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This video is Module 10: ThingWorx Foundation & Analytics Integration of the ThingWorx Analytics Training videos. It gives a brief review of core ThingWorx Platform functionality, and how the Analytics server works on top of the platform. It also describes the process of creating a simple application, complete with a mashup to display the information from a predictive model.
This video concludes Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It gives an overview of the "Statistical Process Control (SPC) Accelerator"
This video continues Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It begins with a ThingWatcher exercise, and concludes by describing Statistical Process Control (SPC). The "SPC Accelerator" will be covered in Module 9 Part 3.
This video begins Module 9: Anomaly Detection of the ThingWorx Analytics Training videos. It describes how Thingwatcher can be set up to monitor values streaming from connected assets, and send an alert if its behavior deviates from its 'normal' behavior.
This video continues Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It continues to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning. It also describes the concept of virtual sensors. It finishes by describing the time series dataset that will be used in the following modules.
This video begins Module 8: Time Series Modeling of the ThingWorx Analytics Training videos. It describes the differences between time series and cross-sectional datasets. It begins to show how ThingWorx Analytics automatically transforms time series datasets into ones that are ready for machine learning.
This video concludes Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how ThingWorx Analytics automatically evaluates a range of values for chosen fields to produce prescriptive scores.
This video begins Module 7: Predictive & Prescriptive Scoring of the ThingWorx Analytics Training videos. It describes how a trained machine learning model takes inputs and makes predictions of different kinds, depending on the use case. It shows how scoring works in production, taking inputs from various sources and producing a score to help users make informed decisions. It also covers the concept of field importance in an individual score.
This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It covers some modeling techniques to help build better predictive models. It discusses the dangers of models that overfit data, and how to avoid overfitting.
This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It then begins to describe some of the performance metrics used to evaluate predictive models.
This video continues Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It describes the remaining machine learning algorithms used by ThingWorx Analytics to build predictive models that weren't covered in Part 1. In addition, this video describes the different kinds of ensembles you can build that utilize multiple algorithms.
This video begins Module 6: Predictive Models & Model Validation of the ThingWorx Analytics Training videos. It gives examples of different types of goal variables. It also discusses data considerations in predictive modeling, It begins describing the machine learning algorithms used by ThingWorx Analytics to build predictive models.
This video concludes Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.
This video begins Module 5: Descriptive Analytics of the ThingWorx Analytics Training videos. It covers signals, profiles, and clusters, and how these forms of descriptive analytics provide crucial insight into your data.
This video concludes Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It covers Descriptive Services and Derived Properties, and how they can be leveraged to create helpful alerts and make data transformation easier.
This video begins Module 4: Data Transformation & Feature Engineering of the ThingWorx Analytics Training videos. It describes what data transformation is, and how feature engineering can improve machine learning models. You will learn about independent and dependent variables in your data, and how an "analytics ready view" looks for use with ThingWorx Analytics.
This video concludes Module 3: Data Profiling of the ThingWorx Analytics Training videos. It shows you a few examples of questions that should be asked of a subject-matter expert (SME) to better understand the information contained in a dataset. Using answers to these questions, you will use a tool such as Microsoft Excel to modify a given dataset, and prepare it for future exercises in this course.
This video continues Module 3: Data Profiling of the ThingWorx Analytics Training videos. It describes metadata, and how it is used to ensure that your data is handled appropriately when running Signals, Profiles, Training, Scoring, and other jobs inside ThingWorx Analytics.
This video begins Module 3: Data Profiling of the ThingWorx Analytics Training videos. It describes the process of examining your data to make sure that it is suitable for the use case you would like to explore.
Learning Paths combine guides into one experience to teach related skills efficiently from start to finish. Begin your learning journey to reduce time to proficiency to get up and running with ThingWorx quickly. Featured Learning Utilizing ThingWorx to Secure Your Aerospace and Defense Systems Connect and Monitor Industrial Plant Equipment Vehicle Predictive Pre-Failure Detection with ThingWorx Platform Industry Solutions Complex and Automatic Food and Beverage Systems Connect and Configure Industrial Devices and Systems Medical Device Service Monitor Factory Supplies and Consumables Using an Allen-Bradley PLC with ThingWorx Learn ThingWorx Getting Started on the ThingWorx Platform Design and Implement Data Models to Enable Predictive Analytics Customize UI and Display Options to Deploy Applications Azure MXChip Development Kit
Getting Started on the ThingWorx Platform Learning Path Learn hands-on how ThingWorx simplifies the end-to-end process of implementing IoT solutions. NOTE: Complete the following guides in sequential order. The estimated time to complete this learning path is 210 minutes. Get Started with ThingWorx for IoT Part 1 Part 2 Part 3 Part 4 Part 5 Data Model Introduction Configure Permissions Part 1 Part 2 Build a Predictive Analytics Model Part 1 Part 2
