Data Analytics Online Summit (Time Zone – EST)

    Theme – Drive Pervasive Outcomes using Data

    AI, Machine Learning and Deep Learning is Re-inventing.
    Don’t get left behind.

    Data analytics is no longer the luxury of organizations with large budgets that can accommodate roving teams of analysts and data scientists. Every organization, no matter the size or industry, deserves a data analytics capability. Thanks to a convergence of technology and market forces, that’s exactly what’s happening.

    At Data Analytics Online Summit, we will be learning the latest and innovations in Data Analytics technologies, processes and tools.

    • Track 1 : Data Science
    • Track 2 : Analytics
    • Track 3 : Machine Learning
    • Track 4 : Languages, Libraries, Platforms & Tools

    Xiaojun Su
    Senior Data Scientist
    Xiaojun Su is a Senior Data Scientist at Newcomp Analytics and leads the data science team to deliver machine learning and artificial intelligence solutions for clients across diverse industries such as energy and gas, retail, hydroelectric power, government and education, and healthcare. She is passionate about new trends and how businesses can leverage new technologies to tackle real challenges. Aside from work, Su joins mentorship programs to help new graduates pursue their careers in data science.
    Diego Hueltes
    Machine Learning Manager
    Diego is the Machine Learning Manager at RavenPack, in Marbella, (Málaga, Spain). He is a teacher in the Big Data & Analytics master for ESESA IMF, an Antonio de Nebrija University title. He also collaborated teaching in the Big Data Executive Program at Escuela de Organización Industrial (EOI), a Spanish business school where he has been also a Big Data mentor. He is passionate about Machine Learning & Artificial Intelligence, and he loves to share this passion speaking in international congresses & seminars, being the opening or closing keynoter in some important ones.
    Irina Matveeva
    Chief of Data Science and AI, Reveal Data
    Irina Matveeva is Chief of Data Science and AI at Reveal Data, an eDiscovery company based in Chicago. She is also Adjunct Professor at the Illinois Institute of Technology where she teaches Data Mining. Matveeva earned her PhD in Computer Science from the University of Chicago and her professional expertise includes Natural Language and Machine Learning technologies. Matveeva frequently presents on the subject of AI and NLP. She has been a volunteer mentor for the Data Science for Social Good fellowship.
    John K. Thompson
    Global Head of Advanced Analytics & AI
    John is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics & Artificial Intelligence team and efforts at CSL. Prior to CSL, John was an Executive Partner at Gartner, where he was management consultant to market leading companies in the areas of digital transformation, data monetization and advanced analytics. Before Gartner, John was responsible for the advanced analytics business unit of the Dell Software Group. John is the author of the new book – Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates. John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017. Analytics is a book that guides non-technical executives through the journey of creating an analytics function, funding initiatives and driving change in business operations through data and applied analytical applications. Mr. Thompson’s technology expertise includes all aspects of advanced analytics and information management including – descriptive, predictive and prescriptive analytics, artificial intelligence, analytical applications, deep learning, cognitive computing, big data, data warehousing, business intelligence systems, and high performance computing. One of John’s primary areas of focus and interest has been to create innovative technologies to increase the value derived by organizations around the world. John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run - sales, marketing, consulting, support and product development organizations. He is a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution. Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.
    Vinnie Saini
    Director, Enterprise Data & Analytics Architecture
    Srujana Kaddevarmuth
    Board member
    Wessel Oosthuizen
    Associate Director – AI Lead
    I am an Associate Director with more than 9 years’ experience in data and analytics. I am part of Deloitte Analytics, a cross-functional team with a focus on embedding data and analytics and AI in organisations across Africa. I am also responsible for the Digital Transformation team in Risk Advisory with a focus on exponential technologies, AI, Digitisation and rapid prototyping. I am passionate to help people on their journey to enable their company to become a data driven organisation, and have a passion for moving Africa into the fourth industrial revolution and enabling the true potential.
    Richard Dutton
    Head of Machine Learning for Corporate Engineering

    The talk will focus on how implementing Robotic Process Automation(RPA) in the sector of finance is optimizing the manual operational processes, while offering effective protection against financial cyber threats. We'll then talk about how RPA is also beneficial for data management and is disrupting financial services with infused Artificial Intelligence(AI).

    Key takeaways from my session:
    In this session, we will walk through the process of succeeding in the analytics journey. We will examine the best approach to building a corporate analytics function and a high performing analytics team. We will describe and discuss the Artisanal and Modular approaches to hiring and building a team. We will also examine how to evolve from the Artisanal and/or Modular model to a Hybrid model. We will examine how to manage a high performing analytics team to deliver analytical models and applications that are implemented and used in daily operations to improve organizational efficiency and effectiveness. Be ready to ask questions, this will be an interactive and open discussion.
    Format can be any of the below:
    · 45 minutes Presentation with real life examples

    Understanding the challenges in applying AI at scale for online decision making, and the opportunities for improved products and architectures that lie in solving them.

    There is a ton of resources available on how to train machine-learned models, but how do you make use of them in real solutions once they are trained? If you need to evaluate models over big data sets and scale to thousands or millions of evaluations per second, such as in online recommender and ad systems, this is a very hard challenge. A few of the few biggest internet companies in the world has proprietary solutions for solving this problem, but until recently it has been out of reach for anybody else.

    This talk will explain why this is a hard problem, show some of the things that become possible once a solution is available, and describe how we use the open source platform to solve this problem in some real use cases including the world's third largest ad network.

    Bert and other deep learning technologies need to be adapted to work for long document classification

    The space race was a EEUU – Soviet Union competition to conquer the space. This competence helped to develop space technology in an incredible manner, developing other derivative technologies as a side effect.
    This race was full of success in both sides, achieving goals that seemed impossible in record time.
    From this space race we can learn some lessons that we can apply to our Machine Learning projects to have a bigger success rate in a limited amount of time.

    As more and more AI/ML models being developed, it becomes a critical topic on how to manage and governance your models. Our solution is to provide a centralized platform with one simple user inteface for data exploration, automated ML, model deployment, and drfit tracking; democrotize the access to data and enable the company to quickly operationalize the data projects.

    Today many industries have to process wast amounts of information daily. This is especially true for online advertisement, where companies need to crunch through terabytes of data daily. In this talk, we'll discuss the typical approaches for handling a lot of data fast and deploying them to production.

    Standard Price
    Till 16 October
    USD 100
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