Bit University

We partner with professors and teachers to create interdisciplinary learning experiences, integrating fundamental technical skills.

Partner with us

Empowering Undergraduate Research in the Social Sciences

Bit Project partnered with Twitter to develop and host a 3 week bootcamp for undergraduates at UC Davis to use Twitter APIs to learn data science techniques used in social science research. Students used Python and Machine Learning libraries (pandas, scikit-learn, etc) to investigate various topics, including echo-chambers and collective intelligence.

Read the Case Study

Data Science in the Classroom

Bit Project's learning modules are short explorations into data science that give students studying non-STEM disciplines the opportunity to work hands-on with a data set relevant to their course of study. They learn the principles of data analysis, statistics, and computing with help from the Bit Project team. This curriculum can be extended and integrated into existing courses from any discipline or field.

Our modules are a great way for students to have a smooth and supported introduction to computing, statistics, and data science tools, which are increasingly relevant across academic disciplines. We empower students to do their own research, pose their own questions, and support their conclusions with data.

Free & Open Source

All of the content that we provide is free, easy to integrate into existing courses.

Beginner Friendly

Students with no prior experience are able to establish fundamentals in data science.

Empower Research

Our modules prepare students for quantitative research in their field of study.

Industry Collaboration

We work with leading companies to bring cutting edge tools & practices to the classroom.

Bringing Data Science to Digital Humanities

Bit Project has partnered with Professor Jamila Moore Pewu of CSUF to bring hands-on data science to her digital humanities course (HIS 403A). Students learned data science fundamentals in Python to build a foundation of quantitive research techniques. They built data models that investigated various hypotheses about the Transatlantic Slave Trade.