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3 Day Predictive Analytics w/ Real Time Data

3 Day Predictive Analytics w/ Real Time Data

Overview

Terrapinn Training
49 W 32nd St
New York, NY 10001
Register for Course
Monday, August 26, 2019 - 5:00am
$3,905

Details

Jube is a platform for Augmented Predictive Analytics. The problem Jube sets out to address is that there is an ever-increasing amount of data in the organisation, oftentimes spread across an ever-increasing number of databases. The problem is that datasets are hard to use for a business user in the first instance and the data required is often stored amount them quite disparately. Get Practical Implement analytical techniques in a corporate environment. Each case study is based on real world experience of the trainer and Jube.IO Gain Strategic Advantage Introduction with Numeric Prediction, Big Database Fundamentals, Machine Learning & Technical Analysis Learn From The Best Instructions on how to implement & maintain this powerful platform from the developer. LEARN HOW TO INSTALL AND MAINTAIN THE JUBE PLATFORM FOR BETTER ANALYTICS The course will present a variety of use cases that showcase all functionality available in the platform. The use cases to be explored in the course are: Fraud Prevention in eCommerce, Debit Card and Risk Based Authentication, Real Time Prescriptive Bidding in Advertising Technology, Stock Market \ Numeric Prediction, Instruction Detection via Packet Sniffing and Syslog Monitoring Jube sets out to empower the business user. The solution is that business users wrangle with data, automate machine learning and dictate precise outcomes (which can include taking immediate action, SMS \ Email Notifications or using Case Management embedded in Jube). Jube takes domain expertise as the foundation for machine learning, where domain expertise is introduced collaboratively and without any code. Jube is entirely in memory and NO IO and, despite being created by a point and click user interface, compiles rules and filters into memory to wrangle data accross thousands of rules in a fraction of a millisecond. Predictive Analytics projects often fail when it comes to integration to the operation and speed of recall, Jube deals with these challenges from the outset taking an integration first approach. IN JUST 3 DAYS YOU WILL: Learn how to apply the predictive analytics methodology in the real world. Understand how to use some of the most powerful Predictive Analytics packages in common use. Lean how to use your models in real-life, including manually running your models in batch or integrating with production systems. Understand the infrastructure of the Jube platform Experience a multitude of case studies, covering a wide range of Jube functionalities Benefit from a highly consultative engagement. There will be plenty of time to discuss your specific projects and learning objective to provide immediate return to your organisation upon course completion. Create numeric prediction models as well as more advanced classification models You will understand machine learning algorithms, introduced from a theoretical and practical perspective ORGANISATIONS THAT WILL BENEFIT: All Forward Thinking Companies Retailers Ecommerce Platforms Software Developers Banks & Financial Services Service providers Business Intelligence Firms Data Companies WHO WILL ATTEND: C-Suite Executives: CEOs, CFOs, CTOs, COOs, CIOs Heads of Technology Heads of Analytics Heads of Enterprise Marketing Managers Risk Executives Big Data Data Warehousing Business Analysts Data Officers/Scientists/Engineers Database Developers Product Managers Product Designers COURSE AGENDA: DAY 1: ARCHITECTURE, INSTALLATION AND CLASSIFICATION The Predictive and Prescriptive Analytics Methodology. Platform Technical Architecture. Case Study 1: Installing the Prerequisites and the Jube Platform. Messaging and Integration Introduction. Case Study 2: Messaging the Jube Platform. Introduction to the Entity Model System. Case Study 3: Financial Transaction Model, Payload Definition and Inline Scripting. Entity Abstractions, Abstraction Calculations, Abstraction Deviations and Search Key Cache. Case Study 4: Creating Fraud Prevention Abstractions. Sampling Activations, Response Elevations, TTL Counters and Evaluations. Case Study 5: TTL Counter Activation and Auto Tagging. DAY 2: NUMERIC PREDICTION, MACHINE LEARNING AND MORE CLASSIFICATION. The Symbol Registry, Symbol Models and Symbol Covariance. Case Study 6: Consuming Stock Prices 1D. Symbol Abstraction, Abstraction Deviation, Activation and Evaluation. Case Study 7: Emulating Technical Analysis of a Chart. Introduction to Adaptations. Machine Learning. Regression and Neural Networks using Exhaustive. Case Study 9: Training and recall of a Symbol Model Adaptation. Syslog and PropSniff integration for network and host intrusion detection. Case Study 10: Block IP on Failed Login, Abuse IP, Excessive Ping or SQL injection. DAY 3: ADJACENT AND ADVANCE FUNCTIONALITY. Case Management. Case Study 11: Fraud Prevention Alerts. Databases, Compression and Encryption Zones. Multi Tenancy, Users, Roles and Permissions. Telerik Reporting Designer. Case Study 14: Fraud Prevention Authentication Types. Entity Model Inheritance, Volume Monitoring and Background Limits. Case Study 15: Create a Real Time Auction for Advertising Technology. Getting Help from Jube, Backup and Recovery of Tenants. vb.net platform augmentation.

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