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 course counts towards the Sutardja Certificate in Technology and Entrepreneurship. Additional information: https://scet.berkeley.edu/certificate-in-entrepreneurship-and-technology/** 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, Data Science, and Machine Learning; if we collectively cannot actually implement and create, 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 as well as academia and industry, by exposing students to state-of-the-art implementation techniques and mindsets. Data-X frameworks, materials, code samples are all open source and available at the public site: https://data-x.blog/ This highly-applied course surveys a variety of key of 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. The course also features an open-ended project that the students will work on in groups of five over the semester. The project topic is up for the students to choose. To see examples of projects created by earlier student groups you can visit: https://data-x.blog/projects/

This lecture will be recorded and available to students. Synchronous participation (online at the day/time listed) is required, however, some participation exceptions may be granted under limited circumstances.

Repeat Rules

Course is not repeatable for credit.