Virtual-Internship Projects

Project Frameworks

Frameworks are created by STEM-Away Principal Mentors. The same frameworks are used in our self-paced projects. Browse the readme category in Career Exploration and Career Advancement to learn more about the frameworks.


Project Spec

Product vision, strategic plans and roadmap will be set by team leads. Guidance will be provided at all stages by our mentors. However, each team will define their own project with the help of the STEM-Away® Project Frameworks.




Bioinformatics

  • Level 1: Bioinformatics Pipeline applied to Transcriptomics - Recreate Research

  • Level 2: Bioinformatics Pipeline applied to Transcriptomics - Extend Research/ Productize

  • Level 3: Develop Bioinformatics Apps using the R Shiny Framework & AWS




Cloud Computing/ Hardware Engineering

Decision about these 2 pathways to be announced by March end.




Full Stack

  • Level 1: Frontend development (Ember.js) - work with open source code repositories

  • Level 2: Frontend (Ember.js) and backend (Ruby) development - work with open source code repositories

  • Level 3: Frontend (Ember.js) and backend (Ruby) development - contribute to open source code repositories




Machine Learning

  • Level 1: Design and deploy a NLP based classification or tagging system

  • Level 2: Drug-Target Relationship Discovery Software Using Machine Learning - Recreate research

  • Level 3: Drug-Target Relationship Discovery Software Using Machine Learning - Extend Research/ Productize

(Levels 2 & 3 are interdisciplinary projects that combine Machine Learning, Bioinformatics and Cloud Computing)




UX & UI

  • Level 1 UX team-centric projects will not be run in summer

  • Level 2: Work alongside participants from one of STEM-Away’s pathways (Full-Stack/ Bioinformatics/ Machine Learning) to build a product that incorporates user research and design methods. Includes creation of a formal UX portfolio.




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Difference between Cloud Computing and Data Science Pathways:

Cloud Computing Pathway

Students on the Cloud Computing Pathway will learn by using and investigating the data science portal. They will learn to:

  • Turn user requirements into customized data science environments and applications using the Conda scientific software manager, along with Docker to distribute containers.
  • Learn to create and manage common parallel computing applications such as Apache Spark and Dask on Kubernetes to aid data scientists in real-time data analysis and visualization of large datasets.
  • Troubleshoot and monitor Kubernetes clusters, including deployment of additional nodes and worker groups to scale to demand.
  • Collaborate with students in the Data Science Portal in order to deploy student-created data visualization applications.
  • Develop the soft skills necessary in order to work in highly collaborative and interdisciplinary fields such as FinTech, Climatology, Biotechnology, etc.

Data Science Pathways

Students on the Machine Learning and Bioinformatics Pathways will be end-users of the Data Science portal. They will learn to:

  • Work collaboratively on shared compute resources and gain experience with industry-standard compute workflows.
  • Learn to create assets such as notebooks and data visualization applications such as CellXGene.
  • Leverage parallel computing software such as Apache Spark and Dask to efficiently analyze big data.
  • Collaborate with students in the Cloud Computing Pathway to finalize compute requirements for their projects and analyses using tools such as Github for code management and Zenhub for project planning.
  • Gain the skills to communicate findings to different perspectives and levels of expertise through the creation of assets such as applications, notebooks, and static websites.
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