Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ and being able to ‘make it work’ are not the same. And, in an area as important as Artificial Intelligence, if we collectively cannot actually implement and create useful systems, then we reduce our competitive advantage, economic strength, and even national/global security.
The Data-X framework is designed to bridge the gap between theory and practice, by exposing students to state-of-the-art implementation techniques and mindsets.
- Rapid implementation of 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.
- A Network of People: 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
McKinsey Dec 2017: A new survey finds that many companies are launching data-focused businesses. But few have achieved significant financial impact, which requires the right combination of strategy, culture, and organization. (link)
Testimonials / What People Say After Experiencing Data-X:
- 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.
- Great awesome and amazing tools for data analysis, ML, and processes in general!
- 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. The vast majority of teams that I’ve applied to within the past year use the tools taught in this class. When I arrived at my
data science internship this summer, I already knew how to use most of my team’s stack.
- 135 has to be my favorite of the ML classes at Berkeley. It covers A TON of content. The course is very application-focused but takes some time to explain the general idea behind concepts.
- I feel like I’m really learning how powerful data science tools can be. When we were brainstorming project ideas, I didn’t think any of the ideas were feasible. However, with each week, I’m learning how pre existing libraries and tools can be easily used and combined to create really powerful products.
- Lots of practical tools and sample code.
- I am gaining an understanding of how to use data science libraries in python
- The algorithms and toolkits I’m learning feel very valuable and tangible given the breakout sessions and direct datasets we work on.
- “I can’t begin to explain how grateful I am that I took this class and got to meet you guys! Long live Data-X!!”
Learn more about Data-X here.