TECH 68 — Machine Learning for Business with Python
Quarter: Fall
Instructor(s): Charlie Flanagan
Date(s): Oct 3—Oct 4
Class Recording Available: No
Class Meeting Day: Saturday and Sunday
Grade Restriction: No letter grade
Class Meeting Time: 10:00 am—4:00 pm (PT)
Tuition: $560
Refund Deadline: Sep 26
Unit(s): 1
Enrollment Limit: 60
Status: Registration opens Aug 17, 8:30 am (PT)
Quarter: Fall
Day: Saturday and Sunday
Duration: 2 days
Time: 10:00 am—4:00 pm (PT)
Date(s): Oct 3—Oct 4
Unit(s): 1
Tuition: $560
Refund Deadline: Sep 26
Instructor(s): Charlie Flanagan
Grade Restriction: No letter grade
Enrollment Limit: 60
Recording Available: No
Status: Registration opens Aug 17, 8:30 am (PT)
Open-source tools and libraries have made it far easier to learn, experiment with, and apply AI technologies to real business problems. In this course, students will use Python and leading libraries—including Scikit-Learn, TensorFlow, spaCy, and Altair—to uncover insights hidden within data and build practical AI-driven solutions.
Through hands-on business cases, we will measure the causal impact of marketing campaigns, predict customer churn, and determine pricing strategies for new products. Students will learn the full workflow, from identifying a business problem and gathering data to implementing scalable, repeatable solutions in code.
Guest speakers working in data science will share how they approach these same challenges in practice. By the end of the course, students will know how to frame business problems in a data science context and apply the appropriate tools to solve them. Students may also complete an optional capstone project suitable for inclusion in a professional portfolio.
Through hands-on business cases, we will measure the causal impact of marketing campaigns, predict customer churn, and determine pricing strategies for new products. Students will learn the full workflow, from identifying a business problem and gathering data to implementing scalable, repeatable solutions in code.
Guest speakers working in data science will share how they approach these same challenges in practice. By the end of the course, students will know how to frame business problems in a data science context and apply the appropriate tools to solve them. Students may also complete an optional capstone project suitable for inclusion in a professional portfolio.
Python programming experience with Pandas, NumPy, and Matplotlib libraries is recommended but not required.