(Same as Electrical and Computer Engineering M148.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 31 or Program in Computing 10A, and 10B, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. How to analyze data arising in real world so as to understand corresponding phenomenon. Covers topics in machine learning, data analytics, and statistical modeling classically employed for prediction. Comprehensive, hands-on overview of data science domain by blending theoretical and practical instruction. Data science lifecycle: data selection and cleaning, feature engineering, model selection, and prediction methodologies. Letter grading.

Review Summary

10.0 / 10
10.0 / 10
5-10 hrs/week
10.0 / 10


    Quarter Taken: Winter 2022 In-Person
    Grade: A+

    Prof. Mirzasoleman is a very nice and a great professor. She is always calm. I really enjoyed her lectures. She was also always available to help students and answer their questions. She is clearly an expert in this area and she enjoys explaining them to students.


Previously taught
23W 22W
Formerly offered as

Grading Information

  • No group projects

  • Attendance not required

  • 0 midterms

  • Finals week final

  • 100% recommend the textbook

Previous Grades

Grade distributions not available.


Textbook information not available.