‘Big Data in FinTech’ Comes to Seton Hall
Seton Hall will host this year's Symposium in the newly renovated University Center.
Seton Hall will host this year's Symposium in the newly renovated University Center.
NJIT’s Departments of Data Science and Mathematical Sciences have launched a new Ph.D. in Data Science program, dedicated to growing the field and generating top-notch data scientists.
Experts from NJBDA, Jim Samuel, Margaret Brennan, Marc Pfeiffer and Matthew Hale, along with professor Clint Andrews from Rutgers University and other research associates, have recently released the Garden State Open Data Index (GSODI) 2023 research report.
The 2023 Symposium will include academic research sessions with presentations on current applied research in Big Data, AI, and Machine Learning. Other topics of particular interest related to the focus of the symposium are welcome, including, but not limited to AI and Finance, Blockchain, Crypto & Digital Currencies, NFTs, Big Data Business Intelligence
The theme for the 10th Annual NJBDA Symposium is Big [...]
“A deep loss to the global community of Data Science, Big Data, and Emerging Technologies”
It is our great pleasure to announce the call for manuscripts for the Journal of Big Data Theory and Practice (JBDTP) special issue - Enabling Technologies in Intelligent Healthcare: From the Internet of Things (IoT), To Artificial Intelligence, Big Data, and Blockchain.
The New Jersey Big Data Alliance (NJBDA) has taken an important step in catalyzing research collaborations in big data, artificial intelligence, and machine learning for both researchers and practitioners with the inaugural release of its new journal.
Collaborating partners TechUnited, Edge, the New Jersey Economic Development Authority (NJEDA), and the New Jersey Big Data Alliance (NJBDA), are pleased to announce the availability of the State of The State of Innovation in New Jersey Report.
This workshop focuses on learning the basics of Deep Learning with Python and Keras including data preparation, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN).