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A hands-on introduction to the methods of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data. The focus is on intuition and implementation, rather than theory and math. Implementation is in Python and Jupyter notebooks, using libraries such as pandas and scikit-learn. Course culminates in a final project where students apply the methods to a data science problem of their choice. Prerequisite: CS 106A or equivalent programming experience in Python. (Students with experience in another programming language should take CS 193Q to catch up on Python.)

This is the first course of a two-quarter capstone series. This is a capstone requirement for the B.A. in Data Science & Social Systems and a capstone option for the B.S. in Data Science. In addition, it satisfies the WIM requirement for either Data Science major. Students will work in teams of 3-4 to provide actionable recommendations and practical tools to partners, which may include government agencies, community organizations, companies, or research labs. Through this partnership, students will integrate material from their coursework, gain experience applying data science techniques to complex, real-world problems, and develop their ability to work in teams. The first quarter focuses on exploratory data analysis, ethical considerations, and problem formulation. Students must complete both 192A and 192B for credit. The grade for 192A is awarded after completion of 192B. Preference given to junior and senior Data Science B.A. and B.S. majors.

Students will apply their data analysis, data visualization, and data interpretation skills to address a cognitive neuroscience research question. The course will comprise introductory lectures on the most foundational discoveries and pressing mysteries in cognitive neuroscience, including pertinent examples of how data science has opened powerful new lines of discovery. Students will then identify a research question of interest, and apply their suite of data science tools to address their question with an open source neuroscience dataset. Students will be provided guidance in formulating their scientific question, and translating that question into an analysis plan. At the end of the course, students will produce a research report and a presentation on their question, analysis approach, and interpretation of results. Prerequisites: STATS 200, DATASCI 112 or STATS 202, and ENGR 108 or MATH 104 or STATS 203. Enrollment limited to senior undergraduates and graduate students. This course fulfills the capstone requirement for the Data Science BS and MCS.

This course is designed to bring students into the world where music, quantum computing, wavelets, and DNA converge through the lens of wave data analytics. This interdisciplinary course explores the profound connections between these seemingly disparate fields, all united by the fundamental principles of wave theory.In the realm of music, students will discover how sound waves create harmony and rhythm, unveiling the mathematical beauty behind musical compositions. Transitioning into the quantum domain, the course will delve into the enigmatic behavior of quantum waves, offering insights into the cutting-edge technologies of quantum computing and their potential to revolutionize data processing.The journey continues with wavelets, powerful mathematical tools that decompose complex signals into simpler components. Students will learn how wavelets are employed in diverse applications, from image compression to signal processing, and their pivotal role in modern data analytics.Finally, the course will explore the fascinating world of DNA, where genetic information is organized in wave-like patterns. By examining the wave properties of DNA sequences, students will gain a deeper understanding of genetic coding, mutation, and the intricate dance of life at the molecular level.Through a blend of theoretical foundations and practical applications we will have projects where students will analyze wave data and get a sense as to what can be done from an analytical perspective.

For undergraduates.

Students will apply their data analysis, data visualization, and data interpretation skills to address a cognitive neuroscience research question. The course will comprise introductory lectures on the most foundational discoveries and pressing mysteries in cognitive neuroscience, including pertinent examples of how data science has opened powerful new lines of discovery. Students will then identify a research question of interest, and apply their suite of data science tools to address their question with an open source neuroscience dataset. Students will be provided guidance in formulating their scientific question, and translating that question into an analysis plan. At the end of the course, students will produce a research report and a presentation on their question, analysis approach, and interpretation of results. Prerequisites: STATS 200, DATASCI 112 or STATS 202, and ENGR 108 or MATH 104 or STATS 203. Enrollment limited to senior undergraduates and graduate students. This course fulfills the capstone requirement for the Data Science BS and MCS.