A Quick and Easy Guide to Improving Your Data Science Portfolio

RMDS Lab
RMDS Lab
Published in
2 min readJul 13, 2021

Nowadays, it’s more important than ever for data science graduates to receive hands-on training that will couple what they learn in the classroom with what they will have to implement in the workforce. GRMDS.org seeks to bridge this gap by providing young professionals with the opportunity to develop their work. Here are four ways to improve your data science skills today:

1. Upload your project information to our project portal. First, you will need to sign up for free at grmds.org. Select “Register” and then fill in your details. From there, you can fill in the required information and upload your project. Once your project is uploaded, fellow data scientists can read and review your work, and provide you with advice.

2. Impact Score: Once you register on grmds.org, you’ll have the opportunity to see the perceived impact of your projects. Our Impact Score is used to calculate your effect on your field. Our system measure project quality, engagement, project outcome, perceived participation and is available on your personal profile page on grmds.org. This will help you to recognize the strengths and weaknesses of your projects.

3. Collaborate with an expert. RMDS Lab’s Expert Consultation and Recommendation Service will allow you the choice of several different tracks. Individuals will have the choice of having their resumes reviewed, talking to experts in their field, and getting feedback on their projects from those with first-hand experience in the field.

4. Peer review: Once users have signed up to grmds.org, they can have the opportunity to chat with each other privately and exchange points to review each other’s work.

GRMDS.org is a platform that aims to provide assistance for data scientists who seek to develop their projects. Once data scientists are signed in and build their profiles and project portfolios, the system will recommend datasets, people with whom to collaborate, assistance on models, algorithms, peer review, and execution.

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