Schedule: Financial Services sessions: Data science + business analytics training: Strata Data Conference

9:00am–5:00pm Tuesday, September 24, 2019

Location: 1A 06

Data Case Studies

David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Rosaria Silipo (KNIME), Leah Xu (Spotify), Arup Nanda (Capital One), Victoriya Kalmanovich (Navy), Tusharadri Mukherjee (Lenovo), David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten), Martin Mendez-Costabel (Bayer Crop Science), gloria macia (F. Hoffmann-La Roche AG), Gwen Campbell (Revibe Technologies), Moise Convolbo (Rakuten), Muhammed Idris (Capria VC | TeraCrunch)

From banking to biotech, retail to government, every business sector is changing in the face of abundant data. Get better at defining business problems and applying data solutions at Strata. Read more.

9:00am–5:00pm Tuesday, September 24, 2019

Location: 1A 08

Findata Day

Alistair Croll (Solve For Interesting), Jennifer Yang (Wells Fargo ECS), Brian Lynch (TD Bank Group), Dan Barker (RSA Security), Rochelle March (Trucost), Catherine Gu (Stanford University), Karan Jaswal (Cinchy), Moto Tohda (Tokyo Century (USA)), Viridiana Lourdes (Ayasdi), Peter Swartz (Altana Trade), Mikheil Nadareishvili (TBC Bank)

From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry. Read more.

1:30pm–5:00pm Tuesday, September 24, 2019

Location: 1A 12/14

Deep learning methods for natural language processing

Data Science, Machine Learning, & AI

Garrett Hoffman (StockTwits)

Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include Word2Vec, recurrent neural networks (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks. Read more.

11:20am–12:00pm Wednesday, September 25, 2019

Location: 1A 21/22

Scaling data engineers

Data Engineering and Architecture

Evgeny Vinogradov (Yandex.Money)

With a microservice architecture, a data warehouse is the first place where all the data meets. It's supplied by many different data sources and used for many purposes—from near-online transactional processing (OLTP) to model fitting and real-time classifying. Evgeny Vinogradov details his experience in managing and scaling data for support of 20+ product teams. Read more.

1:15pm–1:55pm Wednesday, September 25, 2019

Location: 1A 06/07

Improving OCR quality of documents using generative adversarial networks

Data Science, Machine Learning, & AI

Nagendra Shishodia (EXL), Chaithanya Manda (EXL), Solmaz Torabi (EXL)

Every NLP-based document-processing solution depends on converting scanned documents and images to machine readable text using an OCR solution, limited by the quality of scanned images. Nagendra Shishodia, Chaithanya Manda, and Solmaz Torabi explore how GAN can bring significant efficiencies in any document-processing solution by enhancing resolution and denoising scanned images. Read more.

1:15pm–1:55pm Wednesday, September 25, 2019

Location: 1A 08/10

Machine learning and large-scale data analysis on a centralized platform

Data Science, Machine Learning, & AI

James Tang (Walmart Labs), Yiyi Zeng (Walmart Labs), Linhong Kang (Walmart Labs)

James Tang, Yiyi Zeng, and Linhong Kang outline how Walmart provides a secure and seamless shopping experience through machine learning and large scale data analysis on centralized platform. Read more.

2:05pm–2:45pm Wednesday, September 25, 2019

Location: 1E 10/11

Executive Briefing: Building a data-assisted organization

Strata Business Summit

Arup Nanda (Capital One)

Every organization wants to use data more effectively and as a weapon, but few succeed. Arup Nanda explores how Priceline started on this journey and how it was successful using different techniques and tools. Join in to learn how to streamline data assets, make it easier for end users, define KPIs, create value from data, and build sponsorships to build a data organization. Read more.

2:55pm–3:35pm Wednesday, September 25, 2019

Location: 1A 12/14

How machine learning meets optimization

Data Science, Machine Learning, & AI

Jari Koister (FICO )

Machine learning and constraint-based optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. But Jari Koister examines how they're similar, how they're different, and how they can be used to solve complex problems with amazing results. Read more.

2:55pm–3:35pm Wednesday, September 25, 2019

Location: 1A 15/16

How Orange Financial combats financial fraud over 50M transactions a day using Apache Pulsar

Data Engineering and Architecture

Weisheng Xie (Orange Financial), Jia Zhai (StreamNative)

As a fintech company of China Telecom with half of a billion registered users and 41 million monthly active users, risk control decision deployment has been critical to its success. Weisheng Xie and Jia Zhai explore how the company leverages Apache Pulsar to boost the efficiency of its risk control decision development for combating financial frauds of over 50 million transactions a day. Read more.

4:35pm–5:15pm Wednesday, September 25, 2019

Location: 1A 06/07

Deep learning on mobile

Data Science, Machine Learning, & AI

Anirudh Koul (Microsoft), Meher Kasam (Square)

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Anirudh Koul and Meher Kasam take you through how you can get deep neural nets to run efficiently on mobile devices. Read more.

11:20am–12:00pm Thursday, September 26, 2019

Location: 1A 08/10

Working with time series: Denoising and imputation frameworks to improve data density

Data Science, Machine Learning, & AI

Anjali Samani (CircleUp)

The application of smoothing and imputation strategies is common practice in predictive modeling and time series analysis. With a technique-agnostic approach, Anjali Samani provides qualitative and quantitative frameworks that address questions related to smoothing and imputation of missing values to improve data density. Read more.

3:45pm–4:25pm Thursday, September 26, 2019

Location: 1A 12/14

An introduction to machine learning on graphs

Data Science, Machine Learning, & AI

David Mack (Octavian)

Graphs are a powerful way to represent knowledge. Organizations, in fields such as biosciences and finance, are starting to amass large knowledge graphs, but they lack the machine learning tools to extract insights from them. David Mack offers an overview of what insights are possible and surveys the most popular approaches. Read more.

3:45pm–4:25pm Thursday, September 26, 2019

Location: 1A 23/24

Enabling big data and AI workloads on the object store at DBS Bank

Data Engineering and Architecture

Vitaliy Baklikov (DBS Bank), Dipti Borkar (Alluxio )

Vitaliy Baklikov and Dipti Borkar explore how DBS Bank built a modern big data analytics stack leveraging an object store even for data-intensive workloads like ATM forecasting and how it uses Alluxio to orchestrate data locality and data access for Spark workloads. Read more.