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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 2
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 3
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 5
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 6
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 7
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 8
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 9
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 10
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 11
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 12
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
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  • 13
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 14
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 15
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 16
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 17
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 18
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 19
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 20
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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.
    Note: Online resource; Title from title screen (viewed September 10, 2019) , Mode of access: World Wide Web.
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  • 21
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 22
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 23
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 24
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 25
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 26
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 27
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 28
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 29
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 30
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 31
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 32
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 33
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 34
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 35
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 36
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 37
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 38
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 39
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 40
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 41
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 42
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 43
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 44
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 45
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 46
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 47
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 48
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 49
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 50
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    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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  • 51
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    Online Resource
    [Erscheinungsort nicht ermittelbar] : Data Science Salon | Boston, MA : Safari
    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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