UNIVERSITY PARTNERSHIP PROGRAM

Resources and materials for academic professionals, students and aspiring data scientists who want to learn q and kdb

At KX, we’re passionate about all things data and we believe that computer and data science students everywhere should learn the latest data science and programming languages to build their skillsets and get them workplace-ready post-graduation. The KX University Partnership Program provides the knowledge and materials to academic professors and students for teaching and learning the next-generation of timeseries analytics, both in-person and via virtual tutorials.

INTERESTED IN TEACHING q OR kdb?

IF YOU’RE AN ACADEMIC PROFESSOR OR FACULTY MEMBER AT A UNIVERSITY, YOU ARE INVITED TO JOIN THE KX UNIVERSITY PARTNERSHIP PROGRAM WHERE WE CAN WORK WITH YOU TO:

Offer courses dedicated to kdb for time series analytics & data science

Universities can offer courses that focus on kdb and its use in time series analytics and data science. These courses can cover topics such as data preparation, data visualization, time-series analysis, and machine-learning techniques using kdb.

Integrate kdb and q programming into existing courses

kdb can be integrated into existing courses such as database management, data analysis, and machine learning. Professors can use kdb to demonstrate real-world applications of the concepts being taught in the course.

Host workshops and seminars

Universities can host workshops and seminars to introduce students to kdb and the q programming language for time series analytics and data science. These events can be led by industry professionals or kdb experts from KX.

Provide access to kdb software

Universities can provide students with access to kdb software, either through computer labs or remote access. This allows students to practice and experiment with kdb and learn more about the world’s fastest time series database.

Dr. Paul Bilokon fully understands the unrivalled value of kdb, having built an extensive career in Electronic Trading and Quant Research while working at many of the world’s leading investment banks. Paul now shares his experiences by teaching courses in the Department of Computing and the Department of Mathematics at Imperial College London, including the MSc in Mathematics and Finance. Students are frequently encouraged to use kdb and its powerful mathematical programming language q as part of their thesis work, with two such theses being Benchmarking Specialized Databases for High-frequency Data and Realtime Margin Calculations for Crypto Derivatives. Paul has also co-written several books, including one on one of the hottest topics in Computer Science, Machine Learning & Big Data with kdb+/q which is ideal for students looking to learn more about the power of q and kdb.

Dr. Paul Bilokon
Imperial College London

WHAT IS q AND kdb?

q is a programming language built for array processing and is the foundation of kdb, the world’s fastest time series database and analytics engine for AI and ML applications.

ARE YOU A STUDENT OR A BUDDING DATA SCIENTIST IN THE MAKING?

DON’T SIT BACK AND WAIT TO TAKE A STEP INTO A CAREER IN DATA SCIENCE. YOU CAN BEGIN LEARNING INSTANTLY WITH KX. CHECK OUT THE RESOURCES AVAILABLE TO YOU RIGHT NOW

JOIN US

Join a KX event as an special undergraduate guest to learn from the industry’s leading q programmers and kdb use cases.

 

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LEARN WITH US

Leverage the KX Academy to access a range of comprehensive training courses and materials to help you learn kdb for free.

 

View Courses

ACCESS kdb

Access free kdb licenses for academic use to start your journey and add new skills to your data science learning with kdb+.

 

Access kdb

Why is kdb perfect for time series analytics and data science?

kdb is ideal for time series analytics and data science due to its column-based architecture and built-in functions. The database is designed to quickly and efficiently process large amounts of data, making it an ideal choice for time series analysis and data science. kdb’s built-in functions, including machine learning algorithms, make it a powerful tool for analyzing and modeling data across all industries and a wide range of use cases.