2024 Fall
DATA 101 001 - LEC 001
Formerly DATA C101
Data Engineering
Lisa Yan, Michael Ball
Class #:26121
Units: 4
Instruction Mode:
In-Person Instruction
This class is audio and/or visually recorded
Offered through
Data Science Undergraduate Studies
Current Enrollment
Total Open Seats:
0
Enrolled: 430
Waitlisted: 0
Capacity: 430
Waitlist Max: 50
No Reserved Seats
Hours & Workload
1 hours of the exchange of opinions or questions on course material per week, 3 hours of instructor presentation of course materials per week, and 8 hours of outside work hours per week.
Final Exam
TUE, DECEMBER 17TH
03:00 pm - 06:00 pm
Lewis 100
Hearst Field Annex A1
Tan 180
Social Sciences Building 126
Moffitt Library 102
Other classes by Lisa Yan
Other classes by Michael Ball
Course Catalog Description
This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.
Class Notes
*Prerequisites for this course are ENFORCED.
*Course will not allow for time conflicts nor will it accommodate final exam conflicts.
*Read our FAQ: https://www.data101.org/fa24faq
*Course will not allow for time conflicts nor will it accommodate final exam conflicts.
*Read our FAQ: https://www.data101.org/fa24faq
Rules & Requirements
Requisites
- COMPSCI 61B or INFO 206B or equivalent courses in programming with a C- or better, or Pass; AND COMPSCI C100/DATA C100/STAT C100 or COMPSCI 189 or INFO 251 or DATA 144 or equivalent upper-division course in data science with a C- or better, or Pass.
- Undergraduate Data Science Majors
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats
Textbooks & Materials
See class syllabus or https://calstudentstore.berkeley.edu/textbooks for the most current information.
Guide to Open, Free, & Affordable Course Materials