CHECK OUT various contents, events and more
from southeast asia & oceania!
Regional contacts:
Dr. Ian Tan (Monash University) - Malaysia

Yenny Yang (Teradata) - Indonesia

Regional updates:
Students from Malaysia, Thailand and Singapore have been dominating Teradata Analytics Challenge and Data Challenge competitions for multiple years as winners, finalists and honorable mentions!

Meet some of them here:

2019 Analytics Challenge People's Choice Winner: Asia Pacific University, Malaysia
​Vijaya Shree Raja Sekaran
Topic: "​Dynamic Pricing in E-commerce Sector​"

2019 Teradata Technology Award Winner: NIDA Business School Thailand
​​​Veerut Pathsuwan, ​Natee Panomchokpisal, ​Krittitara Sanguanchart

Useful regional resources for data/analytics faculty and students:

Indonesia: PulseLab Jakarta
"Pulse Lab Jakarta combines data science and social research to help make sense of our interconnected, interdependent, and complex world. The Lab is a joint initiative of the United Nations and the Government of Indonesia, via United Nations Global Pulse and the Ministry of National Development and Planning (Bappenas) respectively."

Check out all the resources from Pulse Lab Jakarta's blogs and video collections on how this innovative organization works to close information gaps in the development and humanitarian sectors through the adoption of Big Data, real-time analytics and artificial intelligence.


Dr. Ian Tan is a senior lecturer with Monash University in Malaysia and is actively promoting Teradata University and its resources regionally. 

Please check out one of Dr. Tan's projects below:

This dataset is a subset of more than 900 images in JPEG format that was provided by Melangking Oil Palm Plantation. The palm fruit bunch images were captured and manually classified by the floor supervisor.  Of the 900 images, some were discarded from this dataset as the images were unusable.  The images were discarded due to various reasons such as only partial fruit bunch captured or the background was too noisy (littered with loose fruits or leaves).  The final dataset used consists of 521 images classified into 6 classes:

The classes can be combined and reduced to 3 classes as such:
  • Empty + Rotten Bunches (121 images)
  • Ripe + Dirty Ripe Bunches (272 images)
  • Under Ripe + Unripe Bunches (128 images)

Although the images were classified by experienced sorting workers, some human errors are expected.  The dataset was not re-classified and there may be some that were erroneously categorized.

**We need more contents! If you are teaching data/analytics courses or hosting datathons at your universities, please let us know by emailing! We would love to feature your events here on Teradata University for Academics website!