OmicsLogic | Omics Data Analysis Skills

Design & Development of a comprehensive curriculum for teaching Genomic Data Science to Students, Biologists and Clinicians

My role in developing and launching OmicsLogic was crucial. I led the development team and collaborated with academic institutions to refine the product. I also formed partnerships for its implementation in educational settings. Building a community of over 30,000 users involved creating inclusive features and fostering collaborative learning. We tracked activities, provided badges and certificates, and encouraged independent projects. I oversaw curriculum development, focusing on case-based learning and coding tutorials. Our goal was to empower learners to master data science in biology. Read more about the project below:

 As biotechnology accelerates to become the forefront of innovation and economic growth, a new generation of data-savvy innovators are needed to take the field forward. That is why we developed Omics Logic - an online community to learn biology as a data science. We designed OmicsLogic to support the whole learner in their cognition and social-emotional learning by providing a rich experience adapted to the learner background. Omics Logic goes beyond educational content and has intentionally designed features to address pressing needs of marginalized communities to the world of data science using case studies in biotechnology, medicine and life science research. We know that most learners avoid data science because it takes too long to get to an outcome. That is why we use a strengths-based approach to empower life science learners to flourish as they learn data science. 

Community

With over 20,000 participants on our portal from around the world, our student community comes from diverse educational, ethnic and interest backgrounds. This is critical in an emerging field where representation of minorities is limited. Diversity provides clear examples of how anyone can achieve skills mastery and solid understanding of key concepts needed to succeed in this filed. Our goal is to connect this community to enable collaborative learning, peer-to-peer feedback, as well as exposure to new academic and industry opportunities. 

Our growing mentor community offers extended knowledge and applicability across various fields. Students can put their learning to use immediately by participating in various events such as mentor feedback on their projects, hackathons and discussion organized around expert-led topics.  

Activity Tracking

Omics Logic provides documentable accomplishments through the activity tracking, badges, certificates and a project portfolio aligned with industry and academic needs. Expertise levels around specific skills and achievements listed under a learner’s profile helps self-assess learning and encourages advancement. 

Analysis logic

The coursework is adapted to various levels of data mastery, offering user-friendly data analysis tools that include activities for interactive visual representations of analysis logic. T-BioInfo is a user-friendly analysis platform to learn and perform data analysis with no coding required. Each analysis path results in reproducible workflows that can be adapted to various research projects and offers interactive dashboards that learners can use to understand complex data associations with biological  concepts they understand.

Coding in R and Python

Guided tutorials in data science offer competency-based learning & assessment of data wrangling, visualization, statistical analysis and machine learning. The process includes replicating code, annotation, as well as interactive assignments that have pre-defined answers. Coding challenges expand on the practice with prepared jupyter notebooks that learners are encouraged to submit for expert feedback and add to their portfolio. 

Omics Logic Projects

Omics Logic offers case-based learning with the goal of helping learners understand data analysis applications and develop independent projects for their portfolio. This approach includes curated datasets with associated publications and guides on meta-analysis. Literature  review provides an opportunity for students to evaluate sources of information and identify datasets that come from reputable labs 

Our independent project approach is an opportunity for experiential learning where instead of following tutorials, learners perform independent analysis and seek out biological interpretation of data analysis results. By focusing on a problem or research question of their choice, learners are encouraged to develop multimedia projects that express ideas through multiple media, which includes visual, audio, and digital production.