Sept 17

Introduction - Subash D’Souza - Organizer at Data Con LA
Keynote - Ron Galperin - LA Controller at City of LA
Keynote - Gil Elbaz - Partner at TenOneTen
Keynote -Alison Burnham - Chief Data and Analytics Officer at RepairSmith.com
Keynote - Karen Borchgrevink - Executive Director/ Founder at LA Tech4Good

Detailed discussion of the Los Angeles County InfoHub project that enables deep analytics in support of Board of Supervisors, state, federal and academic requests and mandates. The talk will cover the current state, limitations and future plans for the platform.

In this talk, we will discuss the system we built for Data Scientists to easily get their models into production. We will focus on the abstraction we used to write a model, and how that enables us to simplify their workflow with self-service, configuration-driven offerings.

Ensuring online panel integrity is a time-consuming and manual process, yet critically important. How can we identify fraud, minimize it and focus instead on building a healthy, robust panel? We’ll look how DISQO uses streaming data to detect fraud with a success rate that is equivalent or even greater than manual detection.

Social media data analytics represent unprecedented opportunities for social and humanitarian good. From natural language processing (NLP) to computer vision, various techniques grounded in artificial intelligence enable a wide range of applications, from sentiment analysis to image recognition. Ongoing challenges include model interpretability and data privacy.

What is audio data? How to build features and classification models on audio? How to solve audio data problems in Python? This talk will answer these questions and highlight the challenges in classifying audio, the features that work well and their extraction, useful Python open-source libraries and examples of applications.

The Covid-19 pandemic has changed the minutiae of our lives to an extent no other event has in recent memory. How did this affected crime? Did stay-at-home orders affect burglaries? Did we have less vehicles stolen when people stopped driving? In this talk using machine learning models and data analysis, we look at crime during the worst moments of the pandemic and see if and how it was impacted.

How is it possible for data professionals to empower those that use their data analysis and insights to use that data for good? Learn how Data Product Manager, Cindy Lin, implemented theories around ensuring ethical and equitable data usage into practice with a company wide pilot.

Data, particularly public web-based data, drives forward an openly competitive market. What if I told you that web data also plays a major role in fighting crime, combating social injustice and even saving lives? In this presentation, Lenchner will highlight how web data has been pivotal in promoting noteworthy causes.

Human trafficking is a global issue on a horrific scale, with an estimated 25 million victims across the globe each year.1 Trafficking is the recruitment and enslavement of people by fraud, force, or coercion. "Accenture's Applied Intelligence created the solution for combating human trafficking using data and analytics called Artemis.

Agile Scrum Overview will provide you with insight into the agile scrum framework, scrum roles, scrum events, and scrum artifacts. The scrum insight is supplemented with real world experience and the tools to run scrum and gain competitive advantage.

Data lakes as part of the logical data warehouse (LDW) have entered the trough of disillusionment. Some failures are due to lack of value from businesses focusing on the big data challenges and not the big analytics opportunity. After all, data is just data until you analyze it. While the data management aspect has been fairly well understood over the years, the success of business intelligence (BI) and analytics on data lakes lags behind. In fact, data lakes often fail because they are only accessible by highly skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives? Shant Hovsepian explains why existing BI tools are architected well for data warehouses but not data lakes, the pros and cons of each architecture, and why every organization should have two BI standards: one for data warehouses and one for data lakes.

Data & analytics are opening the doors to how businesses accelerate growth and can differentiate themselves. Join us as we talk to three industries about their journeys with data & analytics and learn how it has impacted their industry, what the barriers are to overcome and what success enablers were key to their projects.

This talk will address the problem of slow Java/.NET applications and show the data that can be used to improve application performance.

Users today now expect personalization in the products they use. Understanding user expectations is a big part of designing strong experiences. By using data to personalize in-app content and interactions, you can create immense user value by providing the right information at just the right time.

A Case Study - Data Movement and Transformation using Azure Data Factory

It describes Business Intelligence/DataOps (BIDO) development workflow using the DataOps framework, code refactoring in AWS Cloud. It provides overview of Egress, PyTest, EMR and Spark features.

Apache Kafka is more than just a messaging broker. It has an ecosystem for importing and exporting data and processing them. This talk will demonstrate how it works together using an application which loads data from social networks such as Twitter and analyzes them using stream processing and machine learning.

This talk focuses on getting production data to non-production environments safely. We use containers to deliver the data easily without the need for DBMS install shenanigans.

XSOC CORP was founded on the notion that the only real way to ensure computer systems and data are protected from external threats is through the continual and methodical use of better cybersecurity tools that include strong encryption, vastly more efficient and secure methods for exchanging keys, and advanced user authentication factors far beyond today's commonly implemented technology.

We'll show you how you can adopt " and adapt to " the latest data and machine learning infrastructure, with examples of data engineering infrastructure/pipelines, analytics platforms, backend software development lifecycles, and documentation that enables and empowers you to democratize analytics and get products out faster with fewer issues.

Discussing marketing data and where the balance is between data privacy and customer experience. The approach to this is one of data as intellectual property covered by a natural non disclosure. This includes GDPR, CANSPAM, CCPA, CASL, Social Media, Google, as well as the philosophy and ethics of privacy vs experience.

Following the explosion of big and fast data pipelines, monolithic data platforms have become the de facto architectural design for engineering teams. However, this way of thinking about data infrastructure has several failure modes that lead to unfulfilled promises, often at scale. We will address these failure modes and address the need to shift to a paradigm that draws from a more modern view of distributed architectures.

We leverage first party behavioral data to detect fraudsters. We have developed dynamic labeling techniques and implemented a supervised learning model on top of the partially available targets to solve for the most optimal coverage of good and bad actors with classical multi objective optimization approach and cnn as a backbone detection on the backend. Leveraged AWS SageMaket to deploy the model in the production.

Here we propose a Macro Model that removes the impact of an ever changing user panel on a brick-and-mortar brand's foot traffic measured over time through our network of partner apps

Learn how to see through market volatility and establish intuitive benchmarks to understand and compare different blockchain applications. We will use open-sourced data to measure the performance, utility, and health of different blockchains, become well-versed in blockchain metrics, and develop a framework to evaluate blockchains.

Customer future value is the most forward looking metric when you want to plan long term strategies for customers. We will discuss how to calculate it using data science and how we would strategize and make action plan according to it. No Coding or data science background required.

Takeaways How does structured data power search? What is the history of structured data and Virtual Assistants like Alexa, Siri, Cortana et al? How do we ensure that structured data doesn't forget People w Disabilities or other groups who are sometimes ignored? What is coming down the pike for the future of structured data? Whatever happened to federated search? Best practices in enterprise and web search

A global pandemic that shuttered movie theaters also cleared the way for the major direct-to-consumer streaming services to bolster their positions with audiences worldwide (e.g., Netflix, Disney+, Peacock, and HBO Max). Factor in the proliferation of social media platforms with vast user generated content, and there is clearly more entertainment for all of us to choose from than ever before. On this panel, you'll hear from prominent media and entertainment executives about how companies across the Hollywood ecosystem are using data to not only keep up with the competition, but also to harness insights that allow them to stay ahead of the game as traditional lines between old and new media become blurred.