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Big Data Madison promotes the understanding and adoption of technologies used to acquire, store, and analyze data in all its forms. This spans everything from data engineering to data science.

Everyone is encouraged to attend, no level of experience is too basic to join and learn.

We will focus on some of the technologies used in the Big Data ecosystem (Hadoop, Spark, streaming data and data processing, etc), as well as topics in Data Science (machine learning, data visualization, analytics and more). We will try to balance the topics between technology talks, use cases, and demos.

Bevorstehende Events (2)

Managing and Governing your Data Assets from Edge to AI

Madison Central Public Library

Abstract: From autonomous vehicles and surgical robots to churn prevention and fraud detection, enterprises rely on data to uncover new insights and power world-changing solutions. Organizations today require multi-function analytics capabilities across all data types and sources to eliminate silos and speed the discovery of data-driven insights. With this flexibility also comes the responsibility of ensuring that your data is secured and governed across any infrastructure, on-prem, cloud or a hybrid from Edge to AI. In this discussion, we will walk through how organizations are leveraging open source Apache projects such as Ranger and Atlas to provide granular, dynamic, role and attribute-based security policies to prevent unauthorized access to sensitive or restricted data access. Along with security policies, we will also discuss how enterprise are leveraging auditing and governance capabilities to drive compliance with GDPR, CCPA and other privacy regulations. BIO: Muki Soomar is a solution engineer with Cloudera helping organizations with their digital transformation journey using cutting edge technologies within the Big Data open source eco-system for real-time and historical analytics. Muki is passionate about using innovative technologies that can help solve complex business problems enabling businesses to derive real-time actionable insights through Predictive and Prescriptive Analytics using CEP and ML; analyzing business events that are generated from not only internal transactional systems but also from external data sources such as social media, mobile devices and many others that the businesses have to react to every day. Muki has over 20 years of experience in the IT industry and has worked in many different roles. He has successfully delivered many complex projects working across many different verticals, including Automotive, Finance, Insurance, Airlines, Medical Device Manufacturing, Healthcare, Market Research and Retail to name a few. Across these verticals, Muki has designed and implemented business solutions using SOA, data integration, complex events processing (CEP), Master Data Management (MDM) and Modern Data Architectures using technologies from the Hadoop ecosystem. Prior to Cloudera, Muki worked at Hortonworks, Software AG, TIBCO Software, Rush Medical University, Allstate Insurance, CNA Insurance, RouteOne, Ford Motor and Chrysler. Muki has three graduate degrees - an M.Sc in Mechanical Engineering from Queen's University, Canada, an MS in Engineering Mechanics from Michigan State University, East Lansing and and MS in Computer Science from University of Michigan, Dearborn. Sponsors: I would like to thank Cloudera for both the food and an after meetup round of drinks.

When Customers Organize Products - Graph Theory in Practice

Madison Central Public Library

Abstract: Retailers have many methodologies for grouping their products together, some may use a merchant driven hierarchy, while others use a hierarchy dictated by Marketing Strategy. Either way, these product groupings influence a number of critical decisions that each retailer must make, such as how products are brought to market, how they are advertised, and how they are discounted. This talk will focus on an alternative method for developing a product hierarchy, a customer driven approach. For demonstration purposes, we will construct a toy data set to represent customer sales data, from which we will construct Random Intersection Graphs. These graphs relate products to one another via transactional history and their projections will be used to create graphs that provide alternative underlying structures for product relationships. The insights driven from uncovering these latent structures in product relationships can assist in driving strategies throughout the business. This talk will focus on how one can determine a customer’s level of brand loyalty when making their purchases. Bio: Tipan Verella is a Data Scientist in Marketing’s Advanced Analytics organization at Kohl’s, where his coffee fueled days are spent doing data engineering/wrangling/analysis, as well as building models that serve as the foundation for executive strategy. Prior to his tenure at Kohl’s, Tipan worked in AdTech, for companies such as Millenial Media and AOL (both now Verizon), primarily focusing on the performance prediction of click-through and conversion rates of online advertisements. Tipan is finishing his PhD work in Systems and Information Engineering at the University of Virginia, where he researched latent structures of complex behavioral systems using tools and techniques from probability and graph theory. Tipan is a proud Marquette University faculty spouse, and Highland Community School parent, with a penchant for mathematics and programming in Python. Sponsors: I would like to thank American Family for the food and Cloudera for an after meetup round of drinks.

Vergangene Events (90)

AI Research at American Family Insurance

Madison Central Public Library

Fotos (53)

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