Explanation of Project Levels/ Pre-requisites

We encourage students to push their boundaries. Aim for a level based on your strengths, interests and the amount of pre-internship training that you are willing to do.

Starter Projects++: Understand How

  • Work on all aspects of defining and executing project.
  • Emphasis on solving concrete problems.

Next Projects++: Understand Why versus How

  • Expand upon concepts, techniques, and technology used in level one.
  • Emphasis on communicating difficult concepts and analyses in realistic settings.

Elite Projects++: Understand Why & Create New How

  • Build a scalable, dynamic product using cutting edge techniques.
  • Emphasis on synthesis of end-to-end product engineering with research principles.

Evaluation Framework

Evaluation will consist of self-rating, quizzes, challenges and group participation during the evaluation phase.

  • No familiarity - You have no prior exposure.
  • Basic knowledge - You have an understanding of basic techniques and concepts.
  • Some experience - You have the level of experience gained in a classroom. You still need help when performing this skill.
  • Practical application - You are able to successfully complete tasks on your own. Help from an expert may be required from time to time.


Evaluation of technical and soft skills will be coordinated via the applicant dashboard. Two sets of skills will be tracked based on selected project:

  • Skills that you will gain in the internship
  • Skills that will be used in the evaluation process

Guideline for prerequisites per pathway is given below. A subset of these skills will be used for evaluation.


Prerequisites - Bioinformatics

Coding/Programming Related Skills:

  1. Familiarity with R/RStudio.

  2. Familiarity with Git/GitHub.

  3. Experience in data visualization (ex. PCA, heatmaps, volcano plots, other non-standard plots, etc.)

  4. Experience with R Shiny

Biology and Bioinformatics Related Skills:

  1. Highest education in biology and genomic concepts.

  2. Experience in disease research.

  3. Familiarity with gene ontology.

  4. Familiarity with gene set enrichment analysis (GSEA).

Tool Related Skills:

  1. GEO database (Gene Expression Omnibus)

  2. STRING-db

  3. Enrichr

  4. DAVID

  5. Metascape

  6. GEPIA

Full Stack

Prerequisites - Full Stack

Frontend related Skills:

  1. Familiarity with styling using HTML, CSS, and SASS.

  2. Familiarity with JavaScript.

  3. Worked with Handlebars or any other templating engine.

Backend related skills:

  1. Familiarity with Ruby on Rails.

  2. Familiarity with the consumption of API endpoints.

Debugging skills:

  1. Familiarity with debugging using Chrome Inspect Tools or any other debugging tools.

Version Control skills:

  1. Familiarity with version control systems like Git or SVN.

Other concepts related skills:

  1. Familiarity with Discourse Forum Platform.

  2. Familiarity with MVC(Model View Controller) design pattern.

Additional questions:

  1. Worked with any of these frontend frameworks/libraries: React, Angular, Vue, Ember.js, Other

  2. Any frameworks/libraries that you would like to learn to build a full-stack application.

Machine Learning

Prerequisites - Machine Learning

Coding/Programming Related Skills:

  1. Familiarity with Python.

  2. Familiarity with Git/GitHub.

  3. Experience with Machine learning concepts (ex. PCA, Linear Regression, SVM, Ensemble Methods, etc)

Natural Language Processing (NLP) Related Skills:

  1. Experience with embeddings.

  2. Experience with transformers.

  3. Experience with models like BERT, LSTM etc

  4. Familiarity with clustering and unsupervised algorithms.

Tool Related Skills:

  1. Colab

  2. Jupyter Notebooks (Jupyter lab)

  3. Conda

  4. Sklearn

  5. HuggingFace library

  6. Pytorch/TensorFlow

  7. Flask/Streamlit