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’ and being able to ‘make it work’ are not the same. And, in areas as important as Artificial Intelligence, Data Science, and Machine Learning, if we collectively cannot actually implement and create, then we'll 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 as well as academia and industry, by exposing students to state-of-the-art implementation techniques and mindsets. This highly-applied course surveys a variety of key concepts and tools that are useful for designing and building data science, AI, and Machine Learning applications and systems. 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, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python libraries like NumPy for math array functions, Pandas for manipulation of tables, Scikit-learn for machine learning modeling, Tensorflow and Keras for deep learning, and many other topics related to NLP, Neural Networks, Recommender Systems etc. See https://datax.berkeley.edu/ and https://scet.berkeley.edu/students/courses/data-x/ for more information for more information. Students from all majors are welcome; however, due to the technical nature of this class, students must have the ability to write code in Python, and have taken a probability or statistics course.

Repeat Rules

Course is not repeatable for credit.