printer friendly pageDATASCI 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: 5
| UG Reqs: WAY-AQR
DATASCI 192A: Data Science Practicum I
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.
Terms: Win
| Units: 2
Instructors:
Nobles, M. (PI)
;
Zhang, J. (TA)
DATASCI 194N: Data Science for Neuroscience (DATASCI 294N)
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.
Terms: Win
| Units: 3
Instructors:
Gwilliams, L. (PI)
DATASCI 198: Practical Training
For students majoring in Data Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience. Students may enroll in Summer Quarters only and for a total of three times. Students must first notify their DS adviser before enrolling in their course section, and must submit a one-page written final report summarizing the knowledge/experience gained upon completion of the internship in order to receive credit.Please notes that F-1 international students enrolled in their department¿s CPT course cannot start working without first obtaining a CPT-endorsed I-20 from Bechtel International Center (enrolling in the CPT course alone is insufficient to meet federal immigration regulations).
Terms: Win, Spr, Sum
| Units: 1
| Repeatable
4 times
(up to 4 units total)
Instructors:
Nobles, M. (PI)
;
Sun, D. (PI)
DATASCI 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-6
| Repeatable
20 times
(up to 300 units total)
Instructors:
Duchi, J. (PI)
;
Kim, G. (PI)
;
Sabatti, C. (PI)
...
more instructors for DATASCI 199 »
Instructors:
Duchi, J. (PI)
;
Kim, G. (PI)
;
Sabatti, C. (PI)
;
Salgado, R. (PI)
;
Sun, D. (PI)
;
Walther, G. (PI)
;
Weinstein, J. (PI)
DATASCI 294N: Data Science for Neuroscience (DATASCI 194N)
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.
Terms: Win
| Units: 3
Instructors:
Gwilliams, L. (PI)
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