using a decision matrix to create a solution workflow. ### 3 - Data Acquisition and Understanding Upon completion of this module, students should have hands-on experience and understanding of how to ingest data into the solution, explore data using the Azure Machine Learning Services (AMLS) Workbench tool, and create a mechanism to orchestrate and manage data flows through a solution. ### 4 – Modeling This module is focused on Machine Learning. In this module, students will learn about Machine Learning options and create a Machine Learning solution in their AMLS environment. Students will be able to create, save, and run Machine Learning models using the AMLS Workbench tool. ### 5 – Deployment This module covers the deployment of an AMLS model. Students will learn to track and monitor models and their runs using the AMLS Workbench tool. The students will learn how to deploy the results of the model to be used in client and downstream applications. ### 6 - Customer Acceptance In this module, several important post-deployment activities are discussed in detail including: customer handoff and acceptance, altering and maintaining a solution, and monitoring and reporting on the solution. Build and Test ============== This project contains three folders: ### Instructions Materials needed to teach or prepare for this course are stored here. ### Instructor All source training materials, PowerPoint files, and other teaching resources are located here. ### Students Pre-Requisites, Student Workbooks, Resource files, Data Sources and other student assets are located here. Contribute ========== You may fork or download this course for your own use. Please notify the training team of any errors or omissions using the “issues” feature on the course’s github location. License ======= Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact with any additional questions or comments.