This is an advanced project course in data science that offers a "maker" and/or "innovation" viewpoint. The course is focused first on developing an open-ended-real world project relating to data science. Related concepts of computer science tools and theoretical concepts are covered to support the project. These concepts include filtering, prediction, classification, LTI systems, and spectral analysis. After reviewing each concept, we explore implementing it in Python using libraries for math array functions, manipulation of tables, data architectures, natural language, and ML frameworks.

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.