Teaching

DSC 80: Data Science in Practice

Fall 2021, Undergraduate, Lower Division, HDSI, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Help students familiarize with Python Data Science toolkits such as numpy, pandas, sklearn, and basics of html Course Website
  • Instructor: Justin Eldridge

DSC 30: Data Structures and Algorithms for Data Science

Fall 2021, Undergraduate, Lower Division, HDSI, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Intermediate computer science course that teaches students data structures such as stack, queue, trees, hashtables in Java. Course Website
  • Course Instructor: Marina Langlois

COGS 137: Practical Data Science in R

Fall 2021, Undergraduate, Upper Division, Dept. of CogSci, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Help students familiarize with R Data Science toolkits [Course Website]
  • Instructor: Shannon Ellis

DSC 30: Data Structures and Algorithms for Data Science

Summer Session II 2021, Undergraduate, Lower Division, HDSI, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Intermediate computer science course that teaches students data structures such as stack, queue, trees, hashtables in Java. Course Website
  • Course Instructor: Aaron Fraenkel

DSC 10: Principles of Data Science

Summer Session I 2021, Undergraduate, Lower Division, HDSI, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Basic ideas about data science - what it is, how do we apply them based on coding and statistics, why do we use data science, and ethics about data science
  • Course Instructor: Justin Eldridge

CSE 151A: Introduction to Machine Learning

Spring 2021, Undergraduate, Upper Division, JSoE, UC San Diego, 2021

  • Title: Tutor (alias for Undergraduate Teaching Assistant)
  • Upper division introductory course for machine learning, covering most of classical machine learning algorithms, such as linear regression, logistic regression, svm, decision tree, random forest, naive bayes, kmeans, bagging & boosting, etc in a mathematical and statistical setting.
  • Course Instructor: Jingbo Shang
  • Students should implement code from scratch from the statistical and mathematical knowledge behind these algorithms.
  • Fun Fact: I started tutoring the course while I was a lower-division (second-year) student in college :)