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In recent years, data science has served as an indispensable tool when taking into consideration investment decisions. With a growing awareness of corporate social and environmental accountability, potential investors can harness the power of data to enact responsible investing. With this in mind, RMDS Lab is announcing its next data science competition, Brace for Impact: Creating a Dashboard for Responsible Investing Using Machine Learning. This special competition's contestants will be challenged with collecting data sets and creating a dashboard that can help potential investors to review the social and environmental impacts of companies in which they might invest. A grant of $1,000 will be awarded to the grand prize winner, and $500 will go to the runner up.

Contestants who complete and submit their project will receive a FREE, one-month premium membership to GRMDS platform as well as a certificate of completion.

For contestants in High School:


For contestants NOT in High School:


Sample Research Questions:

  • What are the factors influencing responsible investment decisions?
  • What aspects (environmental sustainability, social justice, corporate ethics, gender, and sexual discrimination) are valued most by investors in each investment type? ​
  • What are the factors contributing to individual investment choices?
  • What type of socially responsible stock or investments would people prefer to invest in?
  • How do investment firms collect data outside the market?
  • What is the optimal responsible investment pick?
  • What is the omnichannel marketing strategy for various types of investors?
  • What is the plug & play investment technology or technology stack available to investors
  • What is the industry’s best practice for investment business analytics?

All perspectives are welcome! Show us your most valuable insights from your innovative data analytics.


RMDS 2021 Competition Timeline
2021 Competition Timeline


Dataset Overview

Participants will need to start with the dataset provided below to perform their analysis. This dataset contains about 6,000 funds from Morningstar with 30 available features. This dataset is published solely for the use of competition, we do not recommend using it for other purposes directly.

There is also a recommended page for contestants to get access to some possible related datasets and how to get started in creating dashboards. There is also a recommended page for contestants to get access to some possible related datasets and how to get started in creating dashboards. Contestants are encouraged to conduct their own research in addition to using the data provided in our competition. For the purpose of this competition, RMDS Lab is also offering contestants the opportunity to use company-level ESG data from a variety of resources, including: Sustainanalytics; Hum data; Unpri

⚠ You have to sign in to see the recommended page or download the sample dataset. The data would be available for downloading after the competition begins at January 7.



The recommended datasets include:

This dataset comprises demographic data like population, age, sex, race and income, published by the US Census Bureau.

The CO2 Emission data by sector and state, published by EIA.

This dataset contains the employment figures by major industry sector, published by Bureau of Labor Statistics.

It includes multiple tables for different demographic people movements by race and by region.

It displays the historical prices for the funds in the dataset.




RMDS Lab offers our community a variety of educational resources focusing on data science applications and techniques. You may explore the RMDS learning portal containing various data science courses at

Below are additional free resources:

If you have any questions regarding access to training materials and want to learn more about RMDS educational resources, you may use the Forum.


Submission Deliverables


  • Technical report in PDF with names of all team members and team name required
  • Datasets used in .zip folder required
  • Readme on how to run your code and requirements.txt on your development environment required (except for high-school students)
  • CSV of results (Optional)
  • A working prototype like map, web page, apps, Tableau, Excel (Required if no codes submitted)

*By submitting to the contest, contestants agree to have their submissions made publicly available.

All submissions will automatically become an analytical asset that can be used to enrich each contestant's data science portfolio at Contestants can also take part in an exchange for other analytical assets or services by using the RMDS Exchange at the platform.



Impact: What useful business insights are acquired from the proposal? How does this submitted model benefit (or cost) businesses, and what actionable steps are recommended to improve their work?​

Methodology Validity: Document the methodology, mathematics, and economic principles behind the proposal and provide the references or reasoning for your approach. How is the prediction generated and how are the factors weighted sensible? Are the assumptions and limitations of the methodology clearly outlined with suggestions to improve the proposal? Are the quantitative steps of data ingestion, feature engineering, model architecture, and performance optimization valid? How robust is your model?

Reproducibility: Does the solution use coding best practices with workflows and documentation to reproduce one’s work? Are the data ingress and egress pipelines reproducible? Is there a clear presentation of data science work in the documentation?

Usability: Is the information presented in a way that is actionable? Would a member of the general public understand the model, what it means, and what actions to take?

Ability to Deploy: Is getting access to the data realistic? How long is the computation time? How well is the scalability of the system to accept new data sources? How often does it need to be maintained? Is it    hard to maintain/update? How much manpower, time, resources are needed to be allocated to maintain the functionality?​

Fair and Ethical Use of Data: Does the solution consider biases in data? Is the data from open and trusted sources?

Innovation: Will the idea have a big impact? How innovative is the approach, selection and weighting of various factors, or how information is displayed and communicated?


Judging Committee

Dr. Grace Dai, Assessment Expert and Senior Research Associate at Kentucky Council on Postsecondary Education


Masuda Satoshi, President of Japan Information Technology Society


Dr. Alex Liu, Advisor to Harvard Data Science Review and former IBM Chief Data Scientist




Stage 1:  Registration


Participants will register on GRMDS. We will send out a confirmation email to all participants upon successful registration. Once you form your team, one representative from your team must fill out the Team Registration Form. Please note that this competition is open to all participants globally. For any questions you may ask it on the Forum.


Stage 2:  Team work and submission


Submissions must include all deliverables and are due Sunday, January 30, 11:59 PDT. Please upload all deliverables to the GRMDS. Place the names of all team members and team name on the technical report. Submission by any individual group member will represent the whole team.


Stage 3:  Evaluation and Final Presentation


Our expert committee will evaluate all project deliverables and select the finalist teams at the Awards Ceremony.



First place badge


First Place

$1,000 + Certificate

Complimentary six month premium membership at RMDS ecosystem ($240 Value)

Second Place badge


Second Place

$500 + Certificate

Complimentary six month premium membership at RMDS ecosystem ($240 Value)

High School Award Badge

High School Award

$500 + Certificate

Complimentary six month premium membership at RMDS ecosystem ($240 Value)

Rising Star Award Badge


Rising Star Award

Considerations for internship positions at RMDS Lab + Certificate

Complimentary six month premium membership at RMDS ecosystem ($240 Value)


Winners will also be considered for publishing opportunities with our partners.


Code of Conduct


The use of data will adhere to ethical use and protection of individual data privacy. Find the Code of Conduct here.


Frequently Asked Questions

How do I register?

We offer two options for registration based on age. For high school students, CLICK HERE. All other contestants can CLICK HERE. You must be signed in to view the form.

How do I form a team?

Participants are welcomed either as individuals or as teams. In the case of teams, one person must be designated as the team leader and will be solely responsible for communications with the organizers. If you are looking for teammates, advertise your interest on our Forum

How do I make submissions and what are the deliverables?

Submissions can be made here. See above section “Submission Deliverables” to see what must be included in your submission.

What is the deadline to register?

There is no deadline for our registration. But we strongly recommend that your registration is no later than October 27, since you need time to prepare your work.

How does the number of team members impact potential cash prize offerings?

The number of team members will not impact potential prize offerings. The prize offerings will remain the same.

Can teams comprise members from different cities/countries?

Yes. We welcome people from different cities or countries to join our competition. This competition is open to the global community.

How do I get in contact with the organizers?

If you have any questions, you may ask in the Forum. We’ll get back to you as soon as we can.

I already registered my team but need to update my team info. What should I do?

If you need to update your team roaster, please submit the registration form again with all the team members information.

What training material do you have to help my team get started?

Please see resources listed on this page, including recordings of competition training sessions. There is also a dataset sample, data dictionary and further reading material.