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  • 1
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 6
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 7
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 9
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 10
    Online Resource
    Online Resource
    Data Science That Works, Building Impactful Products for Media Clients (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 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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
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