- Implementation: Amazing AI and Data-related projects
- Tools: Covers the open-source software tools needed for these types of projects
- Mindset and Process: Develops the necessary mindset and behaviors to deliver innovative projects within a real-life development process
- Network: Brings together students, faculty, new ventures, and large firms so they can learn from each other in a manner that is both technically deep and yet broad
- Project Resources: Lectures, code samples, slides, references and everything needed for real-life projects and education all in one place
Who is it for?
Data-X is for anyone interested in careers, new ventures, and innovative projects in areas related to data science and information technology systems.
How is it different?
Taking a purely theoretical course is not enough. Students often take course after course in technical subject areas without being able to implement, apply, and/or make an innovative impact. Data-X places a real life innovative emerging technology project at the center of a learning experience that includes powerful tools, theory, and innovation behaviors and mindset. Data-X also builds on Innovation Engineering, a powerful framework for guiding innovation projects.
Unlike Other Courses
Bring your own ideas into an integrative project that can help students pursue new opportunities ranging from starting a new venture to interviewing for industry positions.
Learn more from these articles:
"The opportunity to dive into extensive projects with diverse teams, getting involved with industry mentors, the openness and flexibility of the Profs and GSIs makes the course a must have for everyone interested in data analytics. My two-semester long involvement with the class and the Profs was a significant contributing factor to me being a Data Scientist today."
"I think this class is so awesome because it teaches the tools and concepts that are most commonly used in workplace teams that are involved with data science and applied machine learning."
"135 has to be my favorite of the ML classes at Berkeley. It covers A TON of content. The course is very application-focused and yet explains the general idea behind concepts."