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1 - 6 of 6 results for: DATASCI ; Currently searching spring courses. You can expand your search to include all quarters

DATASCI 112: Principles of Data Science

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.)
Terms: Win, Spr, Sum | Units: 4 | UG Reqs: WAY-AQR

DATASCI 120: Data Narratives (MCS 120)

The class allows students to grow their ability to communicate ideas and insights with data. There are many components of a well-crafted narrative based on data---from a discussion of data sources to visualization, and from pattern detection to generalizable conclusions---which we explore in sequence across the quarter. The class does not introduce advanced data analysis techniques. It rather focuses on the essential elements of an inquiry conducted with data and places a special emphasis on how to record and communicate these. Each student enrolled in class needs to identify a dataset and a question that they are going to explore. As we examine the different components of a data inquiry, the students will carry out a corresponding analysis/writing assignment on the data they have identified, gradually building material for the narrative that will constitute their final paper. This course is required for students participating in SURP-Stats and for Data Science BS students who are fulfilling their capstone requirement with independent research (including Honors thesis) or with DATASCI 190. Prerequisite: Stats191 or equivalent. (WIM)
Terms: Spr | Units: 3
Instructors: Sabatti, C. (PI)

DATASCI 154: Solving Social Problems with Data (COMM 140X, EARTHSYS 153, ECON 163, MS&E 134, POLISCI 154, PUBLPOL 155, SOC 127)

Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use more »
Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. This course is the required gateway course for the new major in Data Science & Social Systems. Preference given to Data Science & Social Systems B.A. majors and prospective majors. Course material and presentation will be at an introductory level. Enrollment and participation in one discussion section is required. Sign up for the discussion section will occur on Canvas at the start of the quarter. Prerequisites: CS106A (required), DATASCI 112 (recommended as pre or corequisite). Limited enrollment. Please complete the interest form here: https://forms.gle/8ui9RPgzxjGxJ9k29. A permission code will be given to admitted students to register for the class.
Terms: Aut, Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

DATASCI 190: The Data Science Experience

This course is intended for juniors and seniors in the Data Science/MCS major and it offers students an opportunity to engage with the campus data science community in its various activities of research, support of data driven decision making, education and communication. By taking part in these, students revisit what they learned through their college career and mature a personal understanding of data science. The course is equivalent to one unit of academic work done throughout the year. It involves attending and actively engaging with events (e.g. round tables, alumni panels, workshops, and technical seminars), participating in data science consultations, and reflecting on the undergraduate experience through meeting with a mentor and developing a portfolio. Workshops, seminars, and data science consultations are chosen among a variety of options to reflect the students' interests. Participation is tracked through a Canvas site throughout one academic year. Students enroll for 1 unit during the Spring semester, when they are also required to attend class meetings. This course satisfies the Capstone Requirement of the Bachelor of Science in Data Science if taken after or concurrently with DATASCI120 "Data Narratives."
Terms: Spr | Units: 1
Instructors: Sabatti, C. (PI)

DATASCI 192B: Data Science Practicum II

This is the second 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. 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 second quarter focuses on modeling, and delivery of results and presentation of progress to partners. This class is only open to students who took DATASCI 192A.
Terms: Spr | Units: 2

DATASCI 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable 20 times (up to 300 units total)
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