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  • Safari, an O'Reilly Media Company.  (17)
  • [Erscheinungsort nicht ermittelbar] : Data Science Salon  (17)
  • Electronic videos ; local  (17)
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  • Electronic videos ; local  (17)
  • 1
    Online Resource
    Online Resource
    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.
    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
    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
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    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.
    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
    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.
    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
    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.
    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
    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.
    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
    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.
    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.
  • 11
    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.
  • 12
    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.
  • 13
    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.
  • 14
    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.
  • 15
    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
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    Online Resource
    MPI Ethno. Forsch.
  • 16
    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
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    Online Resource
    MPI Ethno. Forsch.
  • 17
    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
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    Online Resource
    MPI Ethno. Forsch.
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