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HPCC Systems Academic Program - Big Data Analytics Learning Opportunities

Bahar Fardanian is a key ambassador for the HPCC Systems Open Source Project. Bahar's work is an inspiration to all who experience her presentations, workshops and Hackathon challenges, in particular students looking to become software engineers or data scientists. Find out about her latest venture, which involves delivering a big data analytics course to students at Kennesaw State University.

Data for Social Good - KSU Hackathon and The ADAM Program

The aim of the March Kennesaw State University College of Computing and Software Engineering Hackathon event, was to demonstrate to students how big data analytics may be used for social good to solve problems in society that make a real difference in people's lives. HPCC Systems provided students with the opportunity to learn more about The ADAM Program and take a look at missing children trends.

CodeDay and the HPCC Systems Intern Program - Supporting Young Coders

In 2020, we accepted a CodeDay student, Jefferson Mao, on to the HPCC Systems Intern Program. He will be rejoining the program in 2021 along with two other CodeDay participants, Alexander Parra and Eleanor Carl. Find out more about their involvement with CodeDay and their intern projects, which range from using Machine Learning, our Cloud Native platform and the HPCC Systems Covid-19 Tracker.

Virtual CodeDay Winter 2021 - HPCC Systems Big Data Challenge

LexisNexis Risk Solutions have been a sponsor of CodeDay since 2014. This program was started to provide students with an opportunity to explore careers in computer science and to introduce them to coding, The COVID-19 pandemic has meant that the last few events have taken place completely virtually, including the latest CodeDay, which was was held at the end of February 2021. 

CodeDay Big Data Challenge December 2020

We were delighted to be a sponsor at another CodeDay event in December 2020. HPCC Systems was represented by Bahar Fardanian and Arjuna Chala, who provided support to students taking part in our big data challenge. CodeDay events were held virtually in 2020 attracting more students than ever. Find out about this high school specific program and our involvement in the most recent CodeDay.

Setting up an HPCC Systems cluster on AWS EKS

Our 'Going Cloud Native' blog series continues with this demonstration, showing how to setup and deploy an HPCC Systems Platform Cluster on the AWS Elastic Kubernetes Service (EKS). This tutorial style blog is a truly collaborative effort featuring contributions from Lucas Varella who is an intern studying at the University of Santa Catarina (UFSC) in Brazil, our LexisNexis Risk Solutions Group colleagues, Hugo Watanuki and Xiaoming Wang and Akash Gheewala a Solutions Architect from Amazon Web Services.

Integrating Prior Knowledge with Learning in Biomedical Natural Language Processing

Natural language processing (NLP) research has progressed rapidly in the last year, with significant performance improvement in a variety of NLP tasks. Yet, the research of NLP techniques in the biomedical domain has not progressed much. The advantage of the biomedical domain is that it has a high-quality human curated knowledge base and large-scale literature, both of which contain rich prior knowledge.

Soccer Analytics: Leveraging Raw GPS Data for Optimizing Player Performance

In today’s world, Data Analytics is an important part of sports. Sports organizations on every level use data analytics to predict and improve player performance, a team’s quality of play, prevent injury, increase revenue, and more. Christopher Connelly, a Sport Scientist at North Carolina State University (NCSU), spoke at Community Day 2019 and HPCC Systems Tech Talk 29 about the university’s Athlete 360 program. 

Deep Sequence Learning in Traffic Prediction and Text Classification

Deep learning is a subset of machine learning that is modeled on the basis of the human brain. It essentially teaches computers what comes naturally to humans (learning by examples). In this blog, we discuss how deep learning models using background knowledge were used to achieve sequence learning on traffic and natural language. We also introduce the deep learning tool, TensorLayer.
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