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  • 1
    Online Resource
    Online Resource
    Microsoft AZ-304 Certification Course : Azure Design Technologies (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 11 hr., 47 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: The AZ-304 course is intended for IT engineers and architects who have previous experience of Microsoft Azure. Since Azure services were released, there have been several additions and updates to the platform that will be explored in this course. In order for you to achieve success and pass the AZ-304 exam, this Skylines Academy course will guide you through a series of sections, lectures, demonstrations, and hands-on labs, alongside other helpful study material. We have got you covered! During this course, you will gain knowledge and hands-on experience of: - Design Logging and Monitoring solutions such as Log Analytics, Network Watcher and Security Center Implement Cost Optimization for your environment - Design Compute such as Virtual Machines for Windows and Linux - Design and Manage Storage Accounts - Deploy Automation and Configure Resources - Implement Azure AD and PIM - Design and Manage Hybrid Identities - Configure Cloud Infrastructure Monitoring - Design and Manage Governance Solutions - Design Load Balancing and Network Security - Design for High Availability - Architect Security for Applications - Design an Application Infrastructure - Design Container-based Applications - Design Data Platforms like Azure SQL and CosmosDB - Design a solution for Backup and Recovery with Azure Backup and ASR - Design Migrations using Azure Migrate
    Note: Online resource; Title from title screen (viewed October 22, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    New Frontiers in ML driven Customer Intelligence (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Seemit Sheth – Head of Data Science at Capital One Micah Price – Principal Associate Data Scientist at Capital One Capital One is one of the pioneers of ‘Information based strategy’ which is essentially what we call today as data science. The world of data science in banking has evolved over time and now data science powers strategies in many diverse areas of a bank, aided by cutting-edge technologies. In this talk, we will go over a glimpse of its past, present and how it is changing banking for good as we move toward the future. In particular, we will also explore how cutting-edge algorithms and technologies are improving customer experiences.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Leveraging Entity Resolution to Identify Customers in 3rd Party Data (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Kelsey Redman – AVP, Data Science at Comerica Bank Purchasing 3rd party data on individuals can give great insights on customers, but first we have to know which individuals from that outside data source are actually customers and which are just prospects. Without a unique identifier like SSN or Driver’s License number from the 3rd party data, we have to use a combination of name, address, and demographic information to identify the matching customer. Between nicknames, misspelled names and addresses, and family members with similar names all at one address, this quickly becomes a difficult task involving heavy data cleanup and an increasingly complicated series of rules. In this presentation, we demonstrate some techniques to help resolve these entities across data sources by employing the use of supervised classification machine learning techniques to quantify and predict entity “likeness.” We showcase some of the challenges we faced with exploring other entity resolution methods, with manually labeling a comprehensive training set, and how this approach might extend to solve other data issues.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Model Monitoring Pipelines (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 23 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Boryana Manz – Manager, Data Science at Capital One Model monitoring can make or break how models empower and deliver business value. This talk will highlight the key components for the proactive design of integrated and flexible model monitoring. It will include best practices for making the process seamless, flexible and actionable.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Learning Node Embeddings in Transaction Networks (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Jesse Barbour – Chief Data Scientist at Q2ebanking Due to the specialized and sophisticated nature of many commercially focused financial products offered by banks and fintechs, building recommender systems around those products is especially difficult. Taking inspiration from the field of neural language modeling, we will discuss an application of learning node embeddings on a large-scale financial transaction graph in order to solve this problem.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Cost Management in Azure (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 1 hr., 37 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Welcome to Skylines Academy, the #1 place to learn all about Cost Management in Azure! This course is for anyone getting started with Azure Cost Management. The audience includes Azure Administrators, IT and cloud management, or any group that needs to monitor and analyze Azure spending. Course Description: Moving to Azure? Concerned about keeping track of costs? Moving to the cloud is filled with stories of cost overages and surprise bills. In this course, we use native Azure tools to keep track of an organization’s Azure spend. We view real time and historical cost data, review cost forecasting, and set alerts on monthly charges to prevent unexpected overages. This course will use a lab tenant for the examples in the demonstrations. The audience will be able to: - View and categorize spending by resource type, location, resource group and tags. - Set a budget and budgets alerts on a subscription. - Manage Azure Cost access with Role Based Access Control. - Use Azure Migrate to predict costs associated with moving servers to Azure. - Reduce Azure costs by implementing Azure Advisor recommendations. Using the Skylines Academy approach, lectures will educate you on the fundamental terms and principles of saving costs in Azure, and demos will enable you with a hands-on experience using scenarios to empower you in the real world.
    Note: Online resource; Title from title screen (viewed December 28, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    Microsoft MB-901 Certification Course : Dynamics 365 Fundamentals (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 16 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Welcome to Skylines Academy, where you will receive the best overview of Dynamics 365 and be on your way to achieving your Microsoft Certified: Dynamics 365 Fundamentals badge. This course is for you if you want to gain knowledge of Dynamics 365 modules, including: - Finance - Commerce - Sales - Human Resources - And more! Are looking to begin your Dynamics 365 journey and don’t know where to start Are looking to develop knowledge of one or more Dynamics 365 applications. Want to take the the MB-901 certification exam and are looking for a place to start. Course Description: This course is based on the MB-901 certification curriculum published by Microsoft and is set up to align with the “Skills Measured” documentation. The course also includes a practice quiz at the end of each section to solidify your knowledge as you progress. As a foundation to learning about Microsoft Dynamics 365, Skylines Academy Author and Instructor, Karen Moen, will lead your through: 1. A course introduction and study resources to help you with your learning path 2. An introduction to Dynamics 365: - What is Dynamics 365? - What is Digital Transformation? - Dynamics 365 Business Applications - Artificial Intelligence (AI) in Dynamics 365 - Mixed Reality in Dynamics 365 2. Principles of Cloud Computing: - What is Cloud Computing? - Benefits of Cloud Computing - Cloud Deployment for Dynamics 365 3. Dynamics 365 Model-Driven Apps: - Sales - Marketing - Customer Service - Field Service 4. Dynamics 365 Finance and Operations Apps: - Finance - Commerce - Human Resources - Supply Chain Management - Business Central 5. Microsoft Platform Components for Dynamics 365: - Integration - Cloud Security and Role-based Security - Power Platform Overview - Common Data Service - Reporting and Capabilities
    Note: Online resource; Title from title screen (viewed December 22, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    Azure Overview : Introduction for Beginners (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 2 hr., 16 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local ; Electronic videos
    Abstract: With the Azure Overview: Introduction for Beginners Course, you will be able to begin your journey in learning Microsoft Azure. This course serves as a complete introduction for those looking to get started with Azure, and potentially move on to AZ-900, or other certifications. Users will be introduced to cloud fundamentals and other core topics related to Microsoft Azure. This course includes the following curriculum: Cloud and Azure Overview Azure Networking Azure Storage Azure Virtual Machines Azure PowerShell – Including a FREE Azure PowerShell Guide (See link in course)! Lectures will educate you on the terms and principles of the Azure platform and demos will enable you with a hands-on experience using scenarios to empower you in the real world. Welcome to this Skylines Academy Azure training course! We’re happy you’re joining us!
    Note: Online resource; Title from title screen (viewed April 13, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 9
    Online Resource
    Online Resource
    Real Time, Contextual and Personalized Recommendations (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Dhivya Rajprasad, Data Scientist at Levi Strauss & Co Levi Strauss and Co has always been at the helm of innovation with their classic denims and seasonal takes on the future of denim . We would like to enable users who visit our website, receive our emails and visit our stores to have the most personalized experience with easier product discovery. To enable this, I have built recommendation systems based on live and past user behavior and with minimal infrastructure. The talk features two main areas: 1. How to work with minimal data, implicit feedback and business to build recommender systems that satisfy users needs while keeping in mind overarching business KPIs 2. How to use real stream of events and past indications to give a completely personalized experience that can keep updating based on user interaction with minimal architectural requirements.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 10
    Online Resource
    Online Resource
    IoT and Analytics Condition Based Maintenance (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Stanislaw Schmal Combining IoT and sensor analytics opens a new world of operations and maintenance efficiency. A real-world demonstrator of audio analytics and customized IoT device will be shown and its business application to condition based maintenance will be discussed in this session.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 11
    Online Resource
    Online Resource
    Applied Machine Learning for Spreading Financial Statements (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 22 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Moody Hadi – Group Manager – Financial Engineering at S&P Global Market Intelligence Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the analysts to focus on their high value add operations. This involves combining Optical Character Recognition using pre-trained language neural networks, with context sensitive semantic matching. We will go over the developed ML pipleline and architecture.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 12
    Online Resource
    Online Resource
    All Models are Wrong, but Some are Useful. Especially with the Right Data (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 18 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Alex Schwarm – VP/Head of Data Science at Dun & Bradstreet For many teams, the most challenging step in delivering useful results, is less about the modeling techniques and methods and more about having access to the right data with the appropriate data coverage of the domain of interest. In this talk, we will describe two specific use cases where data pays a crucial role: one around identifying supply chain risks and one related to prospect targeting. For the supply chain risk use case, we will describe how access to unique data assets reveals the impact of the Coronavirus on supply chains and how this impacts global businesses. We will also describe how some companies are offering data scientists the ability to access data to determine which data assets best solve their business analytics and modeling needs, with a particular focus on prospect targeting.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 13
    Online Resource
    Online Resource
    Leveraging Machine Learning and Open Data for Smart Cities (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Priscilla Boyd – Senior Manager, Data Analytics at Siemens Mobility This presentation discusses how AI, machine learning and data publicly available is playing a role in smart cities, walking through applications developed using ML to solve practical transportation for smart cities globally.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 14
    Online Resource
    Online Resource
    Microsoft DP-900 Certification Course : Azure Data Fundamentals (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 1 hr., 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Welcome to Skylines Academy, where you will be introduced to the world of Azure database services with the DP-900 certification course! This course is for you if you: - Want to gain knowledge of Azure database services - Are looking to warm up or begin your data journey and don’t know where to start - Develop a skillset in Azure data, which is becoming more and more sought after as organizations are seeking ways to innovate - Take the DP-900 certification exam and are looking for a place to start Course Description: - A course introduction to set the stage for your learning Core Data Concepts: - Core Data Workload Types - Data Analytics Core Concepts Relational Data on Azure: - Relational Data Workloads - Relational Azure Data Services - Basic Management Tasks for Relational Data - Query Techniques for Data using SQL Language Non-Relational Data on Azure: - Non-Relational Data Workloads - Non-Relational Data Offerings on Azure - Basic Management Tasks for Relational Data Analytics Workloads on Azure: - Analytics Workloads - Modern Data Warehouse Components - Data Ingestions and Processing on Azure - Data Visualization in Microsoft Power BI Using the Skylines Academy approach, lectures will educate you on the fundamental terms and principles of Azure data, and demos will enable you with a hands-on experience using scenarios to empower you in the real world.
    Note: Online resource; Title from title screen (viewed December 22, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 15
    Online Resource
    Online Resource
    Kubernetes on Microsoft Azure - Fundamentals (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 2 hr., 1 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Course Description: Here’s the scenario: You have an application you would like to deploy and test and in order to do so, you must create supporting infrastructure e.g., deploy virtual machines (VMs), manage networking, allow permissions to infrastructure, and identify components required to deploy the application. Those are a lot of prerequisites for deploying and testing an app! But what if you could easily deploy an application and scale it out as much as possible without the need to manage the underlying infrastructure? In this Skylines Academy course, you’ll learn all about how to do just that with an orchestration platform called Kubernetes, specifically, Azure Kubernetes Service (AKS). Skylines Academy Author and Instructor, Michael Levan, will lead your through: - An introduction to Kubernetes - Deploying Azure Kubernetes Service (AKS) - Deploying Applications - Monitoring AKS - Continuous Integration Continuous Deployment (CICD) and AKS Using the Skylines Academy approach, lectures will educate you on the fundamental terms and principles of Kubernetes on Azure, and demos will enable you with a hands-on experience using scenarios to empower you in the real world.
    Note: Online resource; Title from title screen (viewed December 28, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 16
    Online Resource
    Online Resource
    Azure DevOps (AZ-400) : Development Processes & Source Control (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 3 hr., 26 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Welcome to Skylines Academy, where you will get a detailed introduction into Azure DevOps! This course is for you in you want to: • Get the best overview of Azure DevOps from one of the IT industry's leading technologists • Develop a skillset in Azure DevOps, which is becoming more and more sought after as organizations continue to automate processes for faster results • Take the AZ-400 certification and are looking for a place to start Course Description: This course is based on a major section of the AZ-400 Certification from Microsoft: Designing and Implementing Microsoft DevOps Solutions, and includes important fundamental and in-depth lectures and demonstrations on how to start with Azure DevOps. As a foundation to learning all-things Azure DevOps, Author and Instructor Shannon Kuehn will lead you through this course and teach you the following: 1. The importance of source control and how to effectively manage and implement it: o Source Control 101 o Git o Source Control Systems o Authentication to Git Repos o Git Repo Organization 2. An overview of Azure DevOps to get you up-to-speed with the service: o Setting up Azure DevOps with Demo Generator o Azure Repos o In-depth demos on Git Credential Manager, Version Control with Azure Repos, Importing/Cloning/Forking/Branching, and Pull Requests o Azure Pipelines o Jenkins and Azure Pipelines Integration 3. Analyze and Integrate Multi-Stage Files, including: o Deploying Multi-Container Application to Azure Kubernetes Service (AKS) 4. Understanding Parallel Jobs, Build Agents, and Private Agents 5. Implementing Security for Software Configuration: o Secrets and Certificates o Demonstration on Azure Key Vault and Azure Pipelines 6. Mobile DevOps & Visual Studio App Center o Mobile Target Devices and Distribution Groups o Public/Private Distribution Groups o Target UI Test Device Sets o Tester Devices for Deployment Using the Skylines Academy approach, lectures will educate you on the fundamental terms and principles of Azure DevOps, and demos will enable you with a hands-on experience using scenarios to empower you in the real world. The Skylines Academy AZ-400 Course Series This AZ-400 Azure DevOps Development Processes & Source Control is part of a series of courses which will cover the entirety of the AZ-400 Skills Measured document by Microsoft. This course is also an excellent stand-alone knowledge option for those looking to amplify their skills in Source Control without studying for the...
    Note: Online resource; Title from title screen (viewed July 24, 2020) , Mode of access: World Wide Web.
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  • 17
    Online Resource
    Online Resource
    Dark Data & AI Pipelines to Drive Ad Sales & Grow Audiences (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Paul Barrett - Managing Director, Accenture Applied Intelligence Personalization drives more relevant conversations with advertisers and audiences. The key to personalization is data, algorithms and offers. Algorithms feed off data. The data environment is becoming more competitive and more regulated. AI at scale makes it possible to utilize internal and external data sources that were previously too complicated, too expensive or too big. The Dark Data sources can now be utilized to: • Enhance sales leads to drive better propensity scoring, better targeting and drive better sales interactions • Create a deeper understanding of audiences to create more relevant microsegments for better targeting, messaging and greater engagement. Companies that can master AI at scale can create Real-time Analytical Pipelines turning data science into Ai Enhanced customer interactions. Growing sales, Engagement and other key metrics. This talk will cover how companies are leveraging Dark Data and Ai pipelines to transform analytics into Ai enhanced interactions.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 18
    Online Resource
    Online Resource
    Who will fight for AI in Healthcare (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 36 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Amy Daali - Chair at IEEE Engineering in Medicine & Biology Society With recent advancements in artificial intelligence, the healthcare industry is primed to benefit significantly from Machine Learning and Deep Learning technologies. This talk will answer the burning question on who is going to fight for AI in healthcare? New concepts such as “DIY healthcare” will be explored. We will discuss key important players who are going to lead the battle and drive AI adoption in 2020. Together, we will navigate the current Health Technology Landscape and uncover the different challenges that are preventing the implementation of AI. We will conclude the talk by exploring how we can improve collaboration between the Data Science and Medical community to speed up AI adoption in healthcare.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 19
    Online Resource
    Online Resource
    Minority Report : Can we predict emergencies (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Michael Zelenetz – Analytics Project Leader at New York-Presbyterian Hospital Forecasting is widely used in a number of business, but can it be used to optimize operations in an emergency department? This talk will walk through the development of a forecasting model to predict future arrivals to the emergency department. We will review the fundamentals of forecasting, discuss feature engineering, and how to get your first forecast off the ground.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 20
    Online Resource
    Online Resource
    Process Automation using Machine Learning (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Raktim Saha – Director, Digital Insights, CGI The focus of this talk is to showcase how Machine learning and AI is leveraged to radically outperform traditional loan-underwriting acceptance process for one of the largest lenders in US by utilizing a broader set of market, demographic, and various events data. Implementing an automated data-driven process in a large Enterprise and especially in a highly regulated industry has its own challenge spanning from data collection to accuracy of insights, and finally streamline the overall process. The speaker will cover the business, data, and technical aspects of this implementation.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 21
    Online Resource
    Online Resource
    NEW! Microsoft MS-101 Certification Course : M365 Mobility and Security (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 6 hr., 9 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: With the NEW MS-101 Certification Course: M365 Mobility and Security, you’ll be able to take and pass the MS-101 exam. This exam, coupled with the MS-100 exam, will award you the “Microsoft 365 Certified” Enterprise Administrator Expert” certification. As a prerequisite for this course, it is recommended that you: Have some foundational knowledge of Microsoft 365 features and services. If you are looking to gain a base knowledge of M365, check out the MS-900 M365 Fundamentals course. Have taken and passed the MS-100: M365 Identity and Services exam. If you haven’t taken the course or exam yet, have a look at the Skylines Academy MS-100 M365 Identity and Services course, also by David Hood. This course includes the following curriculum: - Introduction and Study Resources - Implement Modern Device Services - Implement M365 Security and Threat Management - Manage M365 Governance and Compliance Lectures will educate you on the terms and principles of the M365 platform and demos will enable you with a hands-on experience using scenarios to empower you in the real world.
    Note: Online resource; Title from title screen (viewed April 6, 2020) , Mode of access: World Wide Web.
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    NEW! Microsoft MS-700 Certification : Managing Teams (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 4 hr., 37 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local ; Electronic videos
    Abstract: Welcome to this Skylines Academy MS-700 Certification Course! This course will help you study for and pass the MS-700 exam by Microsoft, which will earn you the coveted Microsoft 365 Certified: Teams Administrator Associate badge. This course has over 4.5 hours of on-demand content and 52 practice questions to test your knowledge; It follows the Microsoft Skills Measured curriculum. David Hood, Instructor at Skylines Academy and Senior Technical Account Manager at Microsoft will lead you through this course. David is also the author and instructor of the MS-100 and MS-101 courses for Skylines Academy and has received an impressive average rating of 4.7 stars from thousands of students! Student Reviews for the Instructor David Hood: "Very, very good instructor. I am a non-native English speaker but I could clearly understand David and the course concepts. Many thanks and I will of course recommend SKYLINES ACADEMY to friends and colleagues." "This course is extremely awesome, for both ideas and practical demonstrations. I really love the contents and the instructor." "David Hood is another great instructor from the Skylines Academy team - great content and I passed the MS-100 exam. Now on to the MS-101 course by David Hood!" Course Description: With more and more professionals working from home, it has become imperative that organizations learn how to properly administer Microsoft Teams to remote workers. This course is the perfect place to learn all about Microsoft Teams so you can run your Teams environment like a pro while making sure all security measures are in place. During your journey, Skylines Academy will lead you through a series of sections, modules, and demos to prepare you for taking, and ultimately passing, the MS-700 exam. During this course, you will learn how to: Plan and Configure a Microsoft Teams Environment: Upgrade from Skype for Business to Microsoft Teams Plan and Configure Network Settings for Microsoft Teams Implement Governance and Lifecycle Management for Microsoft Teams Configure and Manage Guest Access Manage Security and Compliance Deploy and Manage Microsoft Teams Endpoints Monitor and Analyze Service Usage Manage Chat, Calling, and Meetings Manage Chat and Collaboration Experiences Manage Meeting Experiences Manage Phone Numbers Manage Phone Systems Manage Teams and App Policies Manage a Team Manage Members in a Team Implement Policies for Microsoft Teams Apps Lectures will educate you on the fundamental terms ...
    Note: Online resource; Title from title screen (viewed July 9, 2020) , Mode of access: World Wide Web.
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    Microsoft MS-900 Certification Course : M365 Fundamentals (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 1 hr., 43 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Not sure where to start with Microsoft 365 (M365)? Whether you are new to Office 365, Windows, and/or Enterprise Mobility and Security (EMS), or simply need a refresher, this is exam is exactly what you need to form a solid foundation which will propel you on your Microsoft 365 adventure. The M365 Fundamentals course will provide you with all of the foundational information you need to jump-start your knowledge of M365. During your journey, Skylines Academy will lead you through a series of sections, modules, and demos to prepare you for taking, and ultimately passing, the Microsoft 365 MS -900 exam. The curriculum for this course includes: - Introduction - Cloud and M365 Concepts - M365 Core Services and Features - M365 Security & Compliance - M365 Pricing & Support Lectures will educate you on the fundamental terms and principles of the M365 platform and demos will enable you with a hands-on experience using scenarios to empower you in the real world. Once you've passed the MS-900 exam, check out our MS-100 Course: M365 Identity and Services to continue your learning. Welcome to Skylines Academy and the MS-900 course. Thank you for joining us!
    Note: Online resource; Title from title screen (viewed April 9, 2020) , Mode of access: World Wide Web.
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  • 24
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    Building Data Science Infrastructure (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 22 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Caitlin Hudon - Lead Data Scientist at OnlineMedEd Before AI, before machine learning and pipelines, and before dashboards and BI, an organization starts with a pile of data, some business questions, and a few ideas on how to connect the two - a greenfield, and an entry point for data science. Answering business questions and turning raw data into insights, models, and products means more than just writing code and doing analysis. A successful data science team needs tools, a communication strategy, thoughtful infrastructure, and a plan to deliver on their goals. This talk will cover how to tackle greenfield data science challenges from the perspective of the first data science hire in an organization, and how to build data science infrastructure from the ground up.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 25
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    NEW! Microsoft AZ-303 Certification Course : Azure Architect Technologies (2020)
    [Erscheinungsort nicht ermittelbar] : Skylines Academy, LLC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 13 hr., 53 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local ; Electronic videos
    Abstract: Welcome to Skylines Academy - home of the NEW AZ-303 Course! Since Microsoft announced the new AZ-303 Azure Architect Technologies exam, the Skylines Academy team has been hard at work to provide you with brand new and relevant content to help you pass the AZ-303 exam. The AZ-303 exam is the updated version of the AZ-300 exam and includes a lot of new material. The exam will be available starting June 29, 2020 so you could be one of the first to get certified! After taking the AZ-303 exam, we recommend you take the AZ-304 course from Skylines Academy (coming soon!) and exam. Once you pass both exams, you will be and EXPERT and obtain the Microsoft Certified: Azure Solutions Architect Expert badge. This course follows the Microsoft Skills Measured curriculum for the AZ-303. Nick Colyer, Master Instructor and Co-Founder of Skylines Academy will lead you through this brand-new course, filled with in-depth content around AZ-303 Azure services. Nick's previous version of this course, AZ-300, has received an average of 4.72 starts from thousands of students from every corner of the globe Student Reviews for the Azure Architect Technologies Course by Nick Colyer: "This is a great site to learn and Nick is awesome instructor. Since I have started my Journey with Skylines with AZ-900 this is the Third course I have completed so far. I am now preparing my AZ-300 Exam and after that 301." "Two Words...Just Awesome!!!" "All the information was concise and engaging. I like how there is a lecture followed by a demo right after. It really helps to reinforce the topic while it is fresh in the mind. Overall, my second course with Nick, and I am enjoying them. I passed the AZ-103 exam using Nick's course on here, and moving on to 300 and 301. Thanks a bunch!" Course Description: The AZ-303 course is intended for IT engineers and architects who have previous experience of Microsoft Azure. Since Azure services were released, there have been several additions and updates to the platform that will be explored in this course. In order for you to achieve success and pass the AZ-303 exam, this Skylines Academy course will guide you through a series of sections, lectures, demonstrations, and hands-on labs, alongside other helpful study material. We have got you covered! During this course, you will gain knowledge and hands-on experience of: Understand Azure Active Directory (AD) and Subscriptions Implement Virtual Networking Implement Virtual Machines for Windows and L...
    Note: Online resource; Title from title screen (viewed July 13, 2020) , Mode of access: World Wide Web.
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    Machine Learning for Contextual Targeting (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 20 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: With the enrichment of video content on the Internet, the market of digital advertisement grows faster in recent years. However, due to the large scale and complexity of different platforms, more and more problems such as brand safety issues and insecure contents appear. Bruce is going to talk about how these problems attracts brands' attention and how Zefr built a machine learning product to address these issues.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 27
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    Discovering What Our Players Will Thank Us For (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: The gaming industry, and the entertainment industry as a whole, is a relentless battle for the human attention span. From acquiring the "right" audience to providing the "right" content, a game's success hinges on identifying what is "right" better than your competition. Applying proper research techniques, identifying opportunities within this research, and devising smarter delivery systems to users require constant evolution in technology and methodology. In this talk, I will discuss how Zynga's data science team has risen to this challenge, incorporating interdisciplinary techniques to fit the needs of our live-game operations.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 28
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    Behaviors in Bookkeeping (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 20 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Bookkeeping is an established process businesses need to follow in order to keep track of their financials and tax returns. When translating a machine learning model into what a customer considers to be 'kept books' and what a bookkeeper considers to be 'kept books' variability and a dependency on the customer-bookkeeper relationship come into play. Some of this can be handled by education and process changes, but other elements can be instilled by creating a logic that is applied before, during and after a machine learning model to control for the various types of error. The presentation will go over the ways we can apply logical algorithms outside of the central model to improve the central model, and create a less error-prone training set and output.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 29
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    Case Studies in Data driven Merchandising (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: For retail businesses, inventory is simultaneously the company's greatest asset and its largest risk. Competitor pricing, markdowns, and returns all threaten the margins that drive success, and in practice, inventory doesn't always move according to plan. To win in this highly competitive and rapidly evolving industry, it's essential to have a flexible toolkit that accurately produces forecasts and intelligently adapts to unplanned inventory dynamics. In this talk, I'll outline how Nordstrom applies data science and machine learning to build a wholistic view of inventory management from assortment, through stocking with intelligent size runs, and ending with a customer experience that gets the right product to the right customer at the right time.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 30
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    Hands on Inquiry into Algorithmic Bias and Machine Learning Interpretability (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 35 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Fatih Akici - Manager, Risk Analytics and Data Science at Populus Financial Group As intelligent systems deepen their footprints in our daily lives, algorithmic bias becomes a more prominent problem in today's world. The position of executives and data science leaders to this issue is generally reactive, in that, companies solely respond to the requirements coming from regulatory agencies. In this presentation, I am going to argue why the leaders should be proactive in identifying biases and how they will benefit from fixing them. I will demonstrate my point on an applied example.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 31
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    What Indeed's Job Market Data can tell us about Demographics and Trends in Data Science (2020)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 19 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Chris Lindner - Manager, Product Science at Indeed Over the past decade, data science has exploded as a lucrative, high-demand career. During this time, we've seen rapid expansion in both the demand for data scientists, and for the number of individuals trying to get into the field. But what exactly does the title "data scientist" even mean? Who are these "data scientists," and where do they come from? Are we becoming overly flooded with data science candidates? What emerging trends do we see as more and more jobseekers enter the market to meet this growing demand? Indeed is the world's #1 job site. Using our data on job postings, searches, and resumes, I'll try to explore the answers to some of these questions, and paint a picture of what the job market looks like today, and where it is going in 2020 and beyond.
    Note: Online resource; Title from title screen (viewed March 24, 2020) , Mode of access: World Wide Web.
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  • 32
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    Keys to Leading Highly Effective Data Science Teams (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Darby Laffoon – Sr Manager, Big Data Platform Engineering at Charles Schwab In this session, we will walk through the various aspects of how to build and manage effective data science teams, and how to navigate the many challenges that come with such an undertaking. While there is no right or wrong, experience tells us that there are a multitude of ways to approach any challenge therein, and that’s where we will focus much of our energy.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 33
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    The Open Data Science Platform (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 25 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Andy Terrel at Numfocus NumFOCUS currently represents 26 sponsored and 24 affiliated open scientific coding projects. Glancing at them from a distance, one might have to squint a bit to see a pattern emerge. In truth, the community doesn’t mind that at all, out of the variety comes innovation. Beyond the logos and stickers, what is this community actually doing? I present the various projects and argue that we have inadvertently created one of the most successful data science platforms in existence. From this vantage point, I pose some tough questions the community needs to answer: How can we present that platform more fully? How should we organize ourselves to make our work more valuable? How do we avoid a collapse of our communities?
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 34
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    Deep Learning Applications in the Biosciences (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Charles Alcorn – Head of Data Science at Roche Molecular Systems Oncology diagnostics and treatment is a rapidly changing field of medicine with new advancements announced almost daily. The number of diagnostic tests to select treatments based on a patient’s unique genetic mutations and tumor pathology characteristics has grown, as have the number of therapies available. These therapies can be matched to a patient’s likelihood to respond to treatment based on the results from diagnostic tests. Estimates indicate that Oncologists would have to read the literature continuously around the clock 365 days a year to maintain their knowledge of new diagnostic tests and treatments. Further, diagnostic testing and treatment as well as associated treatment guidelines vary by patient population and country. This talk will describe medical content management concepts for the field of Oncology including data architecture, data curation, adopting to differing-and-ever-changing treatment guidelines, capture of up-to-date regulatory information, and country-specific examples. The roadmap that we are developing to support Clinical Decision Support Software in a Global Marketplace for our products and services will be described.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 35
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    Privacy and Algorithmic Fairness (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Manojit Nand – Senior Data Scientist at JPMorgan Chase & Co. Understanding how algorithms can reinforce societal biases has become an important topic in data science. Recent work for auditing models for fairness often requires access to potentially sensitive demographic information, placing algorithmic fairness in conflict with individual privacy. For example, gender recognition technology struggles to recognize the gender of transgender individuals. To develop more accurate models, we require information that could “out” these individuals, putting their social, psychological, and physical safety at risk. We will discuss social science perspectives on privacy and how these paradigms can be incorporated into statistical measures of anonymity. I will emphasize the importance of ensuring safety and privacy of all individuals represented in our data, even at the cost of model fairness.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 36
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    The Care and Feeding of Data Scientists (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 33 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Michelangelo D’Agostino, VP of Data Science & Engineering at ShopRunner Data scientists are hard to hire. But too often, companies struggle to find the right talent only to make avoidable mistakes that cause their best data scientists to leave. From organizational structure and leadership considerations to tooling and infrastructure to avoiding FOMO through continuing education, I’ll share concrete (and inexpensive) tips for keeping your data scientists engaged, productive, and performing their best for your business.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    Operationalize ML by Empowering People (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Jeff Sharpe – Manager / Master Software Engineer at Capital One Niraj Tank – Sr. Manager, Software Engineering at Capital One We have been working on operationalizing ML for past few years at CapitalOne Bank and would like to share our experiences and lessons we learned in building an ML platform, in our talk we plan to cover: — Self-Service for Data Scientists — Treat models, policies & features as content, not software, and allow live updates to content — Provide software engineering best practices to ML content(s) — How to meet enterprise need at scale — Lightweight services — Re-use models, data, and business logic wherever possible — Containerize software to simplify scaling — Multi-layer abstractions — Respond to real time events — Keep data in close proximity — Focus on low-latency communication and fast computations — Architect high-reliability services Some of the questions this session intend to answer: – Every FinTech enterprise needs to operationalize ML but most of them don’t know where to start, how to deliver and more importantly what not to do? – What architecture choices to explore and what tools to build to satisfy demanding needs of a thriving data science organization. – How can you build ways to include data scientists in the agile development process, leveraging their expertise in feature engineering while enabling them to take part in DevOps practices without needing full DevOps experience.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    How I Learned to Stop Worrying and Love Graph Databases (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 20 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Michael Zelenetz – Analytics Project Leader at New York-Presbyterian Hospital Healthcare data is highly connected but often lives in silos. Graph databases are promising emerging technologies for working with highly connected data. This talk will introduce data scientists to Neo4j—the leading graph database—and will discuss a proof of concept implementation at New York Presbyterian and will demonstrate some of the network analyses we were able to do as a result. This talk will be developer/data scientist focused and will include code snippets. We will introduce the graph data model and loading data into the database. We will discuss the pros and cons of graph databases. We will finish off with some practical examples from out proof of concept including community detection algorithms, using centrality to find providers who may be spreading infections, and examining physician referral patterns. Participants will leave being able to describe a graph database. They should be able to identify situations that may benefit from implementing a graph database. Finally, they should be able to create a simple graph model.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 39
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    Data and AI for Emerging Platforms (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Kabir Seth - VP Machine Learning & AI Strategy, Wall Street Journal & Alex Siegman - AI Technical Program Manager, Dow Jones Walking through the steps necessary to appropriately leverage AI in a large organization, including tips and tricks for identifying business opportunities that lend themselves to AI, as well as best practices for each step of the AI project management process, all while navigating complex organizational structures.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    A Product Development Approach to Improving Data Quality (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Dalela Bharati – Product Owner, Data & Analytics at Booking.com Poor data quality (DQ) is crippling to any data scientist. Especially organisation wide complex DQ challenges for fundamental datasets that can take months to fix. This talk/ session outlines the product development approach we adopted to solving a DQ challenge at Booking.com
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 41
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    Ad Planning & Operations : Predictive Analytics for Resource Allocation (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 28 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Leondra James, Mgr, Analytics & Operations at Saatchi & Saatchi Saatchi & Saatchi is global, full service advertising agency / creative communications network. Learn about the interesting questions they’re asking and how they’re leveraging data to answer them. Entails broad overview of predicting advertising initiative resources and their respective allocations.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 42
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    The AI Knobs, Tips and Tricks to Jumpstart AI projects (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Anusua Trivedi, Sr Data Scientist Lead, AI for Good at Microsoft AI serves the purpose of enabling human beings in making better decisions. In this session, we talk about how the actions of AI are the result of the human inputs going into its programming. We talk about how an AI’s bias is not its own, but the human bias with which it has been programmed. We emphasize the choice of the right metric and the type of data used for testing and training to avoid such bias. We discuss the need to understand the dependence between the data used and the models employed and optimize only areas that matter. We discuss how to focus on feature engineering and be thoughtful about the ethics of ML applications. Other issues such as the need for regulations and other considerations within it that require deliberation are also touched upon.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    AI and the Index Management Problem (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Douglas Hamilton – Chief Data Scientist, NASDAQ’s Machine Intelligence Lab Over the last decade, hundreds of billions of dollars of capital have retreated from active to passive management, driven in part by investors seeking lower management fees. With this we have seen rapid growth in the index and exchange traded fund space, leading to the development of several distinct classes. At least one of these classes, Smart Beta, is ripe for AI augmentation. We outline the unique challenges of index management and discuss the application of Information Theory, Machine Learning, and Optimization in solving those challenges.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 44
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    Data Science Conde Nast AI Infrastructure and Applications (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Sriram Subramanian - Head of Data Sciences at Condé Nast At Conde Nast, we have several AI/ML solutions spanning multiple business areas of the company: advertising, subscriptions, audience engagement and personalization. These solutions use different methodologies, software libraries and run in an automated manner. To manage all of this, we have developed our own purpose built AI infrastructure to support enterprise wide ML/AI applications. We will also cover a couple of applications that leverage this infrastructure and their business impact.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 45
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    Automated Feature Engineering (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 18 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Namita Lokare Feature engineering plays a significant role in the success of a machine learning model. Most of the effort in training a model goes into data preparation and choosing the right representation. In this talk, I will focus on a robust feature engineering method, Randomized Union of Locally Linear Subspaces (RULLS). We generate sparse, non-negative, and rotation invariant features in an unsupervised fashion. RULLS aggregates features from a random union of subspaces by describing each point using globally chosen landmarks. These landmarks serve as anchor points for choosing subspaces. Our method provides a way to select features that are relevant in the neighborhood around these chosen landmarks. Distances from each data point to k closest landmarks are encoded in the feature matrix. The final feature representation is a union of features from all chosen subspaces. The effectiveness of our algorithm is shown on various real-world datasets for tasks such as clustering and classification of raw data and in the presence of noise. We compare our method with existing feature generation methods. Results show a high performance of our method on both classification and clustering tasks.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 46
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    AI and ML for the Data Quality of Clinical Trial Studies (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Hemalatha B Raju – Lead Data Scientist at Biorasi Running a clinical trial is always very complex. But with the implementation of machine learning we can significantly improve the efficiency of clinical trial development. Machine learning logic and algorithms can help us advance the patient selection process for clinical trials, improve the data quality, reduce the time and cost in the execution of clinical trials. for accurate predictions of outcomes using pattern recognition. Machine learning algorithms could be potentially applied to develop comprehensive risk-based monitoring tools, fraud detection pipeline, on-study analytics models and solutions for various clinical trials that alerts the clinical monitors and the pharmaceutical companies about the quality of clinical sites . Clinical data can also be reviewed at aggregate level regularly throughout assigned studies using analytical reporting tools to support the identification of risks and data trends. The rules-based logic could be used throughout the clinical trial studies to analyze the primary and secondary endpoints, required for the safety and efficacy analysis of the investigational drug and thus improve the efficiency of clinical trial studies.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 47
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    Utilizing Data and Data Science to Optimize Tune In (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 20 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Diane Leung, Principal, Analytics Innovation at Altman Vilandrie & Company Measuring and optimizing tune-in is critically important for the media and entertainment industries. This discussion focuses on best practices for utilizing data and machine learning to optimize tune-in on national linear inventory. In order to do this properly, advertisers need to unify their marketing ecosystem, design a holistic measurement approach, and break down barriers for closed-loop, incremental measurement. In this session you will learn how to: 1) Create a framework for utilizing data and machine learning to maximize tune-in and 2) Overcome analytical obstacles created from fragmented and incomplete data.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 48
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    Literate Statistical Programming is not Just About Reproducibility (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by John Peach, Sr Data Scientist at Amazon Alexa Science is facing a crisis around reproducibility and data science is not immune. Literate Statistical Programming is a workflow that binds the code used in an analysis to the interpretation of the results. While this creates reproducibility it also addresses issues around, auditing, re-usability and allows for rapid iteration and experimentation. This talk will describe a workflow that I have successfully used on small-scale data-sets in start-ups and on Amazon-scale problems in my work on Alexa. The talk will cover the tooling, workflow, and the philosophy you need to master Literate Statistical Programming.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 49
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    Rocky Journey to AI Adoption (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Sangeeta Krishnan – Former Director, Enterprise Data Management & Strategy at Asembia Artificial Intelligence (AI) is the current buzz word across all industries. Marvin Minsky’s definition of AI describes it as the science of making machines do things that would normally require human intelligence. However, the opinions are split across two camps. On one side we hear about new AI gadgets getting introduced in the market that would empower and improve our lives. At the same time, there are stories of AI companies going bankrupt like the recent closure of AI powered clock – Bonjour. Any digital technology has its own risks such as Cyber Security, Data Privacy Protection, apart from protecting the interests and careers of the human workforce. When you attempt to transform data into answers, many questions erupt. AI is a combination of techniques that includes Data Analytics and Predictive Analytics. Many organizations start their Analytics and AI journey without implementing the discovery phase of defining clear achievable business values. This results in most of the R&D budget to evaporate in experimental pitfalls with no real gain, resulting in AI being non-productive. It is imperative for any organization to discuss and focus on certain fundamental areas and challenges before embarking on the AI journey. This presentation would give a detailed overview of the areas that need to be addressed for the AI journey to be successful beyond PoC (Proof of Concept). It would demystify AI from an implementation stand point and would cover multiple aspects to demonstrate the current capabilities of AI technologies. It would also put out a road map for the industries to have a smooth journey to the AI world. The presentation would discuss the entire spread of activities such as budgeting, risk mitigation, training and many more.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 50
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    Personalization at Scale with AI (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Ayan Bhattacharya - Advanced Analytics Specialist Leader, Deloitte Consulting Conversational AI is the application of a combination of AI and cognitive services including Natural Language Processing, Speech Recognition and Intent Classification. It is focused on bringing voice, chat, and personal assistant technologies to intelligently automate human and technology interactions and improve client’s business outcomes as well as engagement with customers. There are varying levels of complexity in the virtual agents or chatbots; some are designed for answering common questions while others have a more complex architecture which includes the ability to disambiguate complex dialog and integrate with machine learning algorithms. The current marketplace for Conversational AI is being driven by cloud enabled platforms such as AWS, GCP, Azure and Watson; and there are over 2000 companies that have started developing their own niche industry solutions.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 51
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    Interpretable Predictive Models in the Healthcare Domain (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 32 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Sridharan Kamalakannan, Head of Data Science at Humana Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting.
    Note: Online resource; Title from title screen (viewed February 21, 2019) , Mode of access: World Wide Web.
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  • 52
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    Enabling Better Shopping Experiences Using Technology, Data, & Fashion Attributes (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Rhonda Textor, Head of Data Science at True Fit Recent advances in technology such as computer vision, deep learning, and recommender systems are being used to enable new shopping experiences. Examples include visual search, recommending similar items, and recommending items that other shoppers also viewed. However, technology alone without an understanding of shoppers, fashion, and retail falls short of solving shopping recommendation problems. We at True Fit believe that details matter, especially for modeling individual fashion preferences. Because of that, we have built the largest fashion and retail dataset called the Fashion Genome. In this talk, I will share some insights we have gained from the Fashion Genome that have influenced our approach to building fashion recommendation systems. I will also show how we leverage the fashion details of products, i.e., our Fashion Attributes, to combine fashion and technology to make great recommendations. Finally, I will share how we leverage our large dataset and cutting edge technology to enable personalized shopping experiences.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 53
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    Content Similarity (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 20 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Sylvia Tran, Data Scientist at Gracenote User preferences and content similarity are both key to recommendation systems. While content similarity has been widely explored and utilized by many companies in the media & entertainment industries, it still remains relevant as the amount of data and metadata available continues to grow and change. This talk discusses some of the challenges of content similarity and explores a few different attribute groups (aside from genre and cast) by which content similarity can be measured. Traditional attributes, like genre and cast alone, may not be as additive as they once were. More specifically, movies like Ted (starring Mark Wahlberg) and Shaun of the Dead do not neatly fit into a single genre. This talk also demonstrates how certain tried and true similarity metrics still yield meaningful and reasonably interpretable results for media & entertainment.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 54
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    Challenges in Machine Learning from Model Building to Deployment at Scale (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 19 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Anupama Joshi Companies are moving towards AI/Machine learning very fast. Data scientist are building models and training models. But challenges come when deploying models in production. How to maintain multiple models? Creating a common platform that allows model management and deployment easily and reliably is becoming a necessity for organizations to accelerate product development. In this talk, I will talk about the challenges faced and the solutions used to make this process easy.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 55
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    Algorithmic Recommendations at The New York Times (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Anna Coenen Algorithmic curating at the Times brings many unique challenges. We want our recommendations to feel personal and relevant, but not creepy. We want articles to be timely, yet also showcase older pieces that our readers still enjoy. We want to increase engagement, but without sacrificing editorial judgment. This talk describes how we achieved these goals through a combination of Machine Learning, experimentation, and diligent editorial curation.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 56
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    How to Automate Pricing with Machine Learning : Challenges and Learnings Jennifer Est (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 22 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Jennifer Esteche, Machine Learning Research Engineer at Tryolabs Determining prices for retail products manually is an extremely time-consuming process and leads to neither cost-effective nor consistent pricing. In this talk, we will introduce you to the tremendous potential of machine learning for automated pricing. We will share our learnings from building a machine learning system able to determine prices based on product metadata and images, implemented by a large online consignment marketplace. As a result, the company prices more than half of the products in their catalog automatically. You will learn what kind of data is valuable for an automated pricing system, what machine learning models can be used, some major challenges in building an intelligent pricing system and how to overcome them. Further, we will explore opportunities on how retailers can use machine learning for optimizing their prices for revenue.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 57
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    Data Science That Works, Building Impactful Products for Media Clients (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Lauren Lombardo - Senior Data Scientist at Nielsen Products underpinned by traditional statistical methods, big data, and artificial intelligence increasingly provide new and unique insight into the rapidly growing media landscape. How can you make sure that your product will have the impact that you expect it to? What factors do you need to consider when developing a product to ensure that the tool is understood by the market and used appropriately? To what extent is this the concern of a data scientist? This talk will discuss how these questions apply to those hoping to deliver data-oriented insight to the media and entertainment industry.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 58
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    Search Engine in Startups : What to Optimize (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 28 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Liang Wu, Machine Learning Data Scientist at Airbnb Choosing the correct optimization metric is key to success of a search engine. Unlike in traditional web searches, where clicks are clearly the main objective to optimize, many emerging vertical search engines like E-Commerce search may require a different optimization metric, such as conversions, revenue, and quality. Selection of a good metric may depend on the query type (transactional vs. navigational vs. informational) and also on the goal of a business (profitability vs. growth). For example, a typical product search engine may focus on maximizing the number of transactions and total revenue, while navigational search may aim at minimizing the total number of clicks. In this talk, we will investigate factors needed to be considered when we are in search for a good metric, and we will also walk through an example of designing an optimization metric for a particular business, including how it is selected, mathematically defined, and optimized with a machine learning framework.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 59
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    Product(ive) Data Science (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 26 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Julie Hollek, Sr Data Scientist at Twitter Data scientists are the people behind the scenes, helping others deliver better, smarter results in their daily work. This is especially true for product data scientists who must hone their craft to determine what things are working for a given product, where do we want to take it next, and how can we make product decisions aligned with company is trying to build? Cross-functional communications are critical to success in this role; you need to be able to craft a message that is born out of math to make compelling arguments that are digestible by stakeholders across the business. In this session, we’ll define Product Data Science and discuss contributing factors to success.
    Note: Online resource; Title from title screen (viewed February 21, 2019) , Mode of access: World Wide Web.
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  • 60
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    Using Deep Learning to Build a Unified E-commerce Marketplace (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 19 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Ali Vanderveld, Director of Data Science at ShopRunner ShopRunner is an e-commerce company that receives feeds of product data from over 100 different retailer partners, including large department stores and retailers that specialize in electronics, appliances, nutritional products, and more. In order to provide a great user experience on our website and in our mobile app, we need to have one easy-to-navigate product taxonomy. We also would like to have sets of attribute tags that make it easy to filter down to exactly what any shopper is looking for. In this talk I will describe how we are using computer vision and natural language processing to place all of the products from our retailer partners into one easy-to-navigate shopping experience.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 61
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    Machine Learning and Data Monetization (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Robert Welborn
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 62
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    Improving the Worlds Largest Online Grocery Catalog : A Data Science Story (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 28 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Ishant Nayer, Sr Data Scientist at Instacart Being a data-driven company, Instacart realizes the power of good quality data. While trying to maximize the efficiency of data consumption by all work streams such as recommendation systems and availability systems, we are trying to make our Catalog the best in the world by delivering precise information to all our end-users including shoppers and consumers. This session will cover the following areas: 1. How we used data science to auto-detect inconsistencies in the data attributes of millions of items comprising the Catalog, in real-time. 2. How we defined and utilized North Star metrics to optimize data quality of our Catalog 3. Different approaches being used to deliver a great customer experience.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 63
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    Apply Natural Language Processing in Segmenting Online Audience (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 13 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Jasmine Ngo, Mgr, Analytics & Marketing Science at Deutsch Sometimes, conversations on social media don’t reflect the mass’ sentiments accurately (think about people who rarely use Twitter or have a public profile on Facebook). That’s when local news come into play – local articles can sometimes reflect sentiments on certain topics by specific areas. By scraping thousands of articles online and using NLP / other methodologies to analyze them, we can get interesting insights on different topic. This talk introduces a tool to do that.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 64
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    The Tabloid Proteome : Orthogonal use of public proteomics data to derive biologically related protein network (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 19 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Surya Gupta – Postdoctoral Researcher at VIB-UGent Mass-spectrometry based proteomics experiments produces large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal biological knowledge. We have developed a novel method for such orthogonal data reuse of public proteomics data to detect biologically associated protein pairs. Mass-spectrometry proteomics experiments were obtained and reprocessed from the PRIDE database. For the identified proteins, we calculated the co-occurrence score, using Jaccard similarity. Protein pairs with score of atleast 0.4 were mapped to five knowledgebases; Reactome, Ensembl, IntAct, BioGRID, and CORUM, to assign potential biological relevance. Of the 2325 protein pairs that pass the Jaccard similarity threshold, we 81% of protein pairs with biological annotation (68% with five knowledgebases and 13% with GO terms). While comparison with randomly selected protein pairs, less than 2% protein pairs were found to be annotated. Furthermore, to extend the usability and accessibility of the detected protein pairs for research community, an online database called Tabloid Proteome was established. Our approach shows that by re-using publically available data in a fully orthogonal way, effectively treating these data as a proteome-wide association study, we can extract various biologically meaningful patterns, which moreover, were quite complementary to associations detected by established protein-protein interaction techniques. Additionally, Tabloid Proteome features a simple yet powerful web interface that allows fast and easy access to all these protein associations, with their possible biological annotation.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    Machine Learning & AI : Demystified for TV Advertising (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 26 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Diane Yu, CTO and Cofounder at FreeWheel, Comcast As the leading provider of financial and company data, Bloomberg has access to vast amounts of data on a daily basis. There are two common challenges when working directly with raw data. One is the need to discover and extract data represented in the natural document format that is not machine-readable. Another requirement is validating and ensuring that the data is of high-quality since it is required for building models for predictions, classifications, and various analytics tasks. This talk will cover ways in which data science and machine learning can be used to address these two challenges: (1) ingesting your data by extracting what is contained in natural document format and (2) cleaning your ingested data.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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    Just Ask : Designing intent driven algos (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 26 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Anna Schneider, Data Science Manager at Stitch Fix Classic recommender systems are great for answering the question “what does a user want in general?”. However, they only get you partway to an answer to “what does a user want right now?”. To close the gap, it helps to capture and act on real-time user intent. I’ll share two examples of this paradigm, and the resulting changes to our algorithms and architectures at Stitch Fix.
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    Fraud Prevention from the Ground up (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
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    Language: English
    Pages: 1 online resource (1 video file, approximately 24 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Jason Dolatshahi – Director of Data Science at Stash Stash is the digital platform for saving & investing that promises financial inclusion for all. When we launched our checking account at the end of last year, we already had millions of customers, but we were about to make first contact with a formidable new foe: bank fraud. As a data science team, how do you develop and deploy a model when the dimensions of the problem are brand new and changing quickly? How do you ensure your stakeholders have the information they need to manage the liability risk to the business? How do you keep the user experience delightful while keeping out bad actors? Come find out!
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 68
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    Applications of Embeddings and Deep Learning at Groupon (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Bojan Babic - Sr. Software Engineer at Groupon Groupon is a dynamic Marketplace where we try to match millions of the deals organized in different verticals and taxonomies with the demand across 20 countries around the world. Modeling such complex relationships requires sophisticated machine learning models that utilize hundreds of user and deal features. Customers discover deals by directly entering the search query or browsing on the mobile or desktop devices. The purpose of this paper is to describe a series of techniques used to improve various parts of Search and Ranking algorithms by utilizing the embeddings representations of the user and deal features. The paper will describe improvements made in Query Understanding, Deal Classification, Similar Deals Recommendations and computation of an Image Propensity to Purchase that leverage respective embedding feature representations.
    Note: Online resource; Title from title screen (viewed February 21, 2019) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 69
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    Online Resource
    Using Machine Learning to Target Households in Large Scale Direct Marketing (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 19 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Finding the right audience is the core marketing puzzle of all e-commerce businesses. While in the past this was mostly achieved using coarse segmentation and personas the broad availability of data makes nowadays possible to learn individual consumer preferences by training large scale machine learning models which can combine knowledge from thousands of dimensions and measurements. HelloFresh is the largest meal kit delivery service worldwide; in this talk, we will review how we use machine learning (gradient boosting machines) to acquire millions of customers every year and describe our large scale model training and scoring infrastructure.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 70
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    Online Resource
    10 Questions before you set up your Data Science Team (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Congratulations, you are the new leader of data science at a traditional company. Your customers are internal business units, and everyone is intrigued about the possibilities that data science can bring to the company. You are aware of multiple opportunities to apply a data driven approach and you are ready to jump in. But, wait! Before you start; here are 10 key questions that you may want to ask yourself before you embark on the first project. Knowing how to code or to model will only take you so far, as a leader you are going to have manage several other challenges. The earlier you can answer these questions the faster you will be able to run a successful program.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 71
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    On the Road to More Holistic Player Understanding (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: One of the challenges seemingly all data scientists face is finding a clean data set which contains the state of the player right before they take some event in the system. Typically we need to interact with event-driven systems and/or databases that only maintain the current snapshot of the player. In this talk we highlight some work we have done to recreate the up to date snapshot of the player captured before each event and demonstrate how we can leverage this dataset to improve personalization and model the players' likelihood to churn.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 72
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    Online Resource
    Can Data Science Help Us Find What Makes a Hit Television Show (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 31 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Shilpi Bhattacharyya, Data Scientist at IBM Who does not love the American television sitcom - Friends? And we definitely want to learn what makes this sitcom so popular. Can the most important aspects of some of the top shows of all the times be related? Is there something common which makes them a success? If not, can we find out and draw a correlation amongst them? In this talk, I would demonstrate the essential elements of few of these most successful sitcoms which have helped them connect with the audience at such a massive scale around the world. I would use data science and machine learning techniques as sentiment analysis, data visualization and correlation graphs on the transcripts available for these sitcoms to achieve the results. I would also focus briefly on the favorite characters. I believe this work would be able to bring out a concrete answer to the apparent question amongst the makers to understand the reasons which makes a hit show, with evidence backed up by data science.
    Note: Online resource; Title from title screen (viewed November 7, 2019) , Mode of access: World Wide Web.
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  • 73
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    Online Resource
    The UX of Data (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 17 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Far too often, product leaders and designers are not able to autonomously understand how to analyze their product's success. This talk focuses on how to think through how non-engineers experience data. I will cover intuitive event and property naming, documentation and practices that can empower everyone on your team to better understand data. If you care about making data more accessible, this talk is for you.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 74
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    Online Resource
    Hulu and Hang : Understanding Viewer Sessions with Big Data (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 29 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Everyday, Hulu ingests 100 terabytes of user level app interaction data. This session data is the closest touchpoint we have to subscribers' experience in our product short of joining them on their couch in their living rooms. Making meaning out of session data is a non-trivial effort across data instrumentation, engineering, analytics, and data science teams. In this talk, you will get an inside look at how we are tackling this monumental project at Hulu: from product design, to generating insights, to building predictive models - all to create the most personalized and engaging streaming experience for our subscribers.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 75
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    Online Resource
    Processing Raw Financial Data : Challenges and Solutions (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 21 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Connie Yee - Data Scientist at Bloomberg As the leading provider of financial and company data, Bloomberg has access to vast amounts of data on a daily basis. There are two common challenges when working directly with raw data. One is the need to discover and extract data represented in the natural document format that is not machine-readable. Another requirement is validating and ensuring that the data is of high-quality since it is required for building models for predictions, classifications, and various analytics tasks. This talk will cover ways in which data science and machine learning can be used to address these two challenges: (1) ingesting your data by extracting what is contained in natural document format and (2) cleaning your ingested data.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 76
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    Online Resource
    Re-Inventing Customer Engagement Using Machine Learning (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Consumers today are less brand loyal and primarily driven by rewards, benefits and experiences. Constant changing customer preferences and card business economics point to a need for being hyper focused on customer engagement. In this presentation, Visa Consulting & Analytics showcases some of the approaches to increase customer engagement and improve retention using Machine Learning techniques on transactional data.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 77
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    Online Resource
    Time Series Analysis with Pandas (2019)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 27 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Joshua Malina - Senior Machine Learning Engineer at AMEX Time series data is really fun to play with, but you have to know how to do it. In this talk, I dive into an open source data set to show you how Pandas makes time series data investigation more accessible. After this presentation, you will know about, time series decomposition, hypothesis testing and investigation, data quality issues related to time series, and resampling methods.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 78
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    Recent Advances in NLP and How they Impact Trading (2018)
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (1 video file, approximately 30 min.)
    Edition: 1st edition
    Keywords: Electronic videos ; local
    Abstract: Presented by Brittany Rockwell Researchers and financial practitioners alike are attempting to find meaningful applications for the newest advents in the field of natural language processing (NLP). Although language modelling for stock trading is not novel, the landscape has changed significantly in recent years. With the improvements of computational power, data storage and algorithmic efficiency, we have more data and modelling capacity at our disposal than ever before. A natural consequence of these changes is the exponential growth of available data. There is a growing need for automating the data exploration and identification process for low-latency downstream applications requiring timely forecasting or decision making. This talk will discuss some of the new advances in NLP and their relevance to text-driven trading.
    Note: Online resource; Title from title screen (viewed March 24, 2018) , Mode of access: World Wide Web.
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