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  • 2015-2019  (10)
  • Safari, an O'Reilly Media Company.  (10)
  • [Erscheinungsort nicht ermittelbar] : Data Science Salon  (10)
Datasource
Material
Language
Years
Year
Author, Corporation
Publisher
Keywords
  • 1
    Online Resource
    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.
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    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.
    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
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    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.
    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
    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.
    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
    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.
    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
    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.
    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
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 9
    Online Resource
    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.
    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
    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.
    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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