This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks.

This class does not give permission codes. If you are on the waitlist, please note the number of seats in the class, the reserved seats, and your position on the waitlist to assess your chances of getting into the class. Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ vs. ‘making it work’ is not the same, especially for innovating competitive advantage, economic strength or social good, and even national/global security. Thus, Data-X is a mixture of half Data Science and Data Systems and the other half on Innovation, which includes behaviors and processes required to create new data-related applications. At its core, Data-X is an advanced project course which is highly applied. The course surveys a variety of key theoretical concepts and software tools. Data-X has a focus on helping students validate, design, and build data science, AI, and Machine Learning applications and systems (not only algorithms) for real-world user impact. The course covers 3 tracks aimed to guide students’ project work and future industry work: 1. code and theory (e.g., high- and low-code tools), 2. broader insight from instructors, industry guests, and inductive learning games, and 3. teams and projects (e.g., live team demos and feedback). To further enrich student learning experience, project teams will be assigned an external industry mentor for a few hours per month. Prerequisites: Students from all majors and countries are welcome since diversity adds value in co-building applications and systems to help society. However, the course has technical application, so students should have completed a probability or statistics course, and have the ability to write Python code (or be committed to complete foundational Python material by week 2 end): 1.Code Academy free beginner course, and 2.Data-X introductions to Pandas and Numpy. While the syllabus for next semester is subject to refinement, the Fall 2021 syllabus is public at https://datax.berkeley.edu/berkeley-course/.

Repeat Rules

Course is not repeatable for credit.