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Key Learning Outcomes
Strong fundamental understanding of the essential software engineering tools covered in the course, including but not limited to CLI and Git, Data Science tools and packages, virtual environments, open source ML libraries, containers, hardware, APIs, PostgreSQL.
Ability to contribute to technical projects upon completion of the semester, including website-building, development of machine learning models, etc.
Obtain necessary knowledge and hands-on-experience to be able to present about technical topics and be able to expand on topics through self-learning
Methods of Instruction
90 minute lectures that go over the past week’s homework assignment, current week’s content, and the upcoming homework assignment
60 minute hands-on homework assignments to reinforce learned concepts and provide practical experience with respective tools
Collaboration on homework assignments is highly encouraged
60 minute online office hours for conceptual questions and/or help with homework
Tentatively, the lectures will be in-person with the office hours being held remote. Attendance of in-person lectures will be enforced. Lectures will be recorded for later viewing.
Student Evaluation
Category
Homework
Participation
Final Project
Weight
30%
40%
30%
Description
Online submission of assignments using Google Form. Homework is graded with leniency and an emphasis on conceptual understanding rather than accuracy.
2 free homework drops allowed in the semester
Due date will be specified on each assignment
Attendance to lectures and participation in questions asked during class (graded based on effort rather than correctness in answering questions). We allow 1 unexcused absence, and we will drop your 2 lowest participation grades.
A final group project with 2-3 other students that is evaluated on the following criteria:
Clear usage of at least 2 course concepts
Structured design document consistent with codebase
Final presentation
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