National Data Science Challenge logo

23 Feb - 30 Mar 2019


The largest data science competition in Singapore


The objectives of the National Data Science Challenge (NDSC) are to:

  • Equip students and professionals with essential technical skills and expertise to prepare them for the digital and data-driven economy
  • Bring the tech community closer through working together and knowledge sharing
  • Provide an environment for the development of creative new ideas in Data Science

Through this competition, we hope to showcase the ubiquity and usefulness of data in getting insights, thus reinforcing Singapore’s emphasis on driving the digital economy and the use of big data.

About the Challenge

Free & Fun
Free & Fun

The National Data Science Challenge is free of charge! All you have to do is register and join in the fun.


Open to students currently enrolled in junior colleges, polytechnics or universities in Singapore (including exchange students) as well as non-tech and tech professionals working in Singapore.

Employees of Sea Limited are not eligible to participate in the challenge in the interest of fairness.


Form a team of 3-4 members and execute your project over 4 weeks.

If you sign up individually, we will assign you to a team.

Training Workshops
Training Workshops

Sign up early for the chance to attend training workshops specially organized by our training partners.

Training classes catering to both beginner and advanced participants are available.


The theme of the National Data Science Challenge 2019 is Product Information Extraction in the Wild - a challenge to extract insightful knowledge from large volumes of textual and visual data using Machine Learning Analytics.

Beginner Category

Product Category Classification

  • Students currently enrolled in junior colleges, polytechnics or universities in Singapore (including exchange students).
  • Non-tech professionals working in Singapore who have little or no programming and/or Data Science experience
Advanced Category

Product Information Extraction

  • Students currently enrolled in junior colleges, polytechnics or universities in Singapore (including exchange students).
  • All non-tech and tech professionals working in Singapore.

*Teams comprising students and professionals will be reviewed on a case-by-case basis.


The Top 3 teams and the Most Creative Team from each category will win attractive cash prizes!


  • SGD 3000
  • Full-time / internship opportunities in Shopee
  • Return ticket to visit Shopee Shenzhen Tech Centre

1st Runner-up

  • SGD 2000
  • Full-time / internship opportunities in Shopee

2nd Runner-up

  • SGD 1000
  • Full-time / internship opportunities in Shopee

Most Creative Team

  • SGD 800
  • Full-time / internship opportunities in Shopee

All participants will receive a certificate of accomplishment at the end of the award ceremony only if their project has been submitted successfully before the deadline.


Our panel of prestigious judges will be selecting teams from each category with the most unique and creative solutions.

Professor Bo An
Professor Bo An
Associate Professor, School of Computer Science and Engineering, NTU

Professor Bo An is an Associate Professor at Nanyang Technological University, Singapore whose research interests include artificial intelligence, multiagent systems, game theory, and optimization. He is recipient of numerous awards including INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, winner of 2017 Microsoft Collaborative AI Challenge, and 2018 IEEE Intelligent Systems' AI's 10 to Watch.

Professor Dai Bing Tian
Professor Dai Bing Tian
Assistant Professor of Information Systems
Director, MITB (Artificial Intelligence) Programme, SMU

Dr. Dai Bing Tian is an Assistant Professor at the School of Information Systems, Singapore Management University. His main research interests are data mining and machine learning, and he teaches master-level machine learning courses and algorithm design courses. He is also the director for the Master of IT in Business (Artificial Intelligence) programme.

Professor Ng See Kiong
Professor Ng See Kiong
Director, Translational Research (Data Science)
Professor(Practice), Computer Science, NUS

Professor Ng See Kiong is a Professor of Practice at the National University of Singapore, and Director, Translational Research for the university's Institute of Data Science. As an early practitioner of data mining and machine learning with diverse and cross-disciplinary research interests, his research mission is to develop artificial intelligence techniques to obtain better insights and understanding of the world through computation of data.

Dr. Pan Yaozhang
Dr. Pan Yaozhang
Head of Data Science, Shopee

Dr. Pan Yaozhang is the Head of Data Science at Shopee, having built the data science team from scratch. She is committed to introducing the most advanced technologies of machine learning and artificial intelligence into industrial and commercial applications. Before Shopee, she worked for Grab, Greenwave Systems, and was a research scientist at the Institute for Infocomm Research, A*STAR.


12 Nov 2018

Registration Opens

15 Dec 2018

Training Workshop for Beginner Category

16 Dec 2018

Training Workshop for Advanced Category

26 Jan 2019

Training Workshop for Advanced Category

2 Feb 2019

Training Workshop for Beginner Category

9 Feb 2019

End of registration

16 Feb 2019

Training Workshop by Supporting Training Partners

23 Feb 2019

Opening Ceremony (Guest-of-Honour: Minister Ong Ye Kung)

7 Mar 2019

Data Science Mentoring Clinic

23 Mar 2019

Deadline for Project Submission

30 Mar 2019

Award Ceremony & Prize Presentation

Training Workshops

Register for the challenge and you will receive updates on upcoming training workshops.

Please note that only successfully registered participants will receive invitation and details of the training workshops.

26 Jan 2019

Advanced Training

By YiDu AI

(Registration is closed)

  • Overview of Tensorflow
  • Elements of Tensorflow
  • Visualization of Computational Graph
  • Train your model with Tensorflow
  • Your first neural Network with Tensorflow
  • Advanced topics in Tensorflow
2 Feb 2019

Beginners Training

By YiDu AI

(Registration is closed)

  • Introduction to Python
  • Introduction to Data Science and Machine Learning
  • Linear Regression
  • Logistics Regression
  • Decision Tree
  • Ensemble Learning
  • Support Vector Machine
  • Hands-on Project
9 Jan - 30 Jan 2019

Beginners Training

By UpCode Academy

(Registration is closed)

  • Intro to Deep Learning
  • CNN Introduction
  • Improving CNNs
  • How a CNN learns
12 Jan - 13 Feb 2019

Advanced Training

By UpCode Academy

(Registration is closed)

  • Overfitting
  • CNN + RNN
  • CNNs Top Architectures
  • NLP
16 Feb 2019

Combined Training

(Registration is closed)

Course #1- Recommended for Beginner participants
Data Analysis with pandas by Hackwagon Academy
  • Understanding the basic functionalities of pandas library
  • Application of pandas in preparing data and descriptive data analysis
  • Manipulation of data into appropriate formats and data cleaning with pandas
Data Science 101 by General Assembly
  • Learn what data scientists do and the types of problems they solve. Walk through the typical data science workflow and see how the pros identify powerful business predictions.
  • Explore key tools and processes data scientists use to analyze, visualize, and model data and apply what you’ve learned to a data set to produce your own recommendations.

Course #2- Recommended for Advanced participants
Product Classification by Code Gakko
  • How to set up a simple data processing pipeline
  • Understanding what state-of-the-art neural networks look like
  • Using neural networks to classify and locate product in images
Fashion product recommendation system by UpCode Academy
  • Setting up a feature recognition system for fashion products
  • Generating fashion vector for images
  • Getting recommendations
16 Feb 2019

Deep Learning for Pure Beginners

BySG Code Campus

(Registration is closed)

  • What is Deep Learning?
  • Build your first neural network with TensorFlow and Keras
  • Use cases for Deep Learning


Unfortunately, registration for the National Data Science Challenge 2019 has closed.

Do look forward to our future events!

Participants who confirmed after 26 February 2019 will not be eligible to take part in the competition.

Coding is for Everyone

The video “Coding is for Everyone” showcases four individual profiles coming from different background but having the same interest - coding. The video was screened during the NDSC Opening Ceremony on 23 February 2019.

Frequently Asked Questions

Q1:Can I participate in both Beginner and Advanced training workshops?

This is subject to vacancy, as priority will be given to participants registered in the respective categories. If there are vacancies for both training workshops, we will definitely allow you to participate in both.

Please email to check for vacancies nearer to the dates of the training workshop.

Q2:I am not based in Singapore during the course of the competition, can I still participate in the challenge?

The challenge is currently only open to participants who are based in Singapore.

Q3:I am based in Singapore but I will not be able to attend the opening ceremony on 23 Feb 2019, can I still take part in the competition?

As the challenge details will only be revealed during the opening ceremony on 23 Feb 2019, we will highly encourage you to attend the opening ceremony.

You can participate in the challenge as long as a member of your team is present for the opening ceremony.

Q4:Am I required to code in python?

There is no restriction on the programming language.

Q5:I am a tech professional but with no background in data science or I have just just graduated and joined the tech industry, can I still participate in the beginner category?

This will be reviewed on a case-by-case basis.

Q6:My team consists of both students/non-tech professionals and tech professionals. Do we sign up for the Beginner or Advanced category?

You can sign up for either category. However, registration for the Beginner category of teams consisting of both students/non-tech professionals and tech professionals will be reviewed on a case-by-case basis.

Q7:How do I know if my registration for the NDSC is successful?

You will receive a confirmation email in 1-2 weeks’ time after you have registered. Please reply to the confirmation email with the required information for your registration to be confirmed by 16 February 2019, 2359h.

Q8:How will I be grouped if I signed up as an Individual?

You will be assigned to a group randomly.

Q9:What if I find another individual participant that I want to group with?

If you would like to team up with another individual, please write in to, indicate the name and email address of the individual you want to group with and cc the individual in the email.

Q10:I have been randomly allocated to a group. However, I have tried to contact them multiple times but did not receive any response. What do I do?

You can write in to to request for a new group if the team members still remain unresponsive after 2 weeks.

Q11:How and when will I get my $20 training workshop refund?

The refund will be made at the Award Ceremony on 30 March 2019. Kindly present the email receipt for the refund to be made. More details on the refund will be shared via email nearer to the Award Ceremony.

Q12:I am unable to access the data on Google Drive for the competition.

We have provided another set of links via dropbox. Please go to the 'Data' tab to download the data set.

Q13:I have questions about the competition problem statement and the data sources.

Please submit your technical questions to the 'Discussion' tab on the Kaggle competition page so that other participants with similar issues or questions will be able to assess them. Our Admin (Data Science Team) will answer your questions as soon as possible.

Please DO NOT send your technical enquiries to

Q14:What will be taken account into the final score?

We will take into account both the submission scores and technical novelty as the final score.

Please remember to submit the Experimental Results on Kaggle, and both the Supplementary Material and Source Code to by 23 March 2019, 11:59pm (SGT). More info can be found in the competition guide sent to all confirmed participants.

Q15:Do I submit the deliverables individually or as a team?

Please submit the deliverables as a team by 23 March 2019, 11.59pm (SGT).

Do you have any questions about the National Data Science Challenge?

Contact Us

For partnership opportunities on tech related events, please email to: